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Article Contents

Clinical case definition, clinical signs in young infants, hospital-associated pneumonia, chronic pneumonia, evaluating the severity of pneumonia, standardizing the clinical assessment of pneumonia, conclusions.

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The Definition of Pneumonia, the Assessment of Severity, and Clinical Standardization in the Pneumonia Etiology Research for Child Health Study

Members of the Pneumonia Methods Working Group are listed in the Acknowledgments.

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J. Anthony G. Scott, Chizoba Wonodi, Jennifer C. Moïsi, Maria Deloria-Knoll, Andrea N. DeLuca, Ruth A. Karron, Niranjan Bhat, David R. Murdoch, Jane Crawley, Orin S. Levine, Katherine L. O’Brien, Daniel R. Feikin, the Pneumonia Methods Working Group, The Definition of Pneumonia, the Assessment of Severity, and Clinical Standardization in the Pneumonia Etiology Research for Child Health Study, Clinical Infectious Diseases , Volume 54, Issue suppl_2, April 2012, Pages S109–S116, https://doi.org/10.1093/cid/cir1065

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To develop a case definition for the Pneumonia Etiology Research for Child Health (PERCH) project, we sought a widely acceptable classification that was linked to existing pneumonia research and focused on very severe cases. We began with the World Health Organization’s classification of severe/very severe pneumonia and refined it through literature reviews and a 2-stage process of expert consultation. PERCH will study hospitalized children, aged 1–59 months, with pneumonia who present with cough or difficulty breathing and have either severe pneumonia (lower chest wall indrawing) or very severe pneumonia (central cyanosis, difficulty breastfeeding/drinking, vomiting everything, convulsions, lethargy, unconsciousness, or head nodding). It will exclude patients with recent hospitalization and children with wheeze whose indrawing resolves after bronchodilator therapy. The PERCH investigators agreed upon standard interpretations of the symptoms and signs. These will be maintained by a clinical standardization monitor who conducts repeated instruction at each site and by recurrent local training and testing.

Despite the fact that pneumonia is the most common cause of serious illness and death in young children worldwide, our ability, as clinicians, to infer an infectious pathological process in the lung from specific features of the history and examination is poor. Many common conditions of childhood, including malaria, bacterial sepsis, and severe anemia, produce a spectrum of clinical symptoms and signs that overlaps significantly with pneumonia, and differentiating between these conditions is challenging [ 1–4 ]. In adults, the definition of pneumonia relies heavily on characteristic changes on the chest radiograph. However, many children who have suggestive clinical signs of pneumonia and who respond to appropriate antibiotics do not have any abnormalities on the chest radiograph taken at the onset of the illness; furthermore, radiological facilities are not always available in developing countries. In short, there is no single definition of pneumonia in childhood that is sensitive, specific, and can be widely implemented.

This article describes the clinical features and classifications available to define pneumonia in children and reports the rationale for the definition adopted by the Pneumonia Etiology Research for Child Health (PERCH) study and the process by which that definition was developed and is being standardized across the 7 PERCH sites.

At the outset, we identified 5 criteria that would guide the development of a clinical case definition for PERCH: (1) It should be acceptable to and understandable by the majority of healthcare personnel throughout the developing world; (2) it should capture the essence of the global public health problem of pneumonia, which leads to 1.6 million deaths worldwide each year [ 5 ]; (3) it should focus on children with severe pneumonia, to target a reduction in child mortality; (4) it should permit the findings of PERCH to bridge to published studies on pneumonia prevention and management over the last 30 years [ 6 ], and integrate with analyses of the global burden of pneumonia planned by the Child Health Epidemiology Research Group (CHERG) [ 7 ]; and (5) it should be reproducible within and between observers and within and between study sites.

Hospitalized Pneumonia

A key decision that needed to be made at the outset was how broadly to target pneumonia cases throughout the healthcare system. There is a compelling reason to begin by studying children in the community; the majority of pneumonia episodes are nonsevere and are managed in the community by healthcare workers (HCWs) at primary healthcare facilities. However, if the illness worsens, the child may progress through the hierarchy of the healthcare system from primary to secondary or tertiary care. At the same time, the etiology of pneumonia may also evolve, for example, from a viral upper respiratory tract infection to a viral lower respiratory tract infection and develop into a severe illness through superinfection of the lung by opportunistic colonizing bacteria. Studying children at all grades of severity would provide valuable insights into pneumonia pathogenesis.

However, the resources required to undertake a comprehensive etiology study at primary healthcare facilities, rather than referral hospitals, would be very considerable. The procedures used to define etiology, such as sputum induction, percutaneous lung aspiration, pleural aspiration, and gastric lavage, are not practicable without the support of an inpatient facility. The focus of the study is on severe and potentially fatal pneumonia, and the most efficient way to capture such patients is via hospital admissions.

World Health Organization Clinical Case Definition of Pneumonia

During the 1980s, pediatricians and public health physicians recognized that it was necessary to define, in terms easily memorable to primary HCWs, the clinical features that justified antibiotic use in children with potential pneumonia. A series of studies was undertaken in developing countries to examine the sensitivity and specificity of clinical symptoms and signs of pneumonia: these included a history of cough or breathlessness, inability to feed, raised respiratory rate, lower chest wall indrawing, fever, and tachycardia [ 8–12 ] ( Table 1 ). In 1990, the World Health Organization (WHO) reviewed the available evidence and produced a guideline that has been the foundation of pneumonia detection in developing countries ever since ( Supplementary Figure 1 ) [ 13 , 14 ].

Studies of Childhood Pneumonia Contributing to the Formulation of the World Health Organization Clinical Case Definition

Abbreviations: CXR, chest radiograph; RR, respiratory rate; WHO, World Health Organization.

The WHO algorithm is applied to children who present with cough or difficulty breathing ( Supplementary Figure 1 ). These were introduced as “signs” [ 13 ], suggesting that they are observed by HCWs, although in practice they are elicited more commonly as part of the clinical history from the parent. Fever was considered as a screening sign, but it lacked both sensitivity and specificity for pneumonia [ 13 ]. Once captured by the entry definition, the rest of the algorithm is based around 3 management decisions: (1) Children with pneumonia are treated with antibiotics, (2) those with severe pneumonia are referred to the hospital, and (3) those with very severe pneumonia are treated with oxygen therapy.

Integrated Management of Childhood Illnesses

With time, this definition was incorporated into the Integrated Management of Childhood Illnesses (IMCI) strategy [ 15 ], which provides triage and management guidelines at the primary healthcare level, and into the WHO guidelines for the management of children in hospital [ 16 , 17 ]. It is therefore known and accepted throughout the developing world. Because it was incorporated into IMCI, the definition of very severe pneumonia was influenced by the “danger” signs of “very severe disease,” an important concept for triage regardless of the underlying syndrome, and the features “lethargy, convulsions or impaired consciousness” were added, as was “vomiting everything.” In addition, the original definition of pneumonia did not acknowledge the variation of clinical presentation with age, so head nodding, a mark of respiratory distress in young infants, was included and “unable to drink” was extended to include “unable to breastfeed.”

The primary objective of the WHO clinical case definition was to capture the majority of cases of pneumonia for rapid treatment with antibiotics and supportive therapy to reduce childhood mortality. The assumption was that most severe pneumonia was bacterial in origin and that making antibiotics available to such children would save lives. Subsequently, a meta-analysis of 9 community-based trials using the WHO clinical case definition of nonsevere pneumonia confirmed that antibiotics reduced all-cause mortality by 24% among children <5 years [ 18 ]. This pragmatic perspective has led to a set of definitions that emphasizes sensitivity over specificity to achieve substantial public health gains.

WHO Radiologically Confirmed Pneumonia

It was obvious that each level of the hierarchy of the WHO clinical case definition had a low specificity and negative predictive value: a raised respiratory rate is observed in children with anxiety, anemia, sepsis, and reactive airway disease; lower chest wall indrawing may be observed in any condition that leads to tachypnea; and the danger signs incorporated in the very severe pneumonia definition apply to the “final common pathway” of a wide variety of pathogenic processes. In clinical practice, the disadvantage of a low negative predictive value was simply overtreatment. However, when it came to assessing the impact on pneumonia of new conjugate vaccines against Haemophilus influenzae type b (Hib) and pneumococcus, the low specificity of the WHO clinical case definition would have diluted the measured impact considerably. In response to this, the WHO undertook a parallel review to produce a case definition that was specific for pneumonia caused by these 2 principal bacteria. The definition selected was based on a common interpretation of chest radiographs [ 19 ]. In a trial in The Gambia, the measured efficacy of pneumococcal conjugate vaccine against WHO radiologically confirmed pneumonia was 37%, arguing that the specificity of the case definition was very high, particularly because the efficacy against all invasive pneumococcal disease was only 50% [ 20 ]. Although high specificity is desirable in any epidemiological inquiry, the WHO radiological definition was deliberately biased to capture bacterial pneumonia and would not serve well in an investigation of pneumonia etiology in general.

Refining the WHO Clinical Case Definition for Perch

The WHO clinical case definition is highly sensitive but lacks specificity. The WHO radiological definition is specific for Hib and pneumococcus but lacks sensitivity for other etiologies. Unfortunately, there are no clinicopathological data available to develop a case definition that lies more practically between these 2 extremes. Therefore, at the outset of the PERCH project, we selected the sensitive WHO clinical case definition. This would allow us to capture the full spectrum of pneumonia etiologies. It would also allow us to project our etiologic distribution to other studies (eg, CHERG) that have estimated the burden of childhood pneumonia using the same definition. The poor specificity of the definition means that some children without an infectious etiology (eg, paraffin ingestion, congenital heart disease) would be enrolled in the study and subsequently found not to have pneumonia. However, using the WHO clinical case definition would not only provide wide comparability to other studies and wide clinical experience, it may allow us to suggest refinements to improve its specificity.

The next step was to review the details of this definition against the purpose of an etiology study. The PERCH case definition was refined through an iterative process of presentation, criticism, and response during in-person meetings and teleconferences, first with a globally representative group of pneumonia experts, the Pneumonia Methods Working Group (PMWG) [ 21 ], and later with the investigators from the 7 sites. The final resolution of the case definition is shown in Supplementary Figure 2 . The key areas of adaptation are summarized below.

Although the basic structure of the WHO clinical case definition has been fixed since 1990, there have been amendments and refinements that are reflected by subsequent WHO documents. For example, nasal flaring and grunting (in infants) are not consistently identified as part of the WHO definition of severe pneumonia [ 16 , 17 ]. Within a multicenter study, we needed a constant reference definition; in the interests of parsimony and persuaded by the argument that children with these 2 signs would almost certainly be included on the basis of lower chest wall indrawing or yet more severe signs, we did not include them in the PERCH case definition.

The WHO clinical case definition applies to children aged 2–59 months, but the PERCH study aims to investigate children from 28 days of age. Children aged <2 months with pneumonia present with a broader spectrum of clinical symptoms and signs than older children [ 22 , 23 ]. For the purposes of PERCH, we extrapolated the WHO case definition, including the requirement for cough or difficulty breathing, to children aged 29–59 days. Lethargy is difficult to define and assess in children in the second month of life so we adopted the following definition for this age group: “an infant who does not wake up on stimulation or, on waking, subsequently moves only on stimulation or does not move at all” [ 24 ].

Convulsions

The WHO classification defines children who present with cough or difficulty breathing and have convulsions as “very severe pneumonia.” The PERCH site investigators argued that this would incorporate a significant number of children who presented with simple febrile seizures but had no underlying pneumonia. A febrile illness can lead to both difficulty breathing and a simple febrile convulsion. A febrile convulsion is a single seizure in a 24-hour period lasting <15 minutes in a child with a history of fever [ 25 ]. Within the WHO clinical case definition of very severe pneumonia, we refined the interpretation of “convulsions” to encompass 2 precisely defined events: (1) a single convulsion lasting for ≥15 minutes or (2) at least 2 convulsions within a 24-hour period during the current illness.

Lower chest wall indrawing may be caused by wheeze, which is common among young children in some regions and is itself due to asthma, bronchiolitis, or occasionally bacterial pneumonia. The PERCH project is focused on pneumonia, not bronchiolitis or asthma; therefore, to optimize the proportion of children admitted to the study who have pneumonia as their underlying pathology, we introduced a filter based on the assessment strategy for wheeze recommended by WHO [ 17 ]. Children <2 years of age with lower chest wall indrawing and wheeze will be given at least 1 dose of a rapid-acting bronchodilator by inhaler, and children aged ≥2 years will be given 3 doses. Children whose lower chest wall indrawing resolves with this therapy, regardless of its effect on wheeze, will be excluded from the pool of severe pneumonia patients. In this definition, “wheeze” refers to a characteristic whistling sound on expiration that may be heard either on auscultation or on general examination of the child. Children who meet the criteria for very severe pneumonia will be included in PERCH, regardless of the presence or absence of wheeze.

The filter for wheeze is likely to remove children with reactive airways disease but will exclude only a minority of patients with bronchiolitis. The clinical presentation of bronchiolitis overlaps substantially with that of pneumonia and, because children with bronchiolitis can develop a complicating bacterial pneumonia, the treatment of bronchiolitis is similar to that of pneumonia; therefore, it is reasonable to include them within the scope of the project. They may be separated at the analysis stage on the basis of clinical characteristics (eg, wheeze, hyperinflation, and fine crackles on auscultation) and young age.

In developed countries, the concept of hospital-associated pneumonia is well established. In developing countries, although data are extremely sparse [ 26 ], there is no reason to suppose that hospital-associated infections are any less common. The pattern of pathogens causing hospital-associated pneumonia is characteristically different from that causing community-acquired pneumonia, with greater representation of gram-negative bacteria such as Klebsiella pneumoniae and Pseudomonas aeruginosa and greater prevalence of multiple antibiotic resistance [ 27 , 28 ]. The objective of the PERCH study is to provide etiologic information to guide prevention and treatment of community-acquired pneumonia, and we therefore modified the case definition to exclude children who have been admitted overnight to any hospital within the last 14 days.

A related problem is the development of pneumonia among children who have already been admitted to hospital. If this occurs 48 hours after admission, it is normally considered hospital-associated. However, some children who present at the early stages of an episode of pneumonia do not manifest all of the clinical signs necessary to diagnose the condition but develop them over the next 24–48 hours. This is a relatively infrequent occurrence and it would require considerably more resources to ascertain than a study targeted on the admission assessment. Furthermore, the project seeks to guide the admission management of community-acquired pneumonia, and these cases cannot contribute to that guidance. Therefore, we did not include them in PERCH.

Children with chronic respiratory symptoms are likely to have a different spectrum of pathological processes. However, if their disease is sufficiently severe to warrant admission and to meet the definition of severe or very severe pneumonia, then acute pneumonia may be a component of their illness. The underlying etiology of this group may be different, and such differences will be drawn out in the analysis on the basis of length of history, but there are no exclusion criteria in PERCH based on the duration of symptoms.

In a multisite study of etiology, geography is a key variable of interest. After accounting for variation in major risk factors, such as human immunodeficiency virus infection or sickle cell anemia, are there region-specific differences in etiological agents that provide additional clues about the epidemiology of the disease? One of the strongest confounders for this analysis is disease severity on admission, which may vary as a function of either hospital practice or health-seeking behavior. If one study site recruits less severe cases of pneumonia and another recruits only those in extremis, then the etiologic differences are likely to be due to the admission policy rather than to geographic location. To control for this, we aimed to define an index of clinical severity. The WHO clinical case definition provides only 2 grades—severe pneumonia and very severe pneumonia—and although these are associated with clinical outcome [ 29 , 30 ], they provide a relatively coarse classification to control for a potential confounder. We considered several approaches to provide a finer grading.

The most efficient approach is to use an existing standard such as the British Thoracic Society guidelines, which classify children as having mild or severe pneumonia but also provide criteria for admission to hospital and for transfer to the intensive care unit [ 31 ]. Most of the features used to differentiate the strata are shared with the WHO clinical case classification. A finer differentiation would require considerably more clinical and laboratory data, but the published literature does not provide guidance on the optimal utility of such data.

A second approach is to focus on a single relevant parameter of clinical physiology, the oxygen saturation of the blood. Pulse oximeters are readily available and widely used in developing countries. Hypoxemia has been extensively studied and is associated with a 2- to 5-fold increase in mortality [ 32–35 ]. However, a pulse oximeter may underestimate arterial oxygen saturation if it is incorrectly placed or if the patient has poor peripheral perfusion. In addition, whereas mortality in many cases of pneumonia is driven by poor oxygen exchange, it is not the only mechanism that can lead to death. Oxygen saturation will not accurately capture the degree of mortality risk among pneumonia cases that are threatened by, for example, septic shock, renal failure, or severe anemia.

