medical research dyspnea scale

MRC Dyspnoea Scale

The mMRC (Modified Medical Research Council) Dyspnoea Scale is used to assess the degree of baseline functional disability due to dyspnoea.

It is useful in characterising baseline dyspnoea in patients with respiratory disease such as COPD. Whilst it moderately correlates with other healthcare-associated morbidity, mortality and quality of life scales (particularly in COPD) the scores are only variably associated with patients' perceptions of respiratory symptom burden. It is used as a component of the BODE Index, which predicts adverse outcomes, including mortality and risk of hospitalisation. The scale is easy and efficient to use.

The mMRC breathlessness scale ranges from grade 0 to 4. It is very similar to the original version and is now widely used in studies. It should be noted that the MRC clearly states on its website that it is unable to give permission for use of any modified version of the scale (including therefore, the mMRC scale). Use of the MRC questionnaire is free but should be acknowledged.

The modified MRC was developed by D A Mahler, see  https://pubmed.ncbi.nlm.nih.gov/3342669/

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Measuring Shortness of Breath (Dyspnea) in COPD

How the Perception of Disability Directs Treatment

Dyspnea is the medical term used to describe shortness of breath, a symptom considered central to all forms of chronic obstructive pulmonary disease (COPD) including emphysema and chronic bronchitis.

As COPD is both a progressive and non-reversible, the severity of dyspnea plays a key role in determining both the stage of the disease and the appropriate medical treatment.

Challenges in Diagnosis

From a clinical standpoint, the challenge of diagnosing dyspnea is that it is very subjective. While spirometry tests (which measures lung capacity) and pulse oximetry (which measures oxygen levels in the blood) may show that two people have the same level of breathing impairment, one may feel completely winded after activity while the other may be just fine.

Ultimately, a person's perception of dyspnea is important as it helps ensure the person is neither undertreated nor overtreated and that the prescribed therapy, when needed, will improve the person's quality of life rather than take from it.  

To this end, pulmonologists will use a tool called the modified Medical Research Council (mMRC) dyspnea scale to establish how much an individual's shortness of breath causes real-world disability.

How the Assessment Is Performed

The process of measuring dyspnea is similar to tests used to measure pain perception in persons with chronic pain. Rather than defining dyspnea in terms of lung capacity, the mMRC scale will rate the sensation of dyspnea as the person perceives it.

The severity of dyspnea is rated on a scale of 0 to 4, the value of which will direct both the diagnosis and treatment plan.

Role of the MMRC Dyspnea Scale

The mMRC dyspnea scale has proven valuable in the field of pulmonology as it affords doctors and researchers the mean to:

• Assess the effectiveness of treatment on an individual basis
• Compare the effectiveness of a treatment within a population
• Predict survival times and rates

From a clinical viewpoint, the mMRC scale correlates fairly well to such objective measures as pulmonary function tests and walk tests . Moreover, the values tend to be stable over time, meaning that they are far less prone to subjective variability that one might assume.  

Using the BODE Index to Predict Survival

The mMRC dyspnea scale is used to calculate the BODE index , a tool which helps estimate the survival times of people living with COPD.

The BODE Index is comprised of a person's body mass index ("B"), airway obstruction ("O"), dyspnea ("D"), and exercise tolerance ("E"). Each of these components is graded on a scale of either 0 to 1 or 0 to 3, the numbers of which are then tabulated for a final value.

The final value—ranging from as low as 0 to as high as 10—provides doctors a percentage of how likely a person is to survive for four years. The final BODE tabulation is described as follows:

• 0 to 2 points: 80 percent likelihood of survival
• 3 to 4 points: 67 percent likelihood of survival
• 5 of 6 points: 57 percent likelihood of survival
• 7 to 10 points: 18 percent likelihood of survival

The BODE values, whether large or small, are not set in stone. Changes to lifestyle and improved treatment adherence can improve long-term outcomes, sometimes dramatically. These include things like quitting smoking , improving your diet  and engaging in appropriate exercise to improve your respiratory capacity.

In the end, the numbers are simply a snapshot of current health, not a prediction of your mortality. Ultimately, the lifestyle choices you make can play a significant role in determining whether the odds are against you or in your favor.