The third approach we considered, and the one endorsed by the PMWG, was to collect all of the relevant clinical data available at each study site and use these data retrospectively to define a severity index by comparison against a gold standard such as fatal outcome. Such a modeling approach could also be extended to explore whether individual clinical features, or groups of features, are predictive of specific etiological causes, after accounting for severity. The PERCH study protocol included >50 clinical and laboratory variables, refined by PMWG review, which will be obtained on admission from every child. In addition, dynamic measures of severity, including pyrexia, respiratory rate, oxygen saturation, and oxygen requirement, will be obtained 24 hours and 48 hours after first assessment.

A clinical case definition is of little value unless its component clinical features are elicited and interpreted with consistency, both between individuals and within individuals over time. In children in developing countries, the assessment of respiratory signs shows marked variation between different pairs of clinicians working in the same pediatric service [ 36 ], and the ability of HCWs to elicit “soft” signs (eg, lethargy) consistently is limited [ 12 ]. More pertinent, the κ score for interobserver variation among investigators in Tanzania for the key sign of lower chest wall indrawing was <0.4 [ 37 ]. Therefore, we considered it vital to adopt an active approach to the standardization of clinical assessment in PERCH.

First, we recruited a senior clinician with long experience of pediatrics in a developing country to design and lead the program as a clinical standardization coordinator. She began with a set of written and visual materials, focusing on the clinical symptoms and signs in the PERCH clinical case definition and on key severity markers (eg, measurement of oxygen saturation). The interpretation of clinical signs was then debated, amended, and finally endorsed by the site investigators. The coordinator visited each site immediately before initiation of the study and used a standard set of materials (with particular emphasis on video and sound recordings) to train all staff involved in clinical assessment or clinical specimen sampling. Lectures were supplemented by clinical case scenarios and practical ward-based sessions. Every participant was tested for competency at the end of the training. Each site appointed 1 or more local clinical standardization monitor(s), who participated actively in the startup training and established a local program of monthly refresher training. The original standardized teaching materials, available to all staff on a clinical standardization Web site, accessed through the PERCH sharepoint, were used for both refresher training and the training of new clinical staff. Every 3 months the coordinator posts an evaluation on the Web site, comprising multiple choice questions and new video recordings of key clinical signs for interpretation, which permits comparison of the performance of clinical staff at each site, as well as cross-site comparisons. Results of evaluations are also used to guide the content of subsequent refresher training and to identify individuals or sites in need of increased support. Through monitoring of case record forms, the relative frequency with which clinicians at the same site diagnose severe or very severe pneumonia is used to alert the coordinator to possible discrepancies in the local clinical standardization process. The initial standards will be reinforced by a second training visit by the coordinator during the first year of the study.

PERCH uses a constellation of clinical symptoms and signs to define the syndrome of severe or very severe pneumonia in hospitalized children aged 1–59 months. Any syndromic focus inevitably simplifies the complex interaction of acute disease and underlying risk factors that influence morbidity and mortality in the developing world. Pneumonia occurs against a background of nonpulmonary risk factors or chronic lung disease; it varies in severity and duration; and the unique nature of its clinical characteristics is shaped by the epidemiology of other diseases (especially malaria) and by the patterns of health-seeking behavior, which vary markedly by region. We recognize that the PERCH case definition excludes important parts of the spectrum of childhood pneumonia, such as nonsevere pneumonia, nonhospitalized pneumonia, pneumonia in older children and neonates, and hospital-associated pneumonia. However, by anchoring the study in a widely recognizable clinical case definition, formulated and refined by WHO over 2 decades, and by focusing on an age group (1–59 months) that bears the brunt of pneumonia mortality, PERCH will yield comparable results from a wide spectrum of epidemiological settings that can be linked to the broad existing literature on childhood pneumonia and to current models of the global burden of childhood diseases.

Acknowledgments.

We acknowledge the significant contributions to the processes of case definition, severity assessment, and clinical standardization by the investigators at the PERCH sites (Henry C. Baggett, W. Abdullah Brooks, James Chipeta, Bernard Ebruke, Hubert P. Endtz, Michelle Groome, Laura L. Hammitt, Stephen R. C. Howie, Karen Kotloff, Shabir A. Madhi, Susan A. Maloney, David Moore, Juliet W. Otieno, Phil Seidenberg, Samba O. Sow, Milagritos Tapia, Somsak Thamthitiwat, Donald M. Thea, and Khaleque Zaman).

Pneumonia Methods Working Group.

Robert E. Black, Zulfiqar A. Bhutta, Harry Campbell, Thomas Cherian, Derrick W. Crook, Menno D. de Jong, Scott F. Dowell, Stephen M. Graham, Keith P. Klugman, Claudio F. Lanata, Shabir A. Madhi, Paul Martin, James P. Nataro, Franco M. Piazza, Shamim A. Qazi, and Heather J. Zar.

Disclaimer.

This paper is published with the permission of the Director of the Kenya Medical Research Institute. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention, Department of Health and Human Services, or the US government.

Financial support.

This work was supported by grant 48968 from The Bill & Melinda Gates Foundation to the International Vaccine Access Center, Department of International Health, Johns Hopkins Bloomberg School of Public Health. J. A. G. S. is supported by a clinical fellowship from The Wellcome Trust of Great Britain (081835).

Supplement sponsorship .

This article was published as part of a supplement entitled “Pneumonia Etiology Research for Child Health,” sponsored by a grant from The Bill & Melinda Gates Foundation to the PERCH Project of Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Potential conflicts of interest.

All authors: No reported conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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• Published: 10 September 2022

Unmet needs in pneumonia research: a comprehensive approach by the CAPNETZ study group

• Mathias W. Pletz 1 , 2   na1 ,
• Andreas Vestergaard Jensen 3   na1 ,
• Christina Bahrs 1 , 4 ,
• Claudia Davenport 7 ,
• Jan Rupp 2 , 5 ,
• Martin Witzenrath 2 , 6 , 12 ,
• Grit Barten-Neiner 2 , 7 ,
• Martin Kolditz 8 ,
• Sabine Dettmer 7 , 9 ,
• James D. Chalmers 10 ,
• Daiana Stolz 11 , 12 ,
• Norbert Suttorp 6 , 13 ,
• Stefano Aliberti 14 , 15 ,
• Wolfgang M. Kuebler 16 &
• Gernot Rohde 2 , 7 , 17  

Respiratory Research volume  23 , Article number:  239 ( 2022 ) Cite this article

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Introduction

Despite improvements in medical science and public health, mortality of community-acquired pneumonia (CAP) has barely changed throughout the last 15 years. The current SARS-CoV-2 pandemic has once again highlighted the central importance of acute respiratory infections to human health. The “network of excellence on Community Acquired Pneumonia” (CAPNETZ) hosts the most comprehensive CAP database worldwide including more than 12,000 patients. CAPNETZ connects physicians, microbiologists, virologists, epidemiologists, and computer scientists throughout Europe. Our aim was to summarize the current situation in CAP research and identify the most pressing unmet needs in CAP research.

To identify areas of future CAP research, CAPNETZ followed a multiple-step procedure. First, research members of CAPNETZ were individually asked to identify unmet needs. Second, the top 100 experts in the field of CAP research were asked for their insights about the unmet needs in CAP (Delphi approach). Third, internal and external experts discussed unmet needs in CAP at a scientific retreat.

Eleven topics for future CAP research were identified: detection of causative pathogens, next generation sequencing for antimicrobial treatment guidance, imaging diagnostics, biomarkers, risk stratification, antiviral and antibiotic treatment, adjunctive therapy, vaccines and prevention, systemic and local immune response, comorbidities, and long-term cardio-vascular complications.

Pneumonia is a complex disease where the interplay between pathogens, immune system and comorbidities not only impose an immediate risk of mortality but also affect the patients’ risk of developing comorbidities as well as mortality for up to a decade after pneumonia has resolved. Our review of unmet needs in CAP research has shown that there are still major shortcomings in our knowledge of CAP.

In 1918, Sir William Osler observed that pneumonia had replaced tuberculosis as the leading cause of death in Europe and described pneumonia as the ‘‘Captain of the men of death’’ [ 1 ].

Even one century later, pneumonia remains a major health concern and lower respiratory tract infections continue to be the leading infectious cause of death with more than 2.3 million deaths and more than 91 million years of life lost in 2016 [ 2 ]. With the emergence of the SARS-CoV-2 pandemic the central importance of acute respiratory infections to human health has once again been highlighted.

Pneumonia is defined as an acute infection of the pulmonary parenchyma and it is commonly classified by the “pneumonia triad” into community-acquired pneumonia (CAP) as the most common form of acquisition, hospital-acquired pneumonia (HAP) including ventilator-associated pneumonia (VAP) and pneumonia in the immunocompromised host [ 3 , 4 ]. The clinical presentation and severity of pneumonia is diverse and usually categorized as mild, moderate or severe in major guidelines [ 5 , 6 , 7 ].

The annual incidence of CAP requiring hospitalization ranges from 1.1 to 8.9 per 1000 inhabitants [ 8 , 9 , 10 , 11 ] and the 30-day readmission rate ranges from 12 to 25% [ 12 , 13 , 14 , 15 ]. In patients with CAP who do not need hospital admission the mortality rate is ≤ 1% [ 16 , 17 , 18 ] while in hospitalized patients CAP the short-term mortality ranges from 8 to 14% [ 8 , 10 , 11 , 19 , 20 , 21 , 22 , 23 ]. Further, several studies have shown that long-term mortality is excessive after CAP [ 24 , 25 , 26 , 27 , 28 , 29 ]. This underlines that CAP imposes a significant impact on the healthcare system and because of the aging population and the associated increase in comorbidities predisposing for CAP, it is likely that CAP will continue to be a major health issue in the years to come.

In 1999, the German Ministry of Education and Research published a call to initiate an excellence competition for competence centres in infectious diseases. Through this call the “network of excellence on Community Acquired Pneumonia” (CAPNETZ) was founded in 2001 [ 30 ]. CAPNETZ connects physicians, microbiologists, virologists, epidemiologists and computer scientists within clinical centres in Germany, Switzerland, Austria, The Netherlands, Denmark and Italy [ 31 ]. Today CAPNETZ offers the most comprehensive CAP database worldwide with an associated biobank for samples and pathogens including more than 12,000 patients [ 31 ]. Currently, the CAPNETZ database contains information on adult patients and consequently the pneumonia research within CAPNETZ has been focusing on adults. Pneumonia in children is also a major issue but an entity of its mown due to different pathogens, less burden of comorbidities etc. Recognizing the significance of pneumonia in children CAPNETZ has expanded their collaboration and begun inclusion of children into the pedCAPNETZ cohort [ 32 ]. The pedCAPNETZ cohort is still under construction and therefore the focus of this study is on adults only.

To discuss and identify which future research areas should have the highest priority in adult CAP research, CAPNETZ invited both internal and external CAP experts to participate in a 2-day scientific retreat. This paper summarizes the main research topics discussed, and highlights areas of future research, which deserve high priority to improve the understanding of CAP in order to develop novel prophylactic and therapeutic measures and ultimately reduce the burden of CAP.

The identification of unmet needs in CAP research followed a four-step procedure as depicted in Fig.  1 . In brief, research members of CAPNETZ were asked to identify important issues to be addressed within basic, clinical and translational research. Then the top 100 experts in the field of CAP research were asked for their insights about the unmet needs in CAP research (Delphi approach). The top 100 experts were determined by CAPNETZ as the researchers with the most publications in the web of knowledge database over the past 5 years marked with “community-acquired pneumonia”. Only original publications, reviews and editorials were taken into consideration. Thirty-three of the 100 experts contributed with their views on the unmet needs in CAP research. Finally, the identified unmet needs in CAP research were discussed at a 2-day scientific retreat.

Flowchart for the identification of the unmet needs in community-acquired pneumonia research

Since the SARS-CoV-2 pandemic emerged after the scientific retreat was held no unmet needs where identified prior to the meeting. However, due to the significant impact of the SARS-CoV-2 pandemic, sections on the Covid-19 disease have been added post hoc.

Identified topics covering unmet needs in CAP research

Detection of causative pathogens, current setting.

Defining the causative pathogen in patients with pneumonia is relevant for ensuring the best-suited antimicrobial treatment. However, microbial diagnosis of pneumonia is still based on culture in most hospitals worldwide and a causative pathogen is only detected in the minority of patients. In a recent CAPNETZ study, bacteraemia rates in CAP were 0.5% in outpatients, 7.8% in hospitalized patients, 12.4% in patients admitted to an intensive care unit (ICU), and a relevant proportion of patients with bacteraemia and CAP (34.6%) was afebrile [ 33 ]. With the rapid emergence of novel diagnostic techniques (e.g. isothermic multiplex-PCRs for point of care testing, microbiome analysis, host transcriptomics and metagenomics), the question arises if these techniques will provide an added value to the currently available and established diagnostic methods and whether they will change management.

Unmet needs

Define an improved gold standard for microbial diagnosis of community-acquired pneumonia: Currently, microscopy and culture of lower respiratory tract specimens as well as blood cultures, detection of urine antigen detection and serology are routinely used to complement thorax imaging results in diagnosing pneumonia. However, most tests (particularly serology) lack sensitivity and specificity, while tests with a high sensitivity, such as PCR for non-influenza respiratory viruses may currently not properly translate into clinical relevance [ 34 , 35 ]. No internationally accepted standard for microbiological tests in CAP has been defined, both in terms of including novel PCR-based techniques but also regarding the quality of the required respiratory samples [ 36 ]. There is a need for a consented diagnostic standard that also serves as comparison for new diagnostic techniques [ 37 ].

Next generation sequencing for guidance of antimicrobial treatment

With the emergence of resistant or multidrug resistant (MDR) pathogens and increasing knowledge about potentially harmful or protective microbiomes [ 38 ], antibiotic management is becoming more complex [ 39 ]. Antibiotic stewardship is, therefore, a key priority [ 40 ] fostering pathogen directed treatment and avoiding excessive empiric broad spectrum antimicrobial therapy that increases the risk of complications, antibiotic resistance and mortality.

Metagenomics offers a potential solution. Most studies utilizing “microbiome” or metagenomics techniques to date have used target gene approaches such as sequencing amplicons of the 16s rRNA subunit [ 41 ]. As a more rapid technique, nanopore sequencing allows a much more in-depth data acquisition and analysis of viral and bacterial pathogens in real time [ 42 ]. The INHALE study from the UK showed that nanopore sequencing could provide results within 6 h of sampling from sputum with 96.6% sensitivity compared to culture [ 42 ]. This method also presented data on antibiotic resistance genes. Currently, methods to comprehensively describe bacterial, fungal and viral pathogens as well as the underlying microbiome in the same sample are still limited but technology is advancing rapidly.

Metagenomic research has, to date, been conducted in relatively small studies in limited geographical locations [ 42 ]. Much larger studies are required to fully understand the limitations and added value of metagenomic approaches.

Studies show that molecular techniques out-perform culture in identification of bacterial pathogens [ 38 , 42 ], but studies to demonstrate that implementation of these techniques improves clinical practice are lacking. There are remarkably few studies in which antibiotic choice, antibiotic duration or antibiotic spectrum are reduced or improved by the implementation of such techniques. Randomized controlled trials should be conducted to demonstrate the value of new techniques to improve CAP management.

Clinicians are not bioinformatics-scientists. Therefore, automated informatics tools that can rapidly analyse sequencing data and provide results to clinicians in a usable format are required. Antibiotic resistance genes may be detected by sequencing but their relationship to phenotypic resistance in bacteria is not always known, particularly in Gram-negatives.

Imaging diagnostics

The primary role of imaging in CAP is to confirm the clinical diagnosis. The major diagnostic challenges are the correct identification of opacities and consolidations as being evoked by infectious versus non-infectious differential diagnoses. Different methods are available with chest radiography being the most abundantly used [ 43 ]. However, its low sensitivity and specificity leads to a high false negative and false positive rate, respectively, depending on the infiltrate’s position [ 44 , 45 ]. Computer tomography (CT), therefore, is a useful addition to conventional radiography because of three-dimensional imaging with enhanced spatial resolution [ 44 , 45 , 46 , 47 , 48 ]. Due to higher radiation exposure and costs, however, CT is currently only used in selected cases [ 43 , 49 ]. Further, lung ultrasonography may be an adjuvant resource to accurately diagnose CAP [ 50 ].

Pathogen prediction through imaging analysis: Although radiographic findings do currently not allow the diagnosis of a specific pathogen, a differential diagnosis may be possible using radiological pattern recognition. Deep learning approaches may help to identify patterns that are indicative of specific viral or bacterial CAP. Integrating clinical data available at time of diagnosis may further improve such an approach.

Pre-test probability for general practitioners for chest radiography: Although chest radiographs are routinely used, the efficacy as a diagnostic tool has not been determined. The reduction of unnecessary chest radiographs particularly in the outpatient setting would decrease healthcare costs, and radiation exposure. However, even in the “Prospective Cough Complication Cohort Study (3C)”, where patients with acute cough received chest radiographs, the positive predictive value of at least one of four clinical signs of CAP (temperature > 37.8 °C, crackles on auscultation, oxygen saturation < 95%, heart rate > 100/min) for a radiography-confirmed pneumonia was low at around 20% [ 51 ]. In another European multicentre study including more than 3000 patients with acute cough, the addition of C-reactive protein > 30 mg/l to clinical signs of pneumonia (absence of runny nose and presence of breathlessness, crackles and diminished breath sounds on auscultation, tachycardia, and fever) improved the diagnostic classification [ 52 ]. However, further enhancing the pre-test probability for CAP would be necessary to provide a more reliable guide to general practitioners to identify patients with respiratory tract infections other than CAP where radiographs are not necessary.

Biomarkers may be an effective way to identify patients with severe infection and to monitor and eventually predict the course of infection. In CAP, biomarkers may be indicators of inflammation (local or systemic), may be released specifically after lung injury due to infection or originate from the genome of either the host or the pathogens. Numerous biomarkers have been investigated including pro-atrial natriuretic peptide [ 53 ], pro-vasopressin [ 53 ], cortisol [ 54 ], glucose [ 55 ], glycaemic gap [ 56 ], neutrophil extracellular traps [ 57 ], proadrenomedullin [ 58 ], Angiopoietin [ 59 ] etc. [ 60 , 61 ] but C-reactive protein and procalcitonin remain the most widely used biomarkers [ 60 ] even though they both have well known shortcomings [ 60 ]. Additionally, cardiac troponins, biomarkers for cardiac injury, may identify patients with cardiac complications of CAP that need special attention during follow-up [ 62 , 63 ]. Since coronary artery calcium (CAC), a marker of coronary atherosclerotic burden, is higher in patients already at increased atherosclerotic cardiovascular risk after pneumonia as compared to similar patients without a pneumonia event, assessment of CAC by CT may also be of predictive value [ 64 ].

As the research on immune-modulatory therapies intensifies, so will the search for the appropriate biomarkers for severity and treatment response [ 65 ]. However, the heterogeneity among patients with CAP in terms of onset of symptoms, clinical presentation, severity of disease, causative pathogen(s), comorbidities, genetic disposition etc. is vast. The discovery of a single common biomarker for CAP is therefore unlikely. Instead, combining the different sources of “biomarkers” into a systems-medicine approach reflects the complexity of CAP and could provide new insights, accurate clinical diagnosis, prediction of the severity of disease and help targeting specific adjuvant treatments. The recent advances in the field of metabolomics, genomics, epigenomics, transcriptomics, proteomics and microbiomics offer respective opportunities [ 60 , 66 ].

Risk stratification

Identifying low risk-patients that can be treated in an outpatient setting enhances patient satisfaction and reduces costs. CRB-65 is frequently used as a first step but should be supplemented by oxygenation status, assessment of instable comorbidities and functional parameters [ 67 ]. Identifying high-risk patients allows early application of intensified management, prevents organ failure and, thereby, improves prognosis. ATS/IDSA minor criteria supplemented by lactate are recommended tools [ 68 , 69 ]. Recommendations for early high-risk stratification are available but have not yet been fully implemented. Although there is a five percent risk of death within the first post-discharge weeks [ 70 ], there is only limited data available on risk stratification to predict post-discharge complications.

Harmonization with sepsis risk stratification and implementation of prediction tools: Sepsis is a major complication in CAP. Current sepsis recommendations employ a risk stratification strategy with similarities to CAP risk stratification by using a simple screening score (quickSOFA, qSOFA) and a more specific high-risk prediction score (SOFA) [ 71 ]. However, the scores and respective breakpoints (e.g. respiratory rate in qSOFA vs. CRB-65) are different from those recommended for CAP [ 72 ]. Moreover, the qSOFA with a cut off of ≥ 2 misses more than half of CAP patients whose condition will deteriorate during the course of the disease [ 73 ]. For optimal implementation of risk stratification concepts in busy emergency departments a harmonization of both concepts is needed, and prospective (cluster-randomized) clinical studies to implement recommendations with clinical outcome endpoints are necessary.

Individualization of management-based risk stratification: Today, medicine is heading towards the broad application of state-of-the-art medical technologies including “omics” approaches, which might allow more tailored treatment approaches based on a better understanding of a patient’s individual state of health [ 74 ]. In addition, machine learning could provide algorithms that predict the patient’s individual risk to deteriorate and will allow monitoring treatment responses based on routinely measured markers [ 75 ].

Risk factors for and management strategies against early post-discharge complications: Studies show high re-admission or even early death rate after discharge from hospital because of CAP [ 12 , 76 ]. So far, only little information is available on risk factors for post-discharge events, including infection- and non-infection-related complications, re-hospitalization or death. There is the need to determine the different affected patient groups and to define target groups that require interventions.

Stratification strategy for the coverage of multidrug resistant pathogens: In Europe, MDR pathogens are rare in CAP, but still can complicate the choice of empiric antibiotic therapies and may result in poor outcomes [ 77 ]. However, optimal individual MDR risk prediction is not known as existing scores lack accuracy and external validation.

Antiviral and antibiotic treatment

The early initiation of antibiotic therapy has been shown to provide a survival benefit [ 78 ]. Treatment is typically started empirically, prior to the identification of the causative pathogen. Bacteria are considered to be the primary causative pathogens in CAP and antibiotics are the mainstay of CAP treatment.

Treatment guidelines differ for outpatients and only little information is available on CAP-outpatients. In the hospital, either antibiotic monotherapy or combination therapy is applied but up to now no significant differences in outcome apart from adverse effects have been observed [ 79 ]. Most antibiotics are administered systemically, either through intravenous or oral application. The role of aerosolized antibiotics has so far been only investigated for hospital-acquired (HAP) and ventilator-associated pneumonia, but not for CAP [ 80 ].

Viral pathogens are increasingly recognized as a cause of pneumonia, especially among immunocompromised patients. Although more than 20 viruses have been linked with CAP, antiviral drugs were only available for the treatment of influenza or respiratory syncytial virus (RSV) pneumonia [ 81 ] before COVID-19. Since the occurrence of SARS-CoV-2 existing antiviral drugs such as Remdesivir and lopinavir–ritonavir have been evaluated for the treatment of Covid-19 [ 82 ]. However, lopinavir–ritonavir has not shown any beneficial effect in treating Covid-19 and the effect of Remdesivir in treating Covid-19 is still questionable [ 82 ]. The specifically developed antiviral Paxlovid may hold promise but peer-reviewed data are currently lacking [ 83 ].

Secondary bacterial pneumonia is frequently observed in viral pneumonia [ 84 , 85 ] and influenza patients often receive preventive antibiotic treatment [ 86 ]. However, the pathophysiologic mechanisms promoting a co-infection with bacterial and viral pathogens are not fully understood. Notably, the frequency of bacterial co-infections in Covid-19 patients appears to be low [ 87 ].

Monitoring of ß-lactam antibiotic and macrolide antibiotic treatment and development of resistance: ß-lactams have been the “antibiotic backbone” of antimicrobial therapy of pneumonia for decades [ 88 ]. However, increasing resistance rates are beginning to limit the utility of this antibiotic class. Drug-resistant Gram-negative pathogens associated with pneumonia have been identified with increasing frequency and a variety of resistance patterns. Among the “atypical” agents there is also several reports of increasing macrolide resistance in M. pneumoniae infections throughout the world [ 89 , 90 , 91 ]. Antibiotic resistances show a broad variety between regions. Treatment guidelines, therefore, need to be updated and validated based on local epidemiological data [ 92 ].

Development of new antibiotics vs. improved usage of available antibiotics: Although the number of newly approved antibiotics has tripled in the past 6 years after a 90% decrease between 1983 and 2012, MDR bacteria pose an increasing threat [ 93 ]. Mis- and overuse of empirically prescribed broad-spectrum antibiotics have led to a significant increase in resistance although a great diversity has been observed in different countries [ 94 ]. The timely differentiation between bacterial and viral pneumonia may support the physician in avoiding medically not indicated antibiotic administration. In CAP, development of new antibiotics and improving diagnostic-guided therapy is warranted.

Overview of the treatment of CAP in an outpatient setting: A high proportion of CAP patients is treated in an outpatient setting [ 52 , 70 ]. A comprehensive overview of existing real-life principles that guide treatment in an outpatient setting will allow the formulation of improved guidelines taking the medical environment and available methods into account.

Adjunctive therapy

An excessive inflammatory response seems to be partly responsible for treatment failure in some patients and has been associated with poor clinical outcomes [ 95 ]. Different immunomodulatory and barrier-enhancing agents have been discussed and tested for potential adjunctive therapy to antimicrobial agents in the treatment of CAP. Classical approaches have focused on corticosteroids and immunoglobulins [ 95 ], while the effects of adrenomedullin and angiopoietin-1 have been investigated in more experimental approaches [ 59 , 96 ]. Especially corticosteroids have been the subject of potential adjuncts to conventional CAP treatment [ 95 ]. They are the most used anti-inflammatory drugs and modulate a wide range of physiological processes, but their efficacy has been discussed controversially in CAP treatment [ 97 ]. In hospitalized patients with Covid-19 and who requires oxygen, corticosteroid treatment improves survival [ 98 ] and is therefore recommended [ 82 ].

Further, due to the hyperinflammation state seen in some patients with Covid-19 several immunomodulatory agents, with the intend to block the inflammatory pathway, have been evaluated [ 82 , 99 ]. Fare from all has proven effective but in hospitalized patients with Covid-19 and in need of oxygen treatment, IL-6 inhibitors may reduce the risk of mechanical ventilation or death [ 82 ]. Likewise treatment with Janus kinase (JAK) inhibitors may reduce the risk of respiratory failure and death [ 100 , 101 ]. Inhibition of the IL-1 pathway may also be associated with reduced mortality in patients with Covid-19 [ 102 ] although conflicting results exists [ 103 , 104 ].

As the understanding of the underlying pathophysiology improves, specifically tailored agents for immunomodulatory therapy will likely help to avoid adverse outcomes for specific patient groups.

Identification of patients that benefit most from macrolides: In addition to antimicrobial effects, macrolides also have an immunomodulatory effect [ 105 , 106 ]. Several studies have suggested a benefit of adding macrolides to a β-lactam in the empirical treatment, although the existing literature is conflicting [ 105 , 107 , 108 , 109 ]. It is largely unclear, which patient groups benefit most from an adjunctive macrolide therapy since the effects of macrolides appear to be influenced by the presence of bacteria [ 106 , 110 ] and by the susceptibility of the host to develop cardiac side effects associated with macrolide treatment [ 111 ].

A recent CAPNETZ study has used a machine learning approach to identify patients who benefit most from macrolide treatment [ 112 ]. Such a personalized approach is important for improved management and disease outcome. However, results need to be confirmed in a randomized controlled trial.

Immunoglobulins: Immunoglobulins have been proposed as a promising adjunctive therapy option for severe sepsis, but have only been investigated in small studies [ 113 ]. Both, IgG and IgM levels have been shown to be higher during convalescence in pneumonia [ 114 ]. Additionally, patients with severe CAP admitted to ICU showed lower levels of immunoglobulins than non-ICU patients [ 115 ]. Since therapeutic formulations of immunoglobulins are available further insights into the changes of serum levels of immunoglobulins and IgG subclasses during the course of the disease are of scientific and therapeutic interest.

Pathogen-directed strategies: In addition to antibiotic treatment, a pathogen-directed strategy may include blockers of pathogenicity, virulence, or toxins. These may be small molecule inhibitors or monoclonal antibodies [ 116 ]. Additional use of adoptive cell therapy might be possible as well as the use of phages or phage products. In the ongoing pandemic of Covid-19 treatment with monoclonal antibodies against the SARS-CoV-2 spike protein may reduce the risk of Covid-19-related hospitalization and death in ambulatory patients [ 117 ] and in hospitalized patients with Covid-19 and who are seronegative, treatment with monoclonal antibodies may reduce mortality [ 118 ].

Host-directed strategies: Alternative strategies comprise of specifically stimulating early local immune responses against pathogens, dampening particular components of the local and systemic immune responses to avoid tissue injury, increasing tissue resilience and improving resolution of inflammation and tissue repair.

Vaccines and prevention

Vaccines against pneumococci and influenza virus, the most frequent bacterial and viral causes of CAP, are available. Influenza vaccination has been shown to reduce the number of severe CAP cases and improved overall long-term survival in patients with CAP during influenza seasons [ 119 ]. The known mechanistic link between cardiovascular events and pneumonia may be the cause for the reported cardioprotective effect of vaccines against influenza and pneumococci [ 120 , 121 , 122 ].

Recently, a quadrivalent influenza vaccine that includes both influenza B lines, i.e. Yamagata and Victoria, has been made available for clinical use [ 123 ]. It has already been recommended as the primary influenza vaccine instead of the trivalent influenza vaccines in several countries. The investigational universal influenza vaccine candidate, FLU-v, has entered phase 3 clinical trials [ 124 ] after demonstrating immunogenicity and safety in a recent phase 2b study [ 125 ].

Two types of pneumococcal vaccines are used: pneumococcal conjugate vaccines (PCVs) and pneumococcal polysaccharide vaccines (PPVs). Although PPV has been shown to prevent pneumococcal bacteraemia, its protective effect against non-invasive pneumococcal pneumonia and its effectiveness in the immune-compromised host are limited [ 126 , 127 , 128 ]. Infant vaccination programs with PCV have substantially decreased the contained serotypes by herd protection effects [ 129 ]. However, the decrease of vaccine serotypes was compensated by non-vaccine serotypes (replacement effect), that comprised sometimes even the same pneumococcal clone, which has switched its capsule to another serotype to evade the selective pressure of the vaccine [ 130 ]. Furthermore, strong herd protection effects in invasive and non-invasive pneumococcal CAP seen after the global implementation of the 7-valent conjugate vaccine (PCV7) [ 129 , 131 ] were not reproduced after substitution of PCV7 by the 13-valent conjugate vaccine (PCV13) [ 132 , 133 ]. Particularly, serotype 3, which was included into PCV13, seems not to be affected by herd protection effects. Serotype 3 has been associated with disease severity, i.e. higher rate of patients with hospital admission and oxygen support, and has become one of the leading serotypes in adult CAP [ 133 , 134 ]. The decreased efficacy against serotype 3 as well as the lack of herd protection of this major serotype is not completely understood. One plausible hypothesis includes capsular shedding after binding of antibodies [ 135 ].

Optimal use for pneumococcal polysaccharide vaccines and pneumococcal conjugate vaccines: Although the two vaccines PPV23 and PCV13 share some serotypes they generate different immune responses [ 136 ]. While both vaccines generate antibodies against pneumococcal capsular antigens, only PCV13 induces a T-cell-dependent response. The broad landscape of different pneumococcal vaccination recommendation worldwide (i.e. PPV or PCV or “sequential vaccination” with PCV followed by PPV) reflects uncertainty on the optimal use of these vaccines. Furthermore, simultaneous application of both pneumococcal vaccines (PCV and PPV) is currently investigated for the first time in an ongoing randomized controlled trial to improve immunological response (pneumococcal serotype specific B-cells and humoral immune response) in the elderly [ 137 ]. More research is needed on the topic to provide vaccination recommendations especially for patients with certain underlying medical conditions such as respiratory, hepatic or renal comorbidities and immunosuppression as well as the optimal time for re-vaccination [ 138 ].

Novel vaccines: Like a universal influenza vaccine, a serotype independent pneumococcal vaccine would decrease the global CAP burden tremendously. Currently, a 15-valent PCV was announced in the US and a 20-valent PCV is close to market license [ 139 ]. However, experience with PCV7 and PCV13 have shown that pneumococcal evolution is highly dynamic and that non-vaccine serotypes will emerge and fill the niche created by PCV-induced reduction of vaccine serotypes. Since the inclusion of even more serotypes into a novel vaccine is limited, a serotype independent vaccine targeting e.g. surface proteins would represent a major breakthrough [ 140 ]. Also, a more effective—even singular—vaccine against serotype 3 would decrease the burden of CAP tremendously.

The progress in microbiological diagnostics—and also the recent SARS-CoV-2 pandemics- has uncovered the burden of non-influenza respiratory viruses in CAP. Particularly, RSV seems to expose a substantial burden on adult CAP. Therefore, a RSV vaccine is highly desirable but remains a challenge [ 141 ], since earlier RSV vaccines had no efficacy and even seemed to aggravate disease [ 142 ].