Janssens T, De peuter S, Stans L, et al. Dyspnea perception in COPD: association between anxiety, dyspnea-related fear, and dyspnea in a pulmonary rehabilitation program . Chest. 2011;140(3):618-625. doi:10.1378/chest.10-3257

Manali ED, Lyberopoulos P, Triantafillidou C, et al. MRC chronic Dyspnea Scale: Relationships with cardiopulmonary exercise testing and 6-minute walk test in idiopathic pulmonary fibrosis patients: a prospective study . BMC Pulm Med . 2010;10:32. doi:10.1186/1471-2466-10-32

Esteban C, Quintana JM, Moraza J, et al. BODE-Index vs HADO-score in chronic obstructive pulmonary disease: Which one to use in general practice? . BMC Med . 2010;8:28. doi:10.1186/1741-7015-8-28

Chhabra, S., Gupta, A., and Khuma, M. " Evaluation of Three Scales of Dyspnea in Chronic Obstructive Pulmonary Disease. " Annals of Thoracic Medicine. 2009; 4(3):128-32. DOI: 10.4103/1817-1737.53351 .

Perez, T.; Burgel, P.; Paillasseur, J.; et al. " Modified Medical Research Council scale vs Baseline Dyspnea Index to Evaluate Dyspnea in Chronic Obstructive Pulmonary Disease. " International Journal of Chronic Obstructive Pulmonary Disease . 2015; 10:1663-72. DOI: 10.2147/COPD.S82408 .

By Deborah Leader, RN  Deborah Leader RN, PHN, is a registered nurse and medical writer who focuses on COPD.

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• Volume 54, Issue 7
• Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease
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• J C Bestall b ,
• E A Paul a ,
• R Garrod a ,
• R Garnham a ,
• P W Jones b ,
• J A Wedzicha a
• a Academic Department of Respiratory Medicine, St Bartholomew’s and Royal London School of Medicine and Dentistry, London Chest Hospital, London, UK, b Division of Physiological Medicine, St George’s Hospital Medical School, London SW17 0RE, UK
• Professor P Jones.

BACKGROUND Methods of classifying chronic obstructive pulmonary disease (COPD) depend largely upon spirometric measurements but disability is only weakly related to measurements of lung function. With the increased use of pulmonary rehabilitation, a need has been identified for a simple and standardised method of categorising disability in COPD. This study examined the validity of the Medical Research Council (MRC) dyspnoea scale for this purpose.

METHODS One hundred patients with COPD were recruited from an outpatient pulmonary rehabilitation programme. Assessments included the MRC dyspnoea scale, spirometric tests, blood gas tensions, a shuttle walking test, and Borg scores for perceived breathlessness before and after exercise. Health status was assessed using the St George’s Respiratory Questionnaire (SGRQ) and Chronic Respiratory Questionnaire (CRQ). The Nottingham Extended Activities of Daily Living (EADL) score and Hospital Anxiety and Depression (HAD) score were also measured.

RESULTS Of the patients studied, 32 were classified as having MRC grade 3 dyspnoea, 34 MRC grade 4 dyspnoea, and 34 MRC grade 5 dyspnoea. Patients with MRC grades 1 and 2 dyspnoea were not included in the study. There was a significant association between MRC grade and shuttle distance, SGRQ and CRQ scores, mood state and EADL. Forced expiratory volume in one second (FEV 1 ) was not associated with MRC grade. Multiple logistic regression showed that the determinants of disability appeared to vary with the level of disability. Between MRC grades 3 and 4 the significant covariates were exercise performance, SGRQ and depression score, whilst between grades 4 and 5 exercise performance and age were the major determinants.

CONCLUSIONS The MRC dyspnoea scale is a simple and valid method of categorising patients with COPD in terms of their disability that could be used to complement FEV 1 in the classification of COPD severity.

• MRC dyspnoea scale
• chronic obstructive pulmonary disease

https://doi.org/10.1136/thx.54.7.581

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Dyspnea MRC Scale

Evaluates the severity of dyspnea in patients who suffer from chronic obstructive pulmonary disease.

In the text below the calculator you can find more information about the two versions of the scale and about dyspnea signs in COPD.

The dyspnea MRC scale evaluates how dyspnea affects patients with chronic obstructive pulmonary disease and provides a severity grade.

The scale can be used alongside the BODE index to evaluate the prognosis of COPD patients.

The five clinical grades of dyspnea (breathlessness attributed to low fitness or COPD) are determined based on the individual’s respiratory reaction to different physical daily activities.

The MRC scale was created by Fletcher in 1952 and has been tested, alongside data from the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric classification of COPD.

1. Dyspnea scale calculator

2. MRC scale explained

3. About dyspnea in COPD

4. References

• Original MRC
• Modified MRC

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MRC scale explained

This is a five grade clinical scale for patients with COPD that assesses the degree of dyspnea severity based on its impact on different physical daily activities.

The Medical Research Council scale was created by Fletcher in 1952 and starts from no nuisance from breathlessness during normal activities. Along the scale the degree of dyspnea increases.

The following table introduces the two versions of the MRC scale:

Currently, the modified version of the MRC (the MMRC) is most often used, especially alongside the BODE index, in the prognosis of patients diagnosed with chronic obstructive pulmonary disease.

The discriminative capacity of the MRC has been compared to data from the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric classification of COPD.

The two assessment methods have proven sufficient sensitivity separately but do not correlate between stages.

About dyspnea in COPD

Chronic obstructive pulmonary disease is a respiratory condition characterized by the following symptoms:

■ Breathlessness;

■ Cough (sometimes chronic);

■ Sputum production;

■ Wheezing;

■ Chest tightness;

■ Airway irritability.

The above are suggestive of chronic COPD whilst COPD exacerbation means a stronger infective episode of COPD when the symptom severity increases and fatigue and weight loss are also experienced.

Dyspnea or breathlessness, is defined as a sensation of difficulty in breathing. This is most often attributed to lack of exercise and low level of fitness but also to pulmonary conditions such as COPD.

On exertion, a certain degree of breathlessness can occur normally but in pathological cases, it occurs at a level of activity that is either generally well tolerated or at a level of activity that the patient used to tolerate.

The symptoms include a clearly audible breathing, gasping, flaring nostrils, cyanosis, distressed facial expression and chest protrusion.

The following introduces major causes of dyspnea:

■ Heart attack, congestive heart failure, arrhythmias;

■ Pneumonia or pulmonary hypertension;

■ Gastroesophageal reflux disease;

■ Presence of allergies;

■ Chest wall trauma or foreign object inhalation.

Paroxysmal nocturnal dyspnea (PND) occurs at night and awakens the patient. PND is only relieved by an upright position.

Dyspnea needs to be differenced from other respiratory frequency or flow variations such as tachypnea, hyperventilation, and hyperpnea.

Original source

Fletcher CM. The clinical diagnosis of pulmonary emphysema; an experimental study . Proc R Soc Med. 1952; 45(9):577-84.

Other references

1. Stenton C. The MRC breathlessness scale . Occup Med (Lond). 2008; 58(3):226-7.

2. Fletcher CM, Elmes PC, Fairbairn AS, Wood CH. The significance of respiratory symptoms and the diagnosis of chronic bronchitis in a working population . Br Med J. 1959; 2(5147):257-66.

3. Bestall JC, Paul EA, Garrod R, Garnham R, Jones PW, Wedzicha JA. Usefulness of the Medical Research Council (MRC) dyspnoea scale as a measure of disability in patients with chronic obstructive pulmonary disease . Thorax. 1999; 54(7):581-6.

4. Rhee CK, Kim JW, Hwang YI, Lee JH, Jung KS, Lee MG, Yoo KH, Lee SH, Shin KC, Yoon HK. Discrepancies between modified Medical Research Council dyspnea score and COPD assessment test score in patients with COPD . Int J Chron Obstruct Pulmon Dis. 2015; 10:1623-31.

Specialty:  Pulmonology

System:  Respiratory

Objective:  Evaluation

Type:  Scale

No. Of Criteria:  5

Year Of Study:  1952

Abbreviation:  MRC

Article By:   Denise Nedea

Published On:  June 13, 2017

Last Checked:  June 13, 2017

Next Review:  June 13, 2023

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• Published: 24 July 2024

Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs

• Raúl López-Izquierdo 1 , 2 , 3 ,
• Carlos del Pozo Vegas   ORCID: orcid.org/0000-0001-5561-1384 1 , 4 ,
• Ancor Sanz-García   ORCID: orcid.org/0000-0002-5024-5108 5 , 6 , 7 ,
• Agustín Mayo Íscar   ORCID: orcid.org/0000-0003-0951-6508 8 ,
• Miguel A. Castro Villamor 1 ,
• Eduardo Silva Alvarado 9 , 10 , 11 ,
• Santos Gracia Villar 9 , 10 , 12 ,
• Luis Alonso Dzul López   ORCID: orcid.org/0000-0002-0800-8563 9 , 10 , 12 ,
• Silvia Aparicio Obregón 9 , 11 , 13 ,
• Rubén Calderon Iglesias 9 , 11 , 14 ,
• Joan B. Soriano 3 , 15 , 16 &
• Francisco Martín-Rodríguez 1 , 17  