Systemic and local immune response

In order to facilitate a rapid and efficient immune response against invading microbial pathogens, the lung combines different defence strategies including anatomic, mechanical, humoral, and cellular mechanisms aiming towards the rapid expulsion of pathogens [ 143 ]. The lung is a fragile organ that is finely designed for gas exchange, so that an excessive immune response may itself be damaging and lead to irreparable tissue damage that might be lethal [ 144 , 145 ]. Many anti-inflammatory strategies have failed to improve survival in pneumonia [ 146 ]. It seems that more specific anti-inflammatory strategies, subphenotyping of patients, and precise timing are crucial to achieve beneficial modulation of the inflammatory response. Differences in immune responses may result from genetic predispositions that influence immunomodulation [ 147 , 148 ]. Deciphering the mechanisms of inflammatory response in respiratory infection would allow the identification of different inflammatory phenotypes.

Determination of different inflammatory phenotypes for personalized medicine: Despite sharing the same underlying pathogen, in some patients CAP manifests as a serious disease while in others the course of disease is mild. The susceptibility to infection as well as CAP severity is most likely a phenotypical trait determined by uncountable pathogen- and host-specific factors, including polymorphisms in many collaborating genes in otherwise healthy persons [ 149 ], immunosenescence, pregnancy, lung diseases, immunodeficiency, and specific therapies for preceding diseases or the CAP itself, to name a few. Therefore, specific conditions need specific strategies for promising personalized adjunctive immunomodulatory therapy.

Pulmonary long-term consequences of CAP: While most patients return to normal lung function following pneumonia within weeks to months, some fail to recover ad integrum due to pleura-involvement or parenchymal alteration, and some may be at an increased risk of developing chronic non-infectious lung inflammation including cryptogenic organizing pneumonia (COP) and idiopathic pulmonary fibrosis (IPF) after an episode of pneumonia. The early identification of these patients would allow taking timely countermeasures. For this, specific markers (of clinical course, lung function, imaging, biomarkers, etc.) and therapeutic strategies need to be identified. Therefore, a deeper patho-mechanistic understanding of the pulmonary long-term sequelae of CAP is needed.

Extra-pulmonary long-term consequences of CAP (see also “ Long-term cardio-vascular complications ”): Severe pulmonary and systemic inflammation upon lung infection may result in long-term sequelae regarding organ dysfunction, vascular pathology, and neuromuscular function. Some patho-mechanistic links have been proposed, e.g. direct cardiac damage by bacterial invasion into the myocardium and formation of microscopic lesions finally leading to cardiac scarring [ 150 ] and a causal relationship between pulmonary inflammation and atherosclerotic plaque formation in systemic arteries [ 151 ]. However, many key patho-mechanisms by which pneumonia may trigger or promote subsequent organ dysfunction remain unclear. The complexity of the interplay between pulmonary inflammation and distant organ pathology and the relatively long timeframes render preclinical as well as clinical investigations in this field challenging. Nevertheless, the emerging evidence for the relevance of long-term sequelae for patient outcome and the probability for potentially effective secondary prophylactic measures warrant intense joint scientific efforts.

Comorbidities

Pre-existing comorbidities including chronic respiratory, cardiovascular diseases and diabetes mellitus are frequent in the elderly population and increase the risk of CAP as well as mortality [ 152 , 153 ]. Immunosenescence and therapies with immunosuppressive agents increase the number of immunosuppressed patients. Immunosuppression has been recognized as an independent risk factor for CAP [ 9 ]. Although the prevalence of different comorbidities in CAP patients has been evaluated across several studies, data on their impact on the course of the disease as well as their management during CAP are limited. Furthermore, most of the international guidelines on CAP management clearly state that the proposed recommendations do not apply to patients with immunosuppression [ 5 ]. International and observational studies on immunocompromised patients are limited or consider only a single specific risk factor. A direct implication of this scenario is the possible underestimation of the real prevalence of immunosuppression with a higher rate of treatment failure or an overestimation and overuse of wide-spectrum antibiotics.

Treatment guidelines for immunocompromised patients: Up to 29% of hospitalized patients with CAP have some level of immunosuppression [ 154 ] and it is foreseeable that this population will expand in the following years. These patients may be at risk of both the “core” CAP pathogens and opportunistic microorganisms. Unfortunately, such patients are often excluded from pneumonia studies resulting in a marked knowledge gap concerning causative pathogens, performance of existing prognostic risk scores and performance of advanced diagnostic such as metagenomics among others. Immunocompromised patients do not form a homogeneous group in terms of underlying disease, treatment and severity of immunosuppression and the different immunosuppression states in the context of CAP need to be defined. It is possible that severely immunocompromised patients with CAP may benefit from adjunctive therapies to enhance specific functions of the immune system. Therefore, future studies need to focus on patients with risk factors for immunodeficiency in order to provide clinicians with recommendations for the management of immunocompromised patients with CAP.

Treatment guidelines for patients on special medication (i.e. patients on chronic steroids, biological drugs and cancer patients): Patients suffering from pre-existing lung diseases such as asthma or COPD are often treated with either inhaled or systemic corticosteroids [ 155 ]. Although these drugs reduce inflammation and might prevent exacerbations, the impact of the routine use of corticosteroids on CAP patients has not been investigated to full extent [ 156 ]. Biological drugs targeting tumour immune evasive pathways for the treatment of lung cancer [ 157 ] may guide different treatment regimens of CAP due to a changed immune status in these patients. With the ever-increasing number of patients suffering from pre-existing lung disease, it will be necessary to develop adjusted guidelines and treatment recommendations.

Influence of CAP on dementia and neurological damage: As in sepsis, during the acute phase of pneumonia, confusion is frequently observed [ 158 , 159 ]. Evidence suggests that delirium may hasten cognitive deterioration in people with pre-existing dementia. Since pneumonia is primarily a disease of older patients there is the possibility that an episode of pneumonia will cause neurological damage and that the early onset of worsening of pre-existing dementia is a long-term consequence of CAP that has been overlooked so far.

Long-term cardio-vascular complications

Hospitalized CAP patients have an increased risk of major acute and long-term cardiovascular complications (i.e. new/worsening heart failure, new/worsening arrhythmias, myocardial infarctions and/or strokes) [ 151 , 160 ], which are associated with a 60% increase in short-term mortality [ 161 ]. Mortality remains increased even in long-term survivors of pneumonia [ 27 , 162 ], an effect that has at least in part been attributed to an increased risk for cardiovascular diseases such as heart failure or atherosclerosis [ 151 , 163 , 164 ]. As a result of both acute and long-term effects, the ensuing risk for cardiovascular events associated with pneumonia is similar to or higher as compared to classic cardiovascular risk factors such as smoking or diabetes [ 165 ]. The underlying pathophysiological processes, however, are so far largely unclear.

Spectrum, incidence and outcome of cardio-vascular disease after pneumonia: While there is clearly emerging evidence from epidemiological and preclinical studies for the association of pneumonia with both short- and long-term cardiovascular events, a comprehensive analysis of the various clinical manifestations (e.g. systolic or diastolic heart failure, arrhythmias, atherosclerotic and ischemic events) of cardiovascular disease (CVD), their incidence at different time points following a pneumonia event, and their association with outcome is as yet lacking, but would be critical to develop better diagnostic tools and ultimately tailor interventional trials.

Patients at increased risk of developing cardio-vascular disease after pneumonia: Patients with pre-existing chronic CVD have the strongest risk, followed by patients with common comorbidities including chronic obstructive pulmonary disease (COPD), ischemic heart disease, and diabetes [ 160 ]. The influence of these risk factors prior to the event of pneumonia, however, has not been stratified. Similarly, genetic traits associated with an increased risk for CVD after pneumonia have so far not been assessed. Furthermore, most studies focus so far on short-term outcomes of CAP. Cardiovascular complications associated with CAP may occur up to 10 years or more after the event of pneumonia. It would be helpful to stratify known cardiovascular risk factors to identify patients at an increased risk of developing CVD as early as possible and to develop therapeutic interventions to reduce the incidence of cardiac complications following CAP. Similarly, it would be helpful to identify microbial characteristics, and/or markers of pneumonia severity and host response that may predict association with CVD and CVD-related mortality [ 166 ].

Prevention of cardio-vascular disease after pneumonia: At present, our understanding of the patho-mechanisms that drive acute and chronic CVD following pneumonia is at best rudimentary. Discussed mechanisms include but are not limited to invasion of microbes such as S. pneumoniae into the myocardium with formation of microscopic lesions [ 150 ], or dissemination of inflammatory cells or pro-inflammatory mediators and extracellular vesicles driving inflammatory cardiomyopathic syndromes or formation and destabilization of atherosclerotic plaques [ 151 ]. A detailed in-depth understanding of these mechanisms by comparative systems medicine approaches in patient cohorts and preclinical models is required to inform the development and testing of targeted interventions to prevent CVD in patients-at-risk and/or in specific responder subgroups and as such, to create personalized therapeutic approaches.

Pneumonia has been known as the leading infectious cause of death for more than a century and many attempts have been made to change this fact. However, the mortality of CAP has barely changed in the last 50 years and the Covid-19 pandemic has once again highlighted the central importance of acute respiratory infections to human health. Pneumonia is not just an infection of the lung but a complex disease where the interplay between the pathogen(s), immune system and comorbidities not only impose an immediate risk of mortality but also affect the patients’ risk of developing comorbidities as well as mortality for up to a decade after the pneumonia has resolved. Despite the importance of pneumonia on human health and the fact that many of the identified topics have been focus points for several years, our review of unmet needs in CAP research has shown that there are still major shortcomings in our knowledge of CAP. The poor evidence base that exists for most clinical decisions in acute respiratory infections can no longer be considered acceptable and a co-ordinated focus and investment into research on acute respiratory infections is now needed.

Take home message

Unmet needs have been identified for diagnostics, risk stratification, treatment, adjunctive therapy, and prevention. Major knowledge gaps include immune response, role of comorbidities, and long-term cardio-vascular complications.

Availability of data and materials

Not applicable.

Abbreviations

Cardiovascular disease

Community-acquired pneumonia

Computer tomography

Coronary artery calcium

Chronic obstructive pulmonary disease

Cryptogenic organizing pneumonia

Hospital-acquired pneumonia

Idiopathic pulmonary fibrosis

Intensive care unit

Janus kinase

Multidrug resistant

Network of excellence on Community Acquired Pneumonia

Pneumococcal conjugate vaccines

Pneumococcal polysaccharide vaccines

Respiratory syncytial virus

Ventilator-associated pneumonia

7-Valent conjugate vaccine

13-Valent conjugate vaccine

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Acknowledgements

CAPNETZ is a multidisciplinary approach to better understand and treat patients with community-acquired pneumonia. The network has only been made possible by the contribution of many investigators. We are especially indebted to the work of the investigators in the local clinical centres (LCC) that established and maintained contact with all practitioners, physicians, and respiratory specialists cooperating within the network. The members of the CAPNETZ study group are as follows:

M. Dreher, C. Cornelissen (Aachen); W. Knüppel (Bad Arolsen); D. Stolz (Basel, Switzerland); N. Suttorp, M. Niebank, A. Mikolajewska, M. Witzenrath, W. Pankow, S. Gläser, D. Thiemig (Berlin); M. Prediger, S. Schmager (Cottbus); M. Kolditz, B. Schulte-Hubbert, S. Langner (Dresden); G. Rohde, C. Bellinghausen (Frankfurt); M. Panning (Freiburg); C. Hoffmann (Hamburg); T. Welte, J. Freise, G. Barten, W. Kröner, M. Nawrocki, J. Naim, T. Illig, N. Klopp (Hannover); M. Pletz, C. Kroegel, B. Schleenvoigt, C. Forstner, A. Moeser (Jena); D. Drömann, P. Parschke, K. Franzen, J. Rupp, N. Käding (Lübeck); M. Wouters, K. Walraven, D. Braeken (Maastricht, The Netherlands); C. Spinner (Munich); A. Zaruchas (Paderborn); Schaberg, D. Heigener, I. Hering (Rotenburg/Wümme); W. Albrich, F. Waldeck, F. Rassouli, S. Baldesberger (St. Gallen, Switzerland); S. Stenger, M. Wallner (Ulm); H. Burgmann, L. Traby (Vienna); and all study nurses.

We sincerely thank the international CAP experts for their important and invaluable feedback. Without their contribution, this work would not have been possible.

Open Access funding enabled and organized by Projekt DEAL. CAPNETZ was funded by a German Federal Ministry of Education and Research grant (01KI07145) 2001–2011 and is supported by the German Center for Lung Research (DZL).

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Mathias W. Pletz and Andreas Vestergaard Jensen have contributed equally to this publication

Authors and Affiliations

Institute of Infectious Diseases and Infection Control, Jena University Hospital - Friedrich Schiller University Jena, Jena, Germany

Mathias W. Pletz & Christina Bahrs

CAPNETZ STIFTUNG, Hannover, Germany

Mathias W. Pletz, Jan Rupp, Martin Witzenrath, Grit Barten-Neiner & Gernot Rohde

Department of Pulmonary and Infectious Diseases, Nordsjællands Hospital, Hillerød, Denmark

Andreas Vestergaard Jensen

Division of Infectious Diseases and Tropical Medicine, Department of Medicine I, Medical University of Vienna, Vienna, Austria

Christina Bahrs

Department of Infectious Diseases and Microbiology, University Hospital Schleswig-Holstein, German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Lübeck, Germany

Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany

Martin Witzenrath & Norbert Suttorp

Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany

Claudia Davenport, Grit Barten-Neiner, Sabine Dettmer & Gernot Rohde

Division of Pulmonology, Medical Department I, University Hospital Carl Gustav Carus, Dresden, Germany

Martin Kolditz

Department of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany

Sabine Dettmer

Scottish Centre for Respiratory Research, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, UK

James D. Chalmers

Clinic of Respiratory Medicine and Pulmonary Cell Research, University Hospital, Basel, Switzerland

Daiana Stolz

Department of Pneumology, University Medical Center, Freiburg, Germany

Martin Witzenrath & Daiana Stolz

German Center for Lung Research (DZL), Berlin, Germany

Norbert Suttorp

Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele, Milan, Italy

Stefano Aliberti

IRCCS Humanitas Research Hospital, Respiratory Unit, Via Manzoni 56, 20089, Rozzano, Milan, Italy

Institute of Physiology, Charité – Universitätsmedizin Berlin, Berlin, Germany

Wolfgang M. Kuebler

Department of Respiratory Medicine, Medical Clinic I, University hospital, Goethe University Frankfurt, Theodor-Stern-Kai 7, 60596, Frankfurt/Main, Germany

Gernot Rohde

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Contributions

GBN, as part of CAPNETZ, was responsible for organizing the meeting. AVJ was responsible for grouping the views from the invited researchers into headlines for discussion at the scientific retreat. CD took minutes during the scientific retreat which was used for drafting the individual sections. The individual sections were then drafted by one of the participants of the scientific retreat as follows; Detection of causative pathogens (JR), Next generation sequencing for guidance of antimicrobial treatment (JDC), Imaging diagnostics (SD), Biomarkers (DS). Risk stratification (MK), Antiviral and antibiotic treatment (GR), Adjunctive therapy (NS), Vaccines and prevention (MWP, CB), Systemic and local immune response (MW), Comorbidities (SA), Long-term cardio-vascular complications (WMK). AVJ and MWP reviewed the individual sections and prepared the first draft. All authors reviewed the draft manuscript. All authors were responsible for the decision to publish the manuscript. All authors read and approved the final manuscript.

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Pletz, M.W., Jensen, A.V., Bahrs, C. et al. Unmet needs in pneumonia research: a comprehensive approach by the CAPNETZ study group. Respir Res 23 , 239 (2022). https://doi.org/10.1186/s12931-022-02117-3

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Atypical presentation of an atypical pneumonia: a case report

• Alvin Oliver Payus   ORCID: orcid.org/0000-0003-4675-103X 1 ,
• Clarita Clarence 2 ,
• Tiong Nee 3 &
• Wan Nur Nafisah Wan Yahya 2  

Journal of Medical Case Reports volume  16 , Article number:  105 ( 2022 ) Cite this article

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Neurologic impediments occur in only 0.1% of Mycoplasma pneumoniae infections. Although direct intracerebral infection can occur in these patients, autoimmune-mediated reactions secondary to molecular mimicry are the most common pathophysiology of such neurological complications. These complications include immune-mediated encephalitis, peripheral neuritis such as Guillain–Barré syndrome, and many others. Miller Fisher syndrome is a one of the variants of Guillain–Barré syndrome that has been rarely linked to Mycoplasma pneumoniae infection. It is a condition classically characterized by the triad of ophthalmoplegia, areflexia, and ataxia. Most patients with Miller Fisher syndrome will have positive anti-ganglioside GQ1b antibodies found in their serum, making this autoantibody a very useful serological confirmation parameter. We report a case of a Miller Fisher syndrome in a woman with Mycoplasma pneumoniae infection. To the best of the authors’ knowledge, such cases have been only rarely described in literature.