npj Digital Medicine volume  7 , Article number:  197 ( 2024 ) Cite this article

Metrics details

• Outcomes research
• Predictive markers

Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha , beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions.

Introduction

Emergency medical services (EMSs) must manage acute life-threatening illness as part of the standard workflow. EMS providers must perform timely decision-making without delay and in dynamic, critical scenarios. The quick targeting of high-risk patients represents a major challenge in prehospital care 1 , and new strategies to improve their timely recognition are being continuously implemented 2 . Accordingly, the application of scores, biomarkers, risk models, and other markers is becoming routine in clinical practice 3 .

In patients without a clear acute life-threatening illness, on-scene blood tests may assist in screening for hidden high-risk conditions, e.g., electrolyte disturbances, metabolic-endocrine diseases, respiratory failure, anemia, or renal insufficiency 4 . Point-of-care testing (POCT) allows blood test results, including venous or arterial blood gas levels, renal profile, glucose, lactate, hematocrit, hemoglobin, troponin, D-dimer, myoglobin, and international normalized ratio, to be obtained. The POCT provides EMS healthcare personnel a quick (few minutes) bedside analytical data, which was formerly reserved for hospital use exclusively, but now helps and supports the on-scene decision-making process 5 .

In addition, precision emergency medicine is a hot research area. In prehospital critical care, early warning scores, risk scales, and predictive models are commonly used to detect time-dependent diseases and their short- and long-term prognoses 6 , 7 . Likewise, phenotypes are increasingly used to identify certain pathophysiological conditions in hospitals 8 , 9 , and they are applied in the prehospital setting 10 , 11 .

To our knowledge, there is limited evidence on phenotypes in prehospital care 12 , 13 . Accordingly, we aimed to develop clustering-derived phenotypes in patients with acute life-threatening illnesses based on vital signs and biomarkers collected by EMS upon initial emergency care. Furthermore, we aimed to determine the short- and midterm prognoses of these patients and the diseases associated with each phenotype.

From an original population of 11,182 patients, 8136 were considered eligible, and 7909 (97.2%) subjects satisfied the inclusion criteria and were included in the final cohort analysis (Fig. 1 ). The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas (Table 1 ). Clinical characterization via unsupervised machine learning revealed three clinical phenotypes that exhibited marked differences.

ROSC recovery of spontaneous circulation.

The clustering procedure was preceded by a reduction in dimensionality. As shown in Supplementary Fig. 1 , the first three dimensions explained 81.9% of the variance. The principal component analysis output was subsequently used for the clustering procedure, as shown in Supplementary Fig. 2 . The most parsimonious clustering model was the ellipsoidal, varying volume, shape, and orientation (VVV) model. Moreover, when the number of clusters was increased, the clustering model was stable, and no major difference in the BIC was found, suggesting that the VVV model and the clinically selected number of clusters were supported by the BIC results. The cluster results and an explanation of clinical criteria for the selection of the number of clusters are shown in Supplementary Fig. 3 .

The alpha phenotype was found in 16.2% (1281) of the patients, with a median age of 74 years, 41.7% (534 patients) female sex, an ALS evacuation rate of 76.3% (978 patients) and a nursing home origin of 21.7% (278 patients). The beta phenotype accounted for 28.8% (2279) of the patients, with a median age of 72 years and 39.8% (906 patients) female sex; 32.4% of the ALS patients evacuated and 11.8% (269 patients) evacuated from the origin of the nursing home. Overall, the gamma phenotype represented 55% (4349) of the patients, with a median age of 62 years; 42.3% (1840) of whom were females; 41.5% (1804 patients) who underwent ALS transfer; and 6% (261 patients) who were from nursing homes (Table 1 ).

On-scene vital signs also showed significant differences between the clusters. The alpha phenotype exhibited increased respiratory and cardiac rates and decreased saturation, SaFi, blood pressure and Glasgow coma scale ( p  < 0.001 in all patients). Differences across phenotypes were also evident in blood biomarkers, with significant differences among other parameters in pH, partial pressure of carbon dioxide, lactate, creatinine, and glucose ( p  < 0.001 in all) (Table 1 ).

The distribution of suspected prehospital diagnoses in each cluster is shown in Fig. 2 . Patients with acute life-threatening diseases were assigned by the unsupervised clustering method to the alpha phenotype and a priori less severe diseases or nonspecific syndromic conditions to the other two clusters. Accordingly, patients with the alpha phenotype were characterized by cardiac arrest, heart failure (including congestive heart failure) and dyspnea, followed by febrile syndrome, sepsis, and COVID-19; those with the beta phenotype displayed several heterogeneous conditions: tachyarrhythmias, syncope, seizures, stroke, acute chest pain and poisoning; and those with the gamma phenotype presented syncope, acute chest pain, stroke, poisoning, orthopedic trauma, and seizures.

Chord diagram representing the distribution of suspected prehospital diagnoses in each cluster. The blue line = alpha (C1), the green line = beta (C2), and the red line = gamma (c3).

The 2-day mortality rates were 18.6%, 4.1%, and 0.8% for the alpha , beta and gamma phenotypes, respectively. Moreover, 24.7%, 6.2%, and 1.7% of the patients died within 7 days, and 33%, 10.2%, and 3.2% died within 30 days (Table 2 ). In addition to mortality disparities, the alpha phenotype stood out due to an increased requirement for on-scene advanced life support interventions, associated burden of comorbidities, and major ICU admissions. Survival analysis revealed that the hazard ratios (HRs) for mortality in patients with the beta and alpha phenotypes were 3.37 (95% CI: 2.73–4.16) and 12.8 (95% CI: 10.6–15.6), respectively, when gamma was used as a reference (Supplementary Table 1 ). As shown in Fig. 3 , the highest mortality in the alpha phenotype occurred immediately, while the beta and gamma phenotypes separated within the first five days. All three curves slowed (shallow slopes) as time progressed. Supplementary Fig. 4 shows the survival curves of the three clustering-derived phenotypes as compared to low, medium, and high-risk categories of modified early warning score (MEWS). The mortality curves of each phenotype matched the mortality curve of each risk category of MEWS, this is, gamma phenotype was parallel to low-risk, beta to intermediate risk, and alpha to high risk, but always with phenotypes curves below the MEWS ones.

Survival curve of each phenotype. The blue line = alpha, the green line = beta, and the red line = gamma.

Finally, a clustering of the gamma phenotype was performed (Supplementary Fig. 5 , 6 , 7 ). The three gamma subclusters ( n  = 1321, 1704 and 1324 for gamma #1, #2, and #3, respectively) showed that mortality was higher for gamma #1 (1.14%, 2.8%, and 5.37%, at 2, 7 and 30-day mortality), followed by gamma #3 (1.06%, 2.19%, and 3.39%, at 2, 7 and 30-day mortality), the gamma #2 phenotype presented the lowest mortality rate (0.23%, 0.47%, and 0.82%, at 2, 7 and 30-day mortality) (Supplementary Table 2 ).

Our study described different phenotypes with increasing severity based only on on-scene variables and biomarkers in adults with unselected acute diseases managed by EMS who were evacuated with priority to the ED. The alpha and beta phenotypes identified those patients at risk of clinical worsening in a more appropriate way than the intermediate and high-risk of MEWS, making these clusters more valuable for triage. This study paves the way for applying standardized prehospital laboratory tests and routine vital signs to determine bedside phenotypes. Phenotyping to target critical care and support the decision-making process might become commonplace in clinical practice. This methodology is already well established for sepsis, chronic obstructive pulmonary disease/asthma, and heart failure 9 , 14 . More recently, it has been used to develop real-time solutions against COVID-19 15 . Nevertheless, phenotyping during prehospital critical care is emerging tentatively 12 , 16 , 17 .