Case presentation

A 35-year-old Chinese woman presented with sudden onset of double vision and ataxia 5 days after fever and mild flu symptoms. Her Mycoplasma pneumoniae antigen was positive with 1 over 2560 titer of total mycoplasma antibody and presence of immunoglobulin M antibody, suggesting acute infection, and her nerve conduction study revealed mild sensory axonal polyneuropathy with segmental demyelination. the Miller Fischer syndrome variant of Guillain-Barré syndrome secondary to Mycoplasma pneumonia was suspected and later confirmed by presence of serum anti-GQ1b autoantibody. She was treated with intravenous immunoglobulin 0.4 g/kg once daily for 5 days.

Conclusions

The objective of this report is to share a case of an uncommon neurological complication of Mycoplasma pneumoniae infection, to increase the level of suspicion among clinicians that Miller Fischer syndrome can occur as an atypical presentation of an atypical pneumonia.

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Introduction

Mycoplasma pneumoniae (MP) is an atypical microorganism that commonly causes community-acquired pneumonia. This organism has peculiar properties that not only make it invisible on the usual Gram stain but also nonsusceptible to the broad-spectrum beta-lactam drugs usually applied as first-line antibiotics to treat community-acquired infection [ 1 ]. MP causes atypical pneumonia, associated with a list of extrapulmonary manifestations. These include hemolytic anemia, myringitis, Guillain–Barré syndrome and its variant, and many others. Miller Fisher syndrome is one of the rare extrapulmonary manifestations of MP infection [ 2 ]. This atypical presentation of an atypical pneumonia is the center of discussion in this case report.

A 35-year-old Chinese female with no known medical illness presented with double vision and body imbalances for the past 2 days. She described that the diplopia was of sudden onset, painless, and did not occur on looking in any specific direction. Regarding the body imbalance, she showed a tendency to sway to the right side. On further questioning, she also had history of preceding fever with mild flu symptoms for the past 5 days.

She also complained of numbness and cramping sensation over the hands and feet bilaterally, 1 day before presentation. Otherwise, there was no neck stiffness, no upper and lower limb weakness, no slurring of speech, no dysphagia, no dyspnea, and no history of recent accident or trauma. Upon arrival to the emergency department, she required assistance for ambulation as she has difficulty in maintaining balance. Her vital signs were stable with blood pressure of 124/78 mmHg, pulse rate of 80 beats per minute, regular rhythm and no collapsing character, afebrile, and not tachypneic. On physical examination, she had diplopia over the lateral gaze bilaterally, but otherwise there was no nystagmus, no dysdiadochokinesia, no dysmetria, and no facial weakness, and examination of the rest of the cranial nerves, upper limbs, and lower limbs revealed no abnormal findings. Romberg’s test was negative, but sharpened Romberg’s test was positive. Her abdomen was soft, not tender, and there was no palpable mass or organomegaly. Examination of the cardiorespiratory system revealed no abnormal findings. Initial blood investigation was normal (Table 1 ).

Blood investigations taken upon arrival to the hospital showed normal cell counts. There was no electrolyte abnormality, and the renal profile and liver function were also normal.

Computed tomographic scan of the brain revealed no intracranial bleeding and no space-occupying lesion (Fig. 1 ). Nerve conduction study revealed mild sensory axonal polyneuropathy with segmental demyelination. H reflex was absent bilaterally. Lumbar puncture for cerebrospinal fluid analysis was done and showed normal cell counts and biochemistry profile and no abnormal cells, and was negative for bacterial culture, multiple viral antibody panels, and cryptococcal antigen test. In view of the presence of ataxia and diplopia, the Miller Fischer syndrome variant of Guillain–Barré syndrome was suspected and later confirmed with the presence of serum anti-GQ1b autoantibody. Intravenous immunoglobulin (IVIg) 0.4 gm/kg once daily was started and planned to complete for 5 days. On the second day of admission, she developed worsening cough and shortness of breath. Radiographic imaging of the chest revealed homogeneous opacity over the lower right zone (Fig. 2 ). She was initially started on intravenous (IV) co-amoxiclav 1.2 g three times daily, and was escalated to IV piperacillin–tazobactam 4.5 gm three times daily after 2 days after she failed to show any improvement. As atypical pneumonia was suspected by the treating physician in view of the associated neurological symptoms, Mycoplasma pneumoniae antigen was taken and came back positive with 1 over 2560 titer of total mycoplasma antibody and presence of IgM antibody, suggestive of an acute infection. She was started with macrolide antibiotic (oral azithromycin 500 mg once daily for 5 days) on top of the previous IV antibiotic and IVIg treatment. She was also subjected to inpatient physiotherapy for a few days, then discharged well.

Computed tomographic imaging of brain on admission, showing no intracranial bleeding or space-occupying lesion

Radiographic imaging of chest taken on second day of admission when the patient developed shortness of breath, showing heterogeneous opacity over the lower right zone

Mycoplasma pneumoniae (MP) is a common respiratory pathogen. This short, rod-shaped bacterium lacking a cell wall commonly causes respiratory infections such as bronchitis and pneumonia. It is often considered to be an atypical organism as it is not visible on the usual Gram stain and cannot be cultured using standard methods [ 1 ]. This organism is also not susceptible to the broad-spectrum beta-lactam antibiotics normally used as first-line treatment for community-acquired pneumonia. MP is one of the causative organisms of “atypical” bacterial pneumonia because the respiratory manifestations are predominated by a complex of constitutional symptoms such as low-grade fever, headache, and malaise, usually associated with extrapulmonary manifestations such as hemolytic anemia, myocarditis, myringitis, encephalitis, and many others that can occur alongside or independent of the respiratory symptoms [ 2 ]. Miller Fisher syndrome (MFS) is one of the rare extrapulmonary manifestations of MP infection.

MFS is considered a rare variant of Guillain–Barré syndrome, which is an acute idiopathic, immune-mediated inflammatory polyradiculopathy. The pathophysiology of this condition is still not well understood, but autoantibody-mediated neuritis, a condition in which the immune system attacks the nerves as a result of molecular mimicry that can be triggered by various agents, is a possibility. Among the common pathogens are Campylobacter jejuni , Haemophilus influenzae , and cytomegalovirus. However, MP is a rare pathogen also found to be associated with MFS. MFS classically presents with a triad of ataxia, ophthalmoplegia, and areflexia. However, the clinical manifestations of MFS can vary widely. The less common presentations include limb dysesthesia, ptosis, facial and bulbar palsies, mild muscle weakness, and urinary incontinence [ 3 ]. MFS is two times more common in men and can affect people of all ages, with median age of onset in the fifth decade [ 4 ].

The diagnosis of MFS is made clinically. However, serological confirmation with the presence of Anti-GQ1b antibodies is often made to support the diagnosis. This antibody acts on GQ1b, which is a ganglioside found abundantly in the paranodal region of the extramedullary portion of the oculomotor, abducens, and trochlear nerves. These areas are usually those most affected by self-reactive Anti-GQ1b antibodies, blocking release of acetylcholine from the motor nerve ending [ 4 ]. Although this antibody is not unique to MFS, it can help support the diagnosis in cases of uncertainty. Moreover, it can also determine the severity of the disease, as the level relates to the disease activity.

MFS is a self-limiting condition, and gradual improvement marks its recovery period and often the resolution of symptoms. Rarely, serious complications such as cardiac arrhythmia or respiratory failure have been reported [ 5 ]. Ataxia and ophthalmoplegia typically resolve within 1–3 months after onset, and near-complete recovery is expected within 6 months [ 5 ]. Though areflexia may persist, it is not associated with functional disability. Although MFS follows a self-limiting course, immunomodulatory therapies including intravenous immune globulin and plasmapheresis have been used to hasten disease recovery and perhaps decrease the likelihood of progression to more severe conditions such as GBS [ 6 ]. Based on the observational data available, complete resolution of ataxia in 1 month and resolution of ophthalmoplegia within 3 months would be acceptable outcome measures [ 5 ].

In this report, our patient presented initially with ataxia and diplopia, suggestive of Miller Fischer syndrome that was later confirmed with the presence of anti-GQ1b antibody in serum. She was then treated with IVIg 0.4 g/kg once daily for a total of 5 days. However, she developed worsening respiratory symptoms later on during inpatient immunoglobulin therapy. She did not respond to first-line beta-lactam antibiotics and was investigated for atypical pneumonia, which was later confirmed with the presence of Mycoplasma pneumoniae antibody titer in serum and clinically response to macrolide antibiotic. She recovered well and is currently under regular outpatient clinic and physiotherapy follow-up.

The aim of this case report is to increase the level of suspicion among clinicians regarding the possibility of an association between Miller Fischer syndrome and Mycoplasma pneumoniae infection, which should be considered in any cases of pneumonia that develop neurological manifestations such as ophthalmoplegia and ataxia.

Availability of data and materials

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Acknowledgements

The authors would like to thank the Director General of Ministry of Health of Malaysia for his permission to publish this article.

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Alvin Oliver Payus

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AOP was a major contributor to the writing of this manuscript. CC analyzed and interpreted the patient data. RH helped in data collection and contributed to writing the manuscript. WNNWY is the advisor and helped with writing and final editing of the manuscript. All authors read and approved the final manuscript

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Payus, A.O., Clarence, C., Nee, T. et al. Atypical presentation of an atypical pneumonia: a case report. J Med Case Reports 16 , 105 (2022). https://doi.org/10.1186/s13256-022-03320-y

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Occurrence and treatment of suspected pneumonia in long-term care residents dying with advanced dementia

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• 1 Hebrew SeniorLife, Research and Training Institute and Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts 02131, USA. [email protected]
• PMID: 16460381
• DOI: 10.1111/j.1532-5415.2005.00524.x

Objectives: To describe the occurrence and management of suspected pneumonia in end-stage dementia and to identify factors associated with aggressiveness of antibiotic treatment.

Design: Retrospective cohort study.

Setting: A 675-bed long-term-care facility in Boston, Massachusetts.

Participants: Two hundred forty subjects aged 65 and older who died with advanced dementia between January 2001 and December 2003. Subjects who had suspected pneumonia during the last 6 months of life were identified.

Measurements: Independent variables included subject characteristics and features of suspected pneumonia episodes. These variables were obtained from medical records. Antibiotic treatment for each episode was determined. Multivariate analysis was used to identify independent variables associated with aggressiveness of treatment.

Results: One hundred fifty-four (64%) subjects with advanced dementia experienced 229 suspected pneumonia episodes during the last 6 months of life. Within 30 days of death, 53% of subjects had suspected pneumonia. Antibiotic treatment for the 229 episodes was as follows: none, 9%; oral only, 37%; intramuscular, 25%; and intravenous, 29%. Factors independently associated with more-invasive therapy were lack of a do-not-hospitalize order (adjusted odds ratio (AOR) = 3.24, 95% confidence interval (CI) = 2.02-5.22), aspiration (AOR = 2.75, 95% CI = 1.44-5.26), primary language not English (AOR = 2.21, 95% CI = 1.17-4.15), and unstable vital signs (AOR = 2.02, 95% CI = 1.10-3.72).

Conclusion: Pneumonia is a common terminal event in advanced dementia for which many patients receive parenteral antibiotics. The aggressiveness of treatment is most strongly determined by advance care planning, the patient's cultural background, and clinical features of the suspected pneumonia episode.

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Volume 30, Number 5—May 2024

Research Letter

Novel patterns in high-resolution computed tomography in whipple pneumonia.

Suggested citation for this article

With the use of metagenomic next-generation sequencing, patients diagnosed with Whipple pneumonia are being increasingly correctly diagnosed. We report a series of 3 cases in China that showed a novel pattern of movable infiltrates and upper lung micronodules. After treatment, the 3 patients recovered, and lung infiltrates resolved.

Whipple pneumonia is a rare, chronic, multiorgan disease, with symptoms including arthritis, diarrhea, and weight loss. Diagnosis is traditionally confirmed by a histologic examination of a small bowel biopsy ( 1 ). The causative pathogen is Tropheryma whipplei bacteria, initially identified from the aortic valve of an endocarditis patient in 2000 ( 2 ). The bacterium was successfully cultured again in 2012 by using a sample of bronchoalveolar lavage fluid (BALF) from a pneumonia patient with an acute pulmonary infection ( 2 ). By using special culture systems, laboratories can grow positive staining or immunofluorescence detectable bacteria within a macrophage or fibroblast cell in 40–60 days. Metagenomic next-generation sequencing (mNGS) is a useful tool for diagnosis.

We report 3 patients in China diagnosed with T. whipplei pneumonia by using BALF mNGS (Vision Medicals Company, http://www.visionmedicals.com ) screening during July 2021–December 2022. The patients had unique radiologic patterns, including upper lung gathering of micronodules forming a galaxy sign, and slightly movable infiltrates before, during, and after treatment.

Patient 1 was a 46-year-old man with a productive cough and a 5-year history of lung abnormality. His lung lesions gradually increased over time, and we found gathering micronodules forming a galaxy sign on the right upper lung ( Appendix Figure 1). T. whipplei bacteria was the only pathogen we recovered from BALF screened by using mNGS.

Figure . High-resolution computed tomography imaging and histology findings of the lung biopsy from a 67-year-old patient in China who had Tropheryma whipplei pneumonia. A) High-resolution computed tomography imaging showing...

Patient 2 was a 67-year-old man with progressive dyspnea, productive cough, poor appetite, and weight loss. Repeated high-resolution computed tomography (CT) showed gradual increase of diffused micronodules gathering on the upper right lung for 6 months before diagnosis ( Figure , panel A). Lesions in the upper right lung also showed movement. After bronchoscopic examination, T. whipplei bacteria was the only pathogen we recovered from BALF. Our histologic examination of the lung biopsy showed increased foamy macrophages within the alveolar space and thickened alveolar septal ( Figure , panel B); neutrophils were the predominant cell type seen.

Patient 3 was a 57-year-old man with complaints of cough and chest tightness. We found diffuse ground-glass micronodules in the left upper lung ( Appendix Figure 2). We performed mNGS of BALF and found Cryptococcus spp. yeast and T. whipplei bacteria. We treated the patient with fluconazole. Six months later, the patient was readmitted to our hospital because of chest tightness and dry cough. We repeated mNGS, and T. whipplei bacteria was the only pathogen identified.

The lung tissue from all 3 patients was negative for periodic acid-Schiff and anti-acid staining. We performed an enteroscopic examination on the patients 2 and 3; both were negative. We treated the 3 patients with intravenous ceftriaxone (2 g/d) for 2 weeks, then we began combination therapy of minocycline and hydroxychloroquine for an extended period. All 3 patients responded well to treatment, and chest CT showed improvement of lung lesions.

We conducted a literature review for similar cases. We systematically reviewed PubMed for “ T. whipplei ” or “Whipple’s disease” and “pneumonia” for the period July 2021–December 2022. We included literature for analysis if they provided individual patient and imaging data. We defined acute pulmonary infection by classic clinical manifestation and opacity on a chest radiograph or a CT scan. A total of 97 patients with Whipple pneumonia were mentioned. CT findings were available for 14 patients from 7 studies ( 2 – 8 ). The CT findings included bilateral alveolar consolidation, mass, nodule with cavitation, ground-glass opacity, and diffuse micronodules ( Table ). Mediastinal lymphadenopathy was described in 1 patient. Therapeutic outcomes were described in 5 patients, and no patients died from pneumonia. Only 1 patient had a comparative chest CT before and after treatment. No patients demonstrated movable lung infiltrates.

A 17-year-long retrospective study identified 36 patients with positive PCR results of T. whipplei bacteria; of those, 8 patients had pulmonary involvement, and only 3 patients had abnormalities in chest imaging ( 9 ). Another study showed that 6.1% (88/1,430 samples) of BALF samples were positive for T. whipplei bacteria; 58 patients had pneumonia, and T. whipplei bacteria was identified as the causative pathogen for 9 patients ( 10 ). One study analyzed the characteristics of 70 patients positive for T. whipplei bacteria in BALF detected by mNGS in which T. whipplei was the only pathogen recovered for 20 patients ( 8 ); in that study, 15 patients received therapy, and 6 patients improved after treatment ( 8 ). In our study, T. whipplei bacteria was the only pathogen in 2 patients and was repeatedly detected in the third patient. In our patients, the infiltrates exhibited movable changes over time before, during, and after treatments. Histologic examination of case 2 showed a collagen and carbon deposition within lung tissue without any history of coal mine exposure, suggesting that T. whipplei bacterial infection can cause chronic infection and scar formation, eventually leading to granulomatous-like changes within lung tissue. All 3 patients symptoms improved after receiving the first-line treatment recommendation of minocycline and hydroxychloroquine ( 1 ).

In conclusion, Whipple pneumonia is increasingly recognized when mNGS is used. We report a relatively unique feature of CT findings in patients with Whipple pneumonia and provide support for choosing combination treatment using minocycline and hydroxychloroquine.

Dr. Li is a respiratory diseases specialist at the Seventh Affiliated Hospital, Sun Yatsen University. Her research interests include the mechanisms of respiratory infection and lung injury.

• Boumaza  A , Ben Azzouz  E , Arrindell  J , Lepidi  H , Mezouar  S , Desnues  B . Whipple’s disease and Tropheryma whipplei infections: from bench to bedside. Lancet Infect Dis . 2022 ; 22 : e280 – 91 . DOI PubMed Google Scholar
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• Canessa  PA , Pratticò  L , Sivori  M , Magistrelli  P , Fedeli  F , Cavazza  A , et al. Acute fibrinous and organising pneumonia in Whipple’s disease. Monaldi Arch Chest Dis . 2008 ; 69 : 186 – 8 . PubMed Google Scholar
• Lin  M , Wang  K , Qiu  L , Liang  Y , Tu  C , Chen  M , et al. Tropheryma whipplei detection by metagenomic next-generation sequencing in bronchoalveolar lavage fluid: A cross-sectional study. Front Cell Infect Microbiol . 2022 ; 12 : 961297 . DOI PubMed Google Scholar
• Duss  FR , Jaton  K , Vollenweider  P , Troillet  N , Greub  G . Whipple disease: a 15-year retrospective study on 36 patients with positive polymerase chain reaction for Tropheryma whipplei. Clin Microbiol Infect . 2021 ; 27 : 910.e9 – 13 . DOI PubMed Google Scholar
• Lagier  JC , Papazian  L , Fenollar  F , Edouard  S , Melenotte  C , Laroumagne  S , et al. Tropheryma whipplei DNA in bronchoalveolar lavage samples: a case control study. Clin Microbiol Infect . 2016 ; 22 : 875 – 9 . DOI PubMed Google Scholar
• Figure . High-resolution computed tomography imaging and histology findings of the lung biopsy from a 67-year-old patient in China who had Tropheryma whipplei pneumonia. A) High-resolution computed tomography imaging showing gradual...
• Table . Categorized data from previously published studies on high-resolution CT findings, symptoms, inflammatory indicators, and immune status in patients with Tropheryma whipplei infection

Suggested citation for this article : Li H, Wu J, Mai X, Zeng W, Cai S, Huang X, et al. Novel patterns in high-resolution computed tomography in Whipple pneumonia. Emerg Infect Dis. 2024 May [ date cited ]. https://doi.org/10.3201/eid3005.231130

DOI: 10.3201/eid3005.231130

Original Publication Date: April 10, 2024

Table of Contents – Volume 30, Number 5—May 2024

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Hui Li, the Seventh Affiliated Hospital, Sun Yatsen University, 628 Zhenyuan Rd., Shenzhen, China

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• Open access
• Published: 18 April 2024

The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case–control study

• Maryam Seyedtabib   ORCID: orcid.org/0000-0003-1599-9374 1 ,
• Roya Najafi-Vosough   ORCID: orcid.org/0000-0003-2871-5748 2 &
• Naser Kamyari   ORCID: orcid.org/0000-0001-6245-5447 3  

BMC Infectious Diseases volume  24 , Article number:  411 ( 2024 ) Cite this article

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Background and purpose

The COVID-19 pandemic has presented unprecedented public health challenges worldwide. Understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies. This study aims to unlock the predictive power of data collected from personal, clinical, preclinical, and laboratory variables through machine learning (ML) analyses.

A retrospective study was conducted in 2022 in a large hospital in Abadan, Iran. Data were collected and categorized into demographic, clinical, comorbid, treatment, initial vital signs, symptoms, and laboratory test groups. The collected data were subjected to ML analysis to identify predictive factors associated with COVID-19 mortality. Five algorithms were used to analyze the data set and derive the latent predictive power of the variables by the shapely additive explanation values.

Results highlight key factors associated with COVID-19 mortality, including age, comorbidities (hypertension, diabetes), specific treatments (antibiotics, remdesivir, favipiravir, vitamin zinc), and clinical indicators (heart rate, respiratory rate, temperature). Notably, specific symptoms (productive cough, dyspnea, delirium) and laboratory values (D-dimer, ESR) also play a critical role in predicting outcomes. This study highlights the importance of feature selection and the impact of data quantity and quality on model performance.

This study highlights the potential of ML analysis to improve the accuracy of COVID-19 mortality prediction and emphasizes the need for a comprehensive approach that considers multiple feature categories. It highlights the critical role of data quality and quantity in improving model performance and contributes to our understanding of the multifaceted factors that influence COVID-19 outcomes.

Peer Review reports

Introduction

The World Health Organization (WHO) has declared COVID-19 a global pandemic in March 2020 [ 1 ]. The first cases of SARSCoV-2, a new severe acute respiratory syndrome coronavirus, were detected in Wuhan, China, and rapidly spread to become a global public health problem [ 2 ]. The clinical presentation and symptoms of COVID-19 may be similar to those of Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS), however the rate of spread is higher [ 3 ]. By December 31, 2022, the pandemic had caused more than 729 million cases and nearly 6.7 million deaths (0.92%) were confirmed in 219 countries worldwide [ 4 ]. For many countries, figuring out what measures to take to prevent death or serious illness is a major challenge. Due to the complexity of transmission and the lack of proven treatments, COVID-19 is a major challenge worldwide [ 5 , 6 ]. In middle- and low-income countries, the situation is even more catastrophic due to high illiteracy rates, a very poor health care system, and lack of intensive care units [ 5 ]. In addition, understanding the factors contributing to COVID-19 mortality is critical for effective management and intervention strategies [ 6 ].

Numerous studies have shown several factors associated with COVID-19 outcomes, including socioeconomic, environmental, individual demographic, and health factors [ 7 , 8 , 9 ]. Risk factors for COVID -19 mortality vary by study and population studied [ 10 ]. Age [ 11 , 12 ], comorbidities such as hypertension, cardiovascular disease, diabetes, and COPD [ 13 , 14 , 15 ], sex [ 13 ], race/ethnicity [ 11 ], dementia, and neurologic disease [ 16 , 17 ], are some of the factors associated with COVID-19 mortality. Laboratory factors such as elevated levels of inflammatory markers, lymphopenia, elevated creatinine levels, and ALT are also associated with COVID-19 mortality [ 5 , 18 ]. Understanding these multiple risk factors is critical to accurately diagnose and treat COVID-19 patients.

Accurate diagnosis and treatment of the disease requires a comprehensive assessment that considers a variety of factors. These factors include personal factors such as medical history, lifestyle, and genetics; clinical factors such as observations on physical examinations and physician reports; preclinical factors such as early detection through screening or surveillance; laboratory factors such as results of diagnostic tests and medical imaging; and patient-reported signs and symptoms. However, the variety of characteristics associated with COVID-19 makes it difficult for physicians to accurately classify COVID-19 patients during the pandemic.

In today's digital transformation era, machine learning plays a vital role in various industries, including healthcare, where substantial data is generated daily [ 19 , 20 , 21 ]. Numerous studies have explored machine learning (ML) and explainable artificial intelligence (AI) in predicting COVID-19 prognosis and diagnosis [ 22 , 23 , 24 , 25 ]. Chadaga et al. have developed decision support systems and triage prediction systems using clinical markers and biomarkers [ 22 , 23 ]. Similarly, Khanna et al. have developed a ML and explainable AI system for COVID-19 triage prediction [ 24 ]. Zoabi has also made contributions in this field, developing ML models that predict COVID-19 test results with high accuracy based on a small number of features such as gender, age, contact with an infected person and initial clinical symptoms [ 25 ]. These studies emphasize the potential of ML and explainable AI to improve COVID-19 prediction and diagnosis. Nonetheless, the efficacy of ML algorithms heavily relies on the quality and quantity of data utilized for training. Recent research has indicated that deep learning algorithms' performance can be significantly enhanced compared to traditional ML methods by increasing the volume of data used [ 26 ]. However, it is crucial to acknowledge that the impact of data volume on model performance can vary based on data characteristics and experimental setup, highlighting the need for careful consideration and analysis when selecting data for model training. While the studies emphasize the importance of features in training ML algorithms for COVID-19 prediction and diagnosis, additional research is required on methods to enhance the interpretability of features.

Therefore, the primary aim of this study is to identify the key factors associated with mortality in COVID -19 patients admitted to hospitals in Abadan, Iran. For this purpose, seven categories of factors were selected, including demographic, clinical and conditions, comorbidities, treatments, initial vital signs, symptoms, and laboratory tests, and machine learning algorithms were employed. The predictive power of the data was assessed using 139 predictor variables across seven feature sets. Our next goal is to improve the interpretability of the extracted important features. To achieve this goal, we will utilize the innovative SHAP analysis, which illustrates the impact of features through a diagram.

Materials and methods

Study population and data collection.

Using data from the COVID-19 hospital-based registry database, a retrospective study was conducted from April 2020 to December 2022 at Ayatollah Talleghani Hospital (a COVID‑19 referral center) in Abadan City, Iran.

A total of 14,938 patients were initially screened for eligibility for the study. Of these, 9509 patients were excluded because their transcriptase polymerase chain reaction (RT-PCR) test results were negative or unspecified. The exclusion of patients due to incomplete or missing data is a common issue in medical research, particularly in the use of electronic medical records (EMRs) [ 27 ]. In addition, 1623 patients were excluded because their medical records contained more than 70% incomplete or missing data. In addition, patients younger than 18 years were not included in the study. The criterion for excluding 1623 patients due to "70% incomplete or missing data" means that the medical records of these patients did not contain at least 30% of the data required for a meaningful analysis. This threshold was set to ensure that the dataset used for the study contained a sufficient amount of complete and reliable information to draw accurate conclusions. Incomplete or missing data in a medical record may relate to key variables such as patient demographics, symptoms, lab results, treatment information, outcomes, or other data points important to the research. Insufficient data can affect the validity and reliability of study results and lead to potential bias or inaccuracies in the findings. It is important to exclude such incomplete records to maintain the quality and integrity of the research findings and to ensure that the conclusions drawn are based on robust and reliable data. After these exclusions, 3806 patients remained. Of these patients, 474 died due to COVID -19, while the remaining 3332 patients recovered and were included in the control group. To obtain a balanced sample, the control group was selected with a propensity score matching (PSM). The PSM refers to a statistical technique used to create a balanced comparison group by matching individuals in the control group (in this case, the survived group) with individuals in the case group (in this case, the deceased group) based on their propensity scores. In this study, the propensity scores for each person represented the probability of death (coded as a binary outcome; survived = 0, deceased = 1) calculated from a set of covariates (demographic factors) using the matchit function from the MatchIt library. Two individuals, one from the deceased group and one from the survived group, are considered matched if the difference between their propensity scores is small. Non-matching participants are discarded. The matching aims to reduce bias by making the distribution of observed characteristics similar between groups, which ultimately improves the comparability of groups in observational studies [ 28 ]. In total, the study included 1063 COVID-19 patients who belonged to either the deceased group (case = 474) or the survived group (control = 589) (Fig.  1 ).

Flowchart describing the process of patient selection

In the COVID‑19 hospital‑based registry database, one hundred forty primary features in eight main classes including patient’s demographics (eight features), clinical and conditions features (16 features), comorbidities (18 features), treatment (17 features), initial vital sign (14 features), symptoms during hospitalization (31 features), laboratory results (35 features), and an output (0 for survived and 1 for deceased) was recorded for COVID-19 patients. The main features included in the hospital-based COVID-19 registry database are provided in Appendix Table  1 .

To ensure the accuracy of the recorded information, discharged patients or their relatives were called and asked to review some of the recorded information (demographic information, symptoms, and medical history). Clinical symptoms and vital signs were referenced to the first day of hospitalization (at admission). Laboratory test results were also referenced to the patient’s first blood sample at the time of hospitalization.

The study analyzed 140 variables in patients' records, normalizing continuous variables and creating a binary feature to categorize patients based on outcomes. To address the issue of an imbalanced dataset, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized. Some classes were combined to simplify variables. For missing data, an imputation technique was applied, assuming a random distribution [ 29 ]. Little's MCAR test was performed with the naniar package to assess whether missing data in a dataset is missing completely at random (MCAR) [ 30 ]. The null hypothesis in this test is that the data are MCAR, and the test statistic is a chi-square value.

The Ethics Committee of Abadan University of Medical Science approved the research protocol (No. IR.ABADANUMS.REC.1401.095).

Predictor variables

All data were collected in eight categories, including demographic, clinical and conditions, comorbidities, treatment, initial vital signs, symptoms, and laboratory tests in medical records, for a total of 140 variables.

The "Demographics" category encompasses eight features, three of which are binary variables and five of which are categorical. The "Clinical Conditions" category includes 16 features, comprising one quantitative variable, 12 binary variables, and five categorical features. " Comorbidities ", " Treatment ", and " Symptoms " each have 18, 17, and 30 binary features, respectively. Also, there is one quantitative variable in symptoms category. The "Initial Vital Signs" category features 11 quantitative variables, two binary variables, and one categorical variable. Finally, the "Laboratory Tests" category comprises 35 features, with 33 being quantitative, one categorical, and one binary (Appendix Table  1 ).

Outcome variable

The primary outcome variable was mortality, with December 31, 2022, as the last date of follow‐up. The feature shows the class variable, which is binary. For any patient in the survivor group, the outcome is 0; otherwise, it is 1. In this study, 44.59% ( n  = 474) of the samples were in the deceased group and were labeled 1.

Data balancing

In case–control studies, it is common to have unequal size groups since cases are typically fewer than controls [ 31 ]. However, in case–control studies with equal sizes, data balancing may not be necessary for ML algorithms [ 32 ]. When using ML algorithms, data balancing is generally important when there is an imbalance between classes, i.e., when one class has significantly fewer observations than the other [ 33 ]. In such cases, balancing can improve the performance of the algorithm by reducing the bias in favor of the majority class [ 34 ]. For case–control studies of the same size, the balance of the classes has already been reached and balancing may not be necessary. However, it is always recommended to evaluate the performance of the ML algorithm with the given data set to determine the need for data balancing. This is because unbalanced case–control ratios can cause inflated type I error rates and deflated type I error rates in balanced studies [ 35 ].

Feature selection

Feature selection is about selecting important variables from a large dataset to be used in a ML model to achieve better performance and efficiency. Another goal of feature selection is to reduce computational effort by eliminating irrelevant or redundant features [ 36 , 37 ]. Before generating predictions, it is important to perform feature selection to improve the accuracy of clinical decisions and reduce errors [ 37 ]. To identify the best predictors, researchers often compare the effectiveness of different feature selection methods. In this study, we used five common methods, including Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Naïve Bayes (NB), and Random Forest (RF), to select relevant features for predicting mortality of COVID -19 patients. To avoid overfitting, we performed ten-fold cross-validation when training our dataset. This approach may help ensure that our model is optimized for accurate predictions of health status in COVID -19 patients.

Model development, evaluation, and clarity

In this study, the predictive models were developed with five ML algorithms, including DT, XGBoost, SVM, NB, and RF, using the R programming language (v4.3.1) and its packages [ 38 ]. We used cross-validation (CV) to tune the hyperparameters of our models based on the training subset of the dataset. For training and evaluating our ML models, we used a common technique called tenfold cross validation [ 39 ]. The primary training dataset was divided into ten folding, each containing 10% of the total data, using a technique called stratified random sampling. For each of the 30% of the data, a ML model was built and trained on the remaining 70% of the data. The performance of the model was then evaluated on the 30%-fold sample. This process was repeated 100 times with different training and test combinations, and the average performance was reported.

Performance measures include sensitivity (recall), specificity, accuracy, F1-score, and the area under the receiver operating characteristics curve (AUC ROC). Sensitivity is defined as TP / (TP + FN), whereas specificity is TN / (TN + FP). F1-score is defined as the harmonic mean of Precision and Recall with equal weight, where Precision equals TP + TN / total. Also, AUC refers to the area under the ROC curve. In the evaluation of ML techniques, values were classified as poor if below 50%, ok if between 50 and 80%, good if between 80 and 90%, and very good if greater than 90%. These criteria are commonly used in reporting model evaluations [ 40 , 41 ].

Finally, the shapely additive explanation (SHAP) method was used to provide clarity and understanding of the models. SHAP uses cooperative game theory to determine how each feature contributes to the prediction of ML models. This approach allows the computation of the contribution of each feature to model performance [ 42 , 43 ]. For this purpose, the package shapr was used, which includes a modified iteration of the kernel SHAP approach that takes into account the interdependence of the features when computing the Shapley values [ 44 ].

Patient characteristics

Table 1 shows the baseline characteristics of patients infected with COVID-19, including demographic data such as age and sex and other factors such as occupation, place of residence, marital status, education level, BMI, and season of admission. A total of 1063 adult patients (≥ 18 years) were enrolled in the study, of whom 589 (55.41%) survived and 474 (44.59%) died. Analysis showed that age was significantly different between the two groups, with a mean age of 54.70 ± 15.60 in the survivor group versus 65.53 ± 15.18 in the deceased group ( P  < 0.001). There was also a significant association between age and survival, with a higher proportion of patients aged < 40 years in the survivor group (77.0%) than in the deceased group (23.0%) ( P  < 0.001). No significant differences were found between the two groups in terms of sex, occupation, place of residence, marital status, and time of admission. However, there was a significant association between educational level and survival, with a lower proportion of patients with a college degree in the deceased group (37.2%) than in the survivor group (62.8%) ( P  = 0.017). BMI also differed significantly between the two groups, with the proportion of patients with a BMI > 30 (kg/cm 2 ) being higher in the deceased group (56.5%) than in the survivor group (43.5%) ( P  < 0.001).

Clinical and conditions

Important insights into the various clinical and condition characteristics associated with COVID-19 infection outcomes provides in Table  2 . The results show that patients who survived the infection had a significantly shorter hospitalization time (2.20 ± 1.63 days) compared to those who died (4.05 ± 3.10 days) ( P  < 0.001). Patients who were admitted as elective cases had a higher survival rate (84.6%) compared to those who were admitted as urgent (61.3%) or emergency (47.4%) cases. There were no significant differences with regard to the number of infections or family infection history. However, patients who had a history of travel had a lower decease rate (40.1%).

A significantly higher proportion of deceased patients had cases requiring CPR (54.7% vs. 45.3%). Patients who had underlying medical conditions had a significantly lower survival rate (38.3%), with hyperlipidemia being the most prevalent condition (18.7%). Patients who had a history of alcohol consumption (12.5%), transplantation (30.0%), chemotropic (21.4%) or special drug use (0.0%), and immunosuppressive drug use (30.0%) also had a lower survival rate. Pregnant patients (44.4%) had similar survival outcomes compared to non-pregnant patients (55.6%). Patients who were recent or current smokers (36.4%) also had a significantly lower survival rate.

Comorbidities

Table 3 summarizes the comorbidity characteristics of COVID-19 infected patients. Out of 1063 patients, 54.84% had comorbidities. Chi-Square tests for individual comorbidities showed that most of them had a significant association with COVID-19 outcomes, with P -values less than 0.05. Among the various comorbidities, hypertension (HTN) and diabetes mellitus (DM) were the most prevalent, with 12% and 11.5% of patients having these conditions, respectively. The highest fatality rates were observed among patients with cardiovascular disease (95.5%), chronic kidney disease (62.5%), gastrointestinal (GI) (93.3%), and liver diseases (73.3%). Conversely, patients with neurology comorbidities had the lowest fatality rate (0%). These results highlight the significant role of comorbidities in COVID-19 outcomes and emphasize the need for special attention to be paid to patients with pre-existing health conditions.

The treatment characteristics of the COVID-19 patients and the resulting outcomes are shown in Table  4 . The table shows the frequency of patients who received different types of medications or therapies during their treatment. According to the results, the use of antibiotics (35.1%), remdesivir (29.6%), favipiravir (36.0%), and Vitamin zinc (33.5%) was significantly associated with a lower mortality rate ( P  < 0.001), suggesting that these medications may have a positive impact on patient outcomes. On the other hand, the use of Heparin (66.1%), Insulin (82.6%), Antifungal (89.6%), ACE inhibitors (78.1%), and Angiotensin II Receptor Blockers (ARB) (83.8%) was significantly associated with increased mortality ( P  < 0.001), suggesting that these medications may have a negative effect on the patient's outcome. Also, It seems that taking hydroxychloroquine (51.0%) is associated with a worse outcome at lower significance ( P  = 0.022). The use of Atrovent, Corticosteroids and Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) did not show a significant association with survival or mortality rates. Similarly, the use of Intravenous Immunoglobulin (IVIg), Vitamin C, Vitamin D, and Diuretic did not show a significant association with the patient’s outcome.

Initial vital signs

Table 5 provides initial vital sign characteristics of COVID-19 patients, including heart rate, respiratory rate, temperature, blood pressure, oxygen therapy, and radiography test result. The findings shows that deceased patients had higher HR (83.03 bpm vs. 76.14 bpm, P  < 0.001), lower RR (11.40 bpm vs. 16.25 bpm, P  < 0.001), higher temperature (37.43 °C vs. 36.91 °C, P  < 0.001), higher SBP (128.16 mmHg vs. 123.33 mmHg, P  < 0.001), and higher O 2 requirements (invasive: 75.0% vs. 25.0%, P  < 0.001) compared to the survived patients. Additionally, deceased patients had higher MAP (99.35 mmHg vs. 96.08 mmHg, P  = 0.005), and lower SPO 2 percentage (81.29% vs. 91.95%, P  < 0.001) compared to the survived patients. Furthermore, deceased patients had higher PEEP levels (5.83 cmH2O vs. 0.69 cmH2O, P  < 0.001), higher FiO2 levels (51.43% vs. 8.97%, P  < 0.001), and more frequent bilateral pneumonia (63.0% vs. 37.0%, P  < 0.001) compared to the survived patients. There appears to be no relationship between diastolic blood pressure and treatment outcome (83.44 mmHg vs. 85.61 mmHg).

Table 6 provides information on the symptoms of patients infected with COVID-19 by survival outcome. The table also shows the frequency of symptoms among patients. The most common symptom reported by patients was fever, which occurred in 67.0% of surviving and deceased patients. Dyspnea and nonproductive cough were the second and third most common symptoms, reported by 40.4% and 29.3% of the total sample, respectively. Other common symptoms listed in the Table were malodor (28.7%), dyspepsia (28.4%), and myalgia (25.6%).

The P -values reported in the table show that some symptoms are significantly associated with death, including productive cough, dyspnea, sore throat, headache, delirium, olfactory symptoms, dyspepsia, nausea, vomiting, sepsis, respiratory failure, heart failure, MODS, coagulopathy, secondary infection, stroke, acidosis, and admission to the intensive care unit. Surviving and deceased patients also differed significantly in the average number of days spent in the ICU. There was no significant association between patient outcomes and symptoms such as nonproductive cough, chills, diarrhea, chest pain, and hyperglycemia.

Laboratory tests

Table 7 shows the laboratory values of COVID-19 patients with the average values of the different laboratory results. The results show that the deceased patients had significantly lower levels of red blood cells (3.78 × 106/µL vs. 5.01 × 106/µL), hemoglobin (11.22 g/dL vs. 14.10 g/dL), and hematocrit (34.10% vs. 42.46%), whereas basophils and white blood cells did not differ significantly between the two groups. The percentage of neutrophils (65.59% vs. 62.58%) and monocytes (4.34% vs. 3.93%) was significantly higher in deceased patients, while the percentage of lymphocytes and eosinophils did not differ significantly between the two groups. In addition, deceased patients had higher levels of certain biomarkers, including D-dimer (1.347 mgFEU/L vs. 0.155 mgFEU/L), lactate dehydrogenase (174.61 U/L vs. 128.48 U/L), aspartate aminotransferase (93.09 U/L vs. 39.63 U/L), alanine aminotransferase (74.48 U/L vs. 28.70 U/L), alkaline phosphatase (119.51 IU/L vs. 81.34 IU/L), creatine phosphokinase-MB (4.65 IU/L vs. 3.33 IU/L), and positive troponin I (56.5% vs. 43.5%). The proportion of patients with positive C-reactive protein was also higher in the deceased group.

Other laboratory values with statistically significant differences between the two groups ( P  < 0.001) were INR, ESR, BUN, Cr, Na, K, P, PLT, TSH, T3, and T4. The surviving patients generally had lower values in these laboratory characteristics than the deceased patients.

Model performance and evaluation

Five ML algorithms, namely DT, XGBoost, SVM, NB, and RF, were used in this study to build mortality prediction models COVID -19. The models were based on the optimal feature set selected in a previous step and were trained on the same data set. The effectiveness of the models was evaluated by calculating sensitivity, specificity, accuracy, F1 score, and AUC metrics. Table 8 shows the results of this performance evaluation. The average values are expressed from the test set as the mean (standard deviation).

The results show that the performance of the models varies widely in the different feature categories. The Laboratory Tests category achieved the highest performance, with all models scoring 100% in all metrics. The Symptoms and initial Vital Signs categories also show high performance, with XGBoost achieving the highest accuracy of 98.03% and DT achieving the highest sensitivity of 92.79%.

The Clinical and Conditions category also showed high performance, with all models showing accuracy above 91%. XGBoost achieved the highest sensitivity and specificity of 92.74% and 92.96%, respectively. In contrast, the Demographics category showed the lowest performance, with all models achieving less than 66.5% accuracy.

In summary, the results suggest that certain feature categories may be more useful than others in predicting mortality from COVID-19 and that some ML models may perform better than others depending on the feature category used.

Feature importance

SHapley Additive exPlanations (SHAP) values indicate the importance or contribution of each feature in predicting model output. These values help to understand the influence and importance of each feature on the model's decision-making process.

In Fig.  2 , the mean absolute SHAP values are shown to depict global feature importance. Figure  2 shows the contribution of each feature within its respective group as calculated by the XGBoost prediction model using SHAP. According to the SHAP method, the features that had the greatest impact on predicting COVID-19 mortality were, in descending order: D-dimer, CPR, PEEP, underlying disease, ESR, antifungal treatment, PaO2, age, dyspnea, and nausea.

Feature importance based on SHAP-values. The mean absolute SHAP values are depicted, to illustrate global feature importance. The SHAP values change in the spectrum from dark (higher) to light (lower) color

On the other hand, Fig.  3 presents the local explanation summary that indicates the direction of the relationship between a variable and COVID-19 outcome. As shown in Fig.  3 (I to VII), older age and very low BMI were the two demographic factors with the greatest impact on model outcome, followed by clinical factors such as higher CPR, hospitalization, and hyperlipidemia. Higher mortality rates were associated with patients who smoked and had traveled in the past 14 days. Patients with underlying diseases, especially HTN, died more frequently. In contrast, the use of remdesivir, Vit Zn, and favipiravir is associated with lower mortality. Initial vital signs such as high PEEP, low PaO2 and RR had the greatest impact, as did symptoms such as dyspnea, MODS, sore throat and LOC. A higher risk of mortality is observed in patients with higher D-dimer levels and ESR as the most consequential laboratory tests, followed by K, AST and CPK-MB.

The SHAP-based feature importance of all categories (I to VII) for COVID‑19 mortality prediction, calculated with the XGBoost model. The local explanatory summary shows the direction of the relationship between a feature and patient outcome. Positive SHAP values indicate death, whereas negative SHAP values indicate survival. As the color scale shows, higher values are blue while lower values are orenge

Using the feature types listed in Appendix Table  1 , Fig.  4 shows that the performance of ML algorithms can be improved by increasing the number of features used in training, especially in distinguishing between symptoms, comorbidities, and treatments. In addition, the amount and quality of data used for training can significantly affect algorithm performance, with laboratory tests being more informative than initial vital signs. Regarding the influence of features, quantitative features tend to have a more positive effect on performance than qualitative features; clinical conditions tend to be more informative than demographic data. Thus, both the amount of data and the type of features used have a significant impact on the performance of ML algorithms.

Association between feature sets and performance of machine learning algorithms in predicting COVID-19’s mortality

The COVID-19 pandemic has presented unprecedented public health challenges worldwide and requires a deep understanding of the factors contributing to COVID-19 mortality to enable effective management and intervention. This study used machine learning analysis to uncover the predictive power of an extensive dataset that includes wide range of personal, clinical, preclinical, and laboratory variables associated with COVID-19 mortality.

This study confirms previous research on COVID-19 outcomes that highlighted age as a significant predictor of mortality [ 45 , 46 , 47 ], along with comorbidities such as hypertension and diabetes [ 48 , 49 ]. Underlying conditions such as cardiovascular and renal disease also contribute to mortality risk [ 50 , 51 ].

Regarding treatment, antibiotics, remdesivir, favipiravir, and vitamin zinc are associated with lower mortality [ 52 , 53 ], whereas heparin, insulin, antifungals, ACE, and ARBs are associated with higher mortality [ 54 ]. This underscores the importance of drug choice in COVID -19 treatment.

Initial vital signs such as heart rate, respiratory rate, temperature, and oxygen therapy differ between surviving and deceased patients [ 55 ]. Deceased patients often have increased heart rate, lower respiratory rate, higher temperature, and increased oxygen requirements, which can serve as early indicators of disease severity.

Symptoms such as productive cough, dyspnea, and delirium are significantly associated with COVID-19 mortality, emphasizing the need for immediate monitoring and intervention [ 56 ]. Laboratory tests show altered hematologic and biochemical markers in deceased patients, underscoring the importance of routine laboratory monitoring in COVID-19 patients [ 57 , 58 ].

The ML algorithms were used in the study to predict mortality COVID-19 based on these multilayered variables. XGBoost and Random Forest performed better than other algorithms and had high recall, specificity, accuracy, F1 score, and AUC. This highlights the potential of ML, particularly the XGBoost algorithm, in improving prediction accuracy for COVID-19 mortality [ 59 ]. The study also highlighted the importance of drug choice in treatment and the potential of ML algorithms, particularly XGBoost, in improving prediction accuracy. However, the study's findings differ from those of Moulaei [ 60 ], Nopour [ 61 ], and Mehraeen [ 62 ] in terms of the best-performing ML algorithm and the most influential variables. While Moulaei [ 60 ] found that the random forest algorithm had the best performance, Nopour [ 61 ] and Ikemura [ 63 ] identified the artificial neural network and stacked ensemble models, respectively, as the most effective. Additionally, the most influential variables in predicting mortality varied across the studies, with Moulaei [ 60 ] highlighting dyspnea, ICU admission, and oxygen therapy, and Ikemura [ 63 ] identifying systolic and diastolic blood pressure, age, and other biomarkers. These differences may be attributed to variations in the datasets, feature selection, and model training.

However, it is important to note that the choice of algorithm should be tailored to the specific dataset and research question. In addition, the results suggest that a comprehensive approach that incorporates different feature categories may lead to more accurate prediction of COVID-19 mortality. In general, the results suggest that the performance of ML models is influenced by the number and type of features in each category. While some models consistently perform well across different categories (e.g., XGBoost), others perform better for specific types of features (e.g., SVM for Demographics).

Analysis of the importance of characteristics using SHAP values revealed critical factors affecting model results. D-dimer values, CPR, PEEP, underlying diseases, and ESR emerged as the most important features, highlighting the importance of these variables in predicting COVID-19 mortality. These results provide valuable insights into the underlying mechanisms and risk factors associated with severe COVID-19 outcomes.

The types of features used in ML models fall into two broad categories: quantitative (numerical) and qualitative (binary or categorical). The performance of ML methods can vary depending on the type of features used. Some algorithms work better with quantitative features, while others work better with qualitative features. For example, decision trees and random forests work well with both types of features [ 64 ], while neural networks often work better with quantitative features [ 65 , 66 ]. Accordingly, we consider these levels for the features under study to better assess the impact of the data.

The success of ML algorithms depends largely on the quality and quantity of the data on which they are trained [ 67 , 68 , 69 ]. Recent research, including the 2021 study by Sarker IH. [ 26 ], has shown that a larger amount of data can significantly improve the performance of deep learning algorithms compared to traditional machine learning techniques. However, it should be noted that the effect of data size on model performance depends on several factors, such as data characteristics and experimental design. This underscores the importance of carefully and judiciously selecting data for training.

Limitations

One of the limitations of this study is that it relies on data collected from a single hospital in Abadan, Iran. The data may not be representative of the diversity of COVID -19 cases in different regions, and there may be differences in data quality and completeness. In addition, retrospectively collected data may have biases and inaccuracies. Although the study included a substantial number of COVID -19 patients, the sample size may still limit the generalizability of the results, especially for less common subgroups or certain demographic characteristics.

Future works

Future studies could adopt a multi-center approach to improve the scope and depth of research on COVID-19 outcomes. This could include working with multiple hospitals in different regions of Iran to ensure a more diverse and representative sample. By conducting prospective studies, researchers can collect data in real time, which reduces the biases associated with retrospective data collection and increases the reliability of the results. Increasing sample size, conducting longitudinal studies to track patient progression, and implementing quality assurance measures are critical to improving generalizability, understanding long-term effects, and ensuring data accuracy in future research efforts. Collectively, these strategies aim to address the limitations of individual studies and make an important contribution to a more comprehensive understanding of COVID-19 outcomes in different populations and settings.

Conclusions

In summary, this study demonstrates the potential of ML algorithms in predicting COVID-19 mortality based on a comprehensive set of features. In addition, the interpretability of the models using SHAP-based feature importance, which revealed the variables strongly correlated with mortality. This study highlights the power of data-driven approaches in addressing critical public health challenges such as the COVID-19 pandemic. The results suggest that the performance of ML models is influenced by the number and type of features in each feature set. These findings may be a valuable resource for health professionals to identify high-risk patients COVID-19 and allocate resources effectively.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

World Health Organization

Middle east respiratory syndrome

Severe acute respiratory syndrome

Reverse transcription polymerase chain reaction

Propensity score matching

Synthetic minority over-sampling technique

Missing completely at random

Decision tree

EXtreme gradient boosting

Support vector machine

Naïve bayes

Random forest

Cross-validation

True positive

True negative

False positive

False negative

• Machine learning

Artificial Intelligence

Shapely additive explanation

Cardiopulmonary Resuscitation

Hypertension

Diabetes mellitus

Cardiovascular disease

Chronic Kidney disease

Chronic obstructive pulmonary disease

Human immunodeficiency virus

Hepatitis B virus

Such as influenza, pneumonia, asthma, bronchitis, and chronic obstructive airways disease

Gastrointestinal

Such as epilepsy, learning disabilities, neuromuscular disorders, autism, ADD, brain tumors, and cerebral palsy

Such as fatty liver disease and cirrhosis

Blood disease

Skin diseases

Mental disorders

Intravenous immunoglobulin

Non-steroidal anti-Inflammatory drugs

Angiotensin converting enzyme inhibitors

Angiotensin II receptor blockers

Beats per minute

Respiratory rate

Temperatures

Systolic blood pressure

Diastolic blood pressure

Mean arterial pressure

Oxygen saturation

Partial pressure of oxygen in the alveoli

Positive end-expiratory pressure

Fraction of inspired oxygen

Radiography (X-ray) test result

Smell disorders

Indigestion

Level of consciousness

Multiple organ dysfunction syndrome

Coughing up blood; Coagulopathy: bleeding disorder

High blood glucose

Intensive care unit

Red blood cell

White blood cell

Low-density lipoprotein

High-density lipoprotein

Prothrombin time

Partial thromboplastin time

International normalized ratio

Erythrocyte sedimentation rate

C-reactive-protein

Lactate dehydrogenase

Aspartate aminotransferase

Alanine aminotransferase

Alkaline phosphatase

Creatine phosphokinase-MB

Blood urea nitrogen

Thyroid stimulating hormone

Triiodothyronine

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Acknowledgements

We thank the Research Deputy of the Abadan University of Medical Sciences for financially supporting this project.

Summary points

∙ How can datasets improve mortality prediction using ML models for COVID-19 patients?

∙ In order, quantity and quality variables have more effect on the model performances.

∙ Intelligent techniques such as SHAP analysis can be used to improve the interpretability of features in ML algorithms.

∙ Well-structured data are critical to help health professionals identify at-risk patients and improve pandemic outcomes.

This research was supported by grant No. 1456 from the Abadan University of Medical Sciences. However, the funding source did not influence the study design, data collection, analysis and interpretation, report writing, or decision to publish the article.

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Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

Maryam Seyedtabib

Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran

Roya Najafi-Vosough

Department of Biostatistics and Epidemiology, School of Health, Abadan University of Medical Sciences, Abadan, Iran

Naser Kamyari

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MS: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing–original draft, writing—review & editing, Visualization, Project administration. RNV: Conceptualization, Data curation, Formal analysis, Investigation, Writing–original draft, writing—review & editing. NK: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing–original draft, writing—review & editing, Visualization, Supervision.

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Correspondence to Naser Kamyari .

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This study was approved by the Research Ethics Committee (REC) of Abadan University of Medical Sciences under the ID number IR.ABADANUMS.REC.1401.095. Methods used complied with all relevant ethical guidelines and regulations. The Ethics Committee of Abadan University of Medical Sciences waived the requirement for written informed consent from study participants.

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Seyedtabib, M., Najafi-Vosough, R. & Kamyari, N. The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case–control study. BMC Infect Dis 24 , 411 (2024). https://doi.org/10.1186/s12879-024-09298-w

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Received : 22 December 2023

Accepted : 05 April 2024

Published : 18 April 2024

DOI : https://doi.org/10.1186/s12879-024-09298-w

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BMC Infectious Diseases

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NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Case study: 33-year-old female presents with chronic sob and cough.

Sandeep Sharma ; Muhammad F. Hashmi ; Deepa Rawat .

Affiliations

Last Update: February 20, 2023 .

• Case Presentation

History of Present Illness:  A 33-year-old white female presents after admission to the general medical/surgical hospital ward with a chief complaint of shortness of breath on exertion. She reports that she was seen for similar symptoms previously at her primary care physician’s office six months ago. At that time, she was diagnosed with acute bronchitis and treated with bronchodilators, empiric antibiotics, and a short course oral steroid taper. This management did not improve her symptoms, and she has gradually worsened over six months. She reports a 20-pound (9 kg) intentional weight loss over the past year. She denies camping, spelunking, or hunting activities. She denies any sick contacts. A brief review of systems is negative for fever, night sweats, palpitations, chest pain, nausea, vomiting, diarrhea, constipation, abdominal pain, neural sensation changes, muscular changes, and increased bruising or bleeding. She admits a cough, shortness of breath, and shortness of breath on exertion.

Social History: Her tobacco use is 33 pack-years; however, she quit smoking shortly prior to the onset of symptoms, six months ago. She denies alcohol and illicit drug use. She is in a married, monogamous relationship and has three children aged 15 months to 5 years. She is employed in a cookie bakery. She has two pet doves. She traveled to Mexico for a one-week vacation one year ago.

Allergies:  No known medicine, food, or environmental allergies.

Past Medical History: Hypertension

Past Surgical History: Cholecystectomy

Medications: Lisinopril 10 mg by mouth every day

Physical Exam:

Vitals: Temperature, 97.8 F; heart rate 88; respiratory rate, 22; blood pressure 130/86; body mass index, 28

General: She is well appearing but anxious, a pleasant female lying on a hospital stretcher. She is conversing freely, with respiratory distress causing her to stop mid-sentence.

Respiratory: She has diffuse rales and mild wheezing; tachypneic.

Cardiovascular: She has a regular rate and rhythm with no murmurs, rubs, or gallops.

Gastrointestinal: Bowel sounds X4. No bruits or pulsatile mass.

• Initial Evaluation

Laboratory Studies:  Initial work-up from the emergency department revealed pancytopenia with a platelet count of 74,000 per mm3; hemoglobin, 8.3 g per and mild transaminase elevation, AST 90 and ALT 112. Blood cultures were drawn and currently negative for bacterial growth or Gram staining.

Chest X-ray

Impression:  Mild interstitial pneumonitis

• Differential Diagnosis
• Aspiration pneumonitis and pneumonia
• Bacterial pneumonia
• Immunodeficiency state and Pneumocystis jiroveci pneumonia
• Carcinoid lung tumors
• Tuberculosis
• Viral pneumonia
• Chlamydial pneumonia
• Coccidioidomycosis and valley fever
• Recurrent Legionella pneumonia
• Mediastinal cysts
• Mediastinal lymphoma
• Recurrent mycoplasma infection
• Pancoast syndrome
• Pneumococcal infection
• Sarcoidosis
• Small cell lung cancer
• Aspergillosis
• Blastomycosis
• Histoplasmosis
• Actinomycosis
• Confirmatory Evaluation

CT of the chest was performed to further the pulmonary diagnosis; it showed a diffuse centrilobular micronodular pattern without focal consolidation.

On finding pulmonary consolidation on the CT of the chest, a pulmonary consultation was obtained. Further history was taken, which revealed that she has two pet doves. As this was her third day of broad-spectrum antibiotics for a bacterial infection and she was not getting better, it was decided to perform diagnostic bronchoscopy of the lungs with bronchoalveolar lavage to look for any atypical or rare infections and to rule out malignancy (Image 1).

Bronchoalveolar lavage returned with a fluid that was cloudy and muddy in appearance. There was no bleeding. Cytology showed Histoplasma capsulatum .

Based on the bronchoscopic findings, a diagnosis of acute pulmonary histoplasmosis in an immunocompetent patient was made.

Pulmonary histoplasmosis in asymptomatic patients is self-resolving and requires no treatment. However, once symptoms develop, such as in our above patient, a decision to treat needs to be made. In mild, tolerable cases, no treatment other than close monitoring is necessary. However, once symptoms progress to moderate or severe, or if they are prolonged for greater than four weeks, treatment with itraconazole is indicated. The anticipated duration is 6 to 12 weeks total. The response should be monitored with a chest x-ray. Furthermore, observation for recurrence is necessary for several years following the diagnosis. If the illness is determined to be severe or does not respond to itraconazole, amphotericin B should be initiated for a minimum of 2 weeks, but up to 1 year. Cotreatment with methylprednisolone is indicated to improve pulmonary compliance and reduce inflammation, thus improving work of respiration. [1] [2] [3]

Histoplasmosis, also known as Darling disease, Ohio valley disease, reticuloendotheliosis, caver's disease, and spelunker's lung, is a disease caused by the dimorphic fungi  Histoplasma capsulatum native to the Ohio, Missouri, and Mississippi River valleys of the United States. The two phases of Histoplasma are the mycelial phase and the yeast phase.

Etiology/Pathophysiology 

Histoplasmosis is caused by inhaling the microconidia of  Histoplasma  spp. fungus into the lungs. The mycelial phase is present at ambient temperature in the environment, and upon exposure to 37 C, such as in a host’s lungs, it changes into budding yeast cells. This transition is an important determinant in the establishment of infection. Inhalation from soil is a major route of transmission leading to infection. Human-to-human transmission has not been reported. Infected individuals may harbor many yeast-forming colonies chronically, which remain viable for years after initial inoculation. The finding that individuals who have moved or traveled from endemic to non-endemic areas may exhibit a reactivated infection after many months to years supports this long-term viability. However, the precise mechanism of reactivation in chronic carriers remains unknown.

Infection ranges from an asymptomatic illness to a life-threatening disease, depending on the host’s immunological status, fungal inoculum size, and other factors. Histoplasma  spp. have grown particularly well in organic matter enriched with bird or bat excrement, leading to the association that spelunking in bat-feces-rich caves increases the risk of infection. Likewise, ownership of pet birds increases the rate of inoculation. In our case, the patient did travel outside of Nebraska within the last year and owned two birds; these are her primary increased risk factors. [4]

Non-immunocompromised patients present with a self-limited respiratory infection. However, the infection in immunocompromised hosts disseminated histoplasmosis progresses very aggressively. Within a few days, histoplasmosis can reach a fatality rate of 100% if not treated aggressively and appropriately. Pulmonary histoplasmosis may progress to a systemic infection. Like its pulmonary counterpart, the disseminated infection is related to exposure to soil containing infectious yeast. The disseminated disease progresses more slowly in immunocompetent hosts compared to immunocompromised hosts. However, if the infection is not treated, fatality rates are similar. The pathophysiology for disseminated disease is that once inhaled, Histoplasma yeast are ingested by macrophages. The macrophages travel into the lymphatic system where the disease, if not contained, spreads to different organs in a linear fashion following the lymphatic system and ultimately into the systemic circulation. Once this occurs, a full spectrum of disease is possible. Inside the macrophage, this fungus is contained in a phagosome. It requires thiamine for continued development and growth and will consume systemic thiamine. In immunocompetent hosts, strong cellular immunity, including macrophages, epithelial, and lymphocytes, surround the yeast buds to keep infection localized. Eventually, it will become calcified as granulomatous tissue. In immunocompromised hosts, the organisms disseminate to the reticuloendothelial system, leading to progressive disseminated histoplasmosis. [5] [6]

Symptoms of infection typically begin to show within three to17 days. Immunocompetent individuals often have clinically silent manifestations with no apparent ill effects. The acute phase of infection presents as nonspecific respiratory symptoms, including cough and flu. A chest x-ray is read as normal in 40% to 70% of cases. Chronic infection can resemble tuberculosis with granulomatous changes or cavitation. The disseminated illness can lead to hepatosplenomegaly, adrenal enlargement, and lymphadenopathy. The infected sites usually calcify as they heal. Histoplasmosis is one of the most common causes of mediastinitis. Presentation of the disease may vary as any other organ in the body may be affected by the disseminated infection. [7]

The clinical presentation of the disease has a wide-spectrum presentation which makes diagnosis difficult. The mild pulmonary illness may appear as a flu-like illness. The severe form includes chronic pulmonary manifestation, which may occur in the presence of underlying lung disease. The disseminated form is characterized by the spread of the organism to extrapulmonary sites with proportional findings on imaging or laboratory studies. The Gold standard for establishing the diagnosis of histoplasmosis is through culturing the organism. However, diagnosis can be established by histological analysis of samples containing the organism taken from infected organs. It can be diagnosed by antigen detection in blood or urine, PCR, or enzyme-linked immunosorbent assay. The diagnosis also can be made by testing for antibodies again the fungus. [8]

Pulmonary histoplasmosis in asymptomatic patients is self-resolving and requires no treatment. However, once symptoms develop, such as in our above patient, a decision to treat needs to be made. In mild, tolerable cases, no treatment other than close monitoring is necessary. However, once symptoms progress to moderate or severe or if they are prolonged for greater than four weeks, treatment with itraconazole is indicated. The anticipated duration is 6 to 12 weeks. The patient's response should be monitored with a chest x-ray. Furthermore, observation for recurrence is necessary for several years following the diagnosis. If the illness is determined to be severe or does not respond to itraconazole, amphotericin B should be initiated for a minimum of 2 weeks, but up to 1 year. Cotreatment with methylprednisolone is indicated to improve pulmonary compliance and reduce inflammation, thus improving the work of respiration.

The disseminated disease requires similar systemic antifungal therapy to pulmonary infection. Additionally, procedural intervention may be necessary, depending on the site of dissemination, to include thoracentesis, pericardiocentesis, or abdominocentesis. Ocular involvement requires steroid treatment additions and necessitates ophthalmology consultation. In pericarditis patients, antifungals are contraindicated because the subsequent inflammatory reaction from therapy would worsen pericarditis.

Patients may necessitate intensive care unit placement dependent on their respiratory status, as they may pose a risk for rapid decompensation. Should this occur, respiratory support is necessary, including non-invasive BiPAP or invasive mechanical intubation. Surgical interventions are rarely warranted; however, bronchoscopy is useful as both a diagnostic measure to collect sputum samples from the lung and therapeutic to clear excess secretions from the alveoli. Patients are at risk for developing a coexistent bacterial infection, and appropriate antibiotics should be considered after 2 to 4 months of known infection if symptoms are still present. [9]

Prognosis 

If not treated appropriately and in a timely fashion, the disease can be fatal, and complications will arise, such as recurrent pneumonia leading to respiratory failure, superior vena cava syndrome, fibrosing mediastinitis, pulmonary vessel obstruction leading to pulmonary hypertension and right-sided heart failure, and progressive fibrosis of lymph nodes. Acute pulmonary histoplasmosis usually has a good outcome on symptomatic therapy alone, with 90% of patients being asymptomatic. Disseminated histoplasmosis, if untreated, results in death within 2 to 24 months. Overall, there is a relapse rate of 50% in acute disseminated histoplasmosis. In chronic treatment, however, this relapse rate decreases to 10% to 20%. Death is imminent without treatment.

• Pearls of Wisdom

While illnesses such as pneumonia are more prevalent, it is important to keep in mind that more rare diseases are always possible. Keeping in mind that every infiltrates on a chest X-ray or chest CT is not guaranteed to be simple pneumonia. Key information to remember is that if the patient is not improving under optimal therapy for a condition, the working diagnosis is either wrong or the treatment modality chosen by the physician is wrong and should be adjusted. When this occurs, it is essential to collect a more detailed history and refer the patient for appropriate consultation with a pulmonologist or infectious disease specialist. Doing so, in this case, yielded workup with bronchoalveolar lavage and microscopic evaluation. Microscopy is invaluable for definitively diagnosing a pulmonary consolidation as exemplified here where the results showed small, budding, intracellular yeast in tissue sized 2 to 5 microns that were readily apparent on hematoxylin and eosin staining and minimal, normal flora bacterial growth. 

• Enhancing Healthcare Team Outcomes

This case demonstrates how all interprofessional healthcare team members need to be involved in arriving at a correct diagnosis. Clinicians, specialists, nurses, pharmacists, laboratory technicians all bear responsibility for carrying out the duties pertaining to their particular discipline and sharing any findings with all team members. An incorrect diagnosis will almost inevitably lead to incorrect treatment, so coordinated activity, open communication, and empowerment to voice concerns are all part of the dynamic that needs to drive such cases so patients will attain the best possible outcomes.

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Histoplasma Contributed by Sandeep Sharma, MD

Disclosure: Sandeep Sharma declares no relevant financial relationships with ineligible companies.

Disclosure: Muhammad Hashmi declares no relevant financial relationships with ineligible companies.

Disclosure: Deepa Rawat declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Sharma S, Hashmi MF, Rawat D. Case Study: 33-Year-Old Female Presents with Chronic SOB and Cough. [Updated 2023 Feb 20]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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