Based on 30 variables (sociodemographic, clinical, and analytical biomarkers) collected during prehospital care and blinded to the main dependent outcome, three clustering-derived phenotypes were identified. The alpha phenotype was characterized by a compromised clinical condition (tachypnea, desaturation, impaired SaFi, lower blood pressure, tachycardia, and a poorer consciousness level), associated with acidosis, hypercapnia, negative base excess, hyperlactacidemia, an abnormal renal profile (creatinine and blood urea nitrogen rises) and hyperglycemia; such patients presented a marked dependence over time for on-scene life support interventions, the greatest rates of ICU admissions, and mortality (3-times greater for the beta phenotype and 10-times greater for the alpha phenotype, both compared to gamma ). Next, patients in the beta phenotype were characterized by an improved acid‒base balance, increased blood oxygen, mild hyperlactacidemia, a renal profile that returned to target ranges, and mild hyperglycemia. Finally, most of the gamma phenotype patients presented results within normal ranges.

As previously mentioned, the suspected prehospital diagnoses vary largely by phenotype; the alpha phenotype is characterized by severe heart disease and other conditions associated with high short- and long-term morbidity and mortality 18 . The beta phenotype conditions were highly heterogeneous. Finally, the gamma phenotype included a priori less severe diseases or nonspecific syndromic conditions. Our results aligned well with previous evidence, pooling in one cluster of critically ill patients 14 , 19 .

Consistently, the alpha phenotype was associated with high-level on-scene advanced life support interventions, ICU admissions, frequent advanced airway management and intravenous medication. This finding contrasted with the findings of beta phenotypes , particularly with the gamma phenotype, which requires less use of health services, which even more critical for the gamma subcluster #2, presenting a very low mortality rate of less than 1%, therefore, requiring lower attention by the EMS. Clinical evidence suggests an association between unplanned mechanical ventilation and mortality, just as concomitant administration of medication correlates with a worse prognosis 20 , thus suggesting that the group of patients who meet these criteria are in the most critical phenotype category. As expected, the cluster with the poorest outcomes ( alpha ) mostly included elderly patients and was more burdened by comorbidities. Several risk scores consider age and comorbidities as vulnerability indicators, such as the aCCI 21 . Frailty syndrome is a well-described multidimensional condition that, despite individual variability, constitutes a focal point directly related to poor outcomes. As age and comorbidities progress, physiological and psychosocial reserves may be jeopardized, enhancing clinical vulnerability 22 .

An innovative objective of this study was to conduct phenotyping with ultra-early (first contact with patients by the EMS staff) analytical data based on prospective and standardized POCT. From primitive capillary glucometers to current POCTs, technological advances have favored the production of novel devices available on-scene with reduced dimensions that are portable, robust and highly reliable, making them an ideal solution for deployment in ambulances 3 . Due to the support provided by POCT, EMS providers obtain crucial medical data quickly during the turnaround period; otherwise, the data are retrieved only from the hospital. We demonstrated that objective and structured clinical evaluation combined with biomarker testing in acute life-threatening diseases can guide targeted life support interventions on-scene or en route and optimize decision-making processes in prehospital critical care, all of which are aligned with international guideline recommendations 5 , 23 .

Phenotyping has begun to be incorporated in particular diseases, mainly in the hospital setting. García-Vidal, C. et al. 24 developed a system for the timely detection of high-risk patients during the first wave of the last COVID-19 pandemic. Using artificial intelligence techniques, they were able to identify three phenotypes: inflammation, superinfection and thrombotic events. Their system analyzed data in real time, allowing early decisions and quick personalized treatments, with a 90% prediction of patient evolution and a 50% reduction in mortality. Komorowski, M. et al. 25 developed the “AI Clinician”, a computational model based on reinforcement learning capable of dynamically suggesting optimal treatments for ICU patients with sepsis. Their model uses variables very similar to those proposed in our model. The AI Clinician was able to suggest individualized and clinically interpretable treatment strategies for sepsis. In an independent cohort, patients who received the treatments suggested by the AI Clinician had the lowest mortality rate. In the prehospital setting, Kang, D. et al. 26 , using deep learning algorithms, predicted the need for critical care by the EMS, with an AUROC of 0.867, outperforming conventional triage tools and early warning scores.

Unfortunately, prehospital care studies, such as the one from Kang et al., are exceptions, in part, due to the complexity of out-of-hospital work, hindering the implementation of EMS systems. The on-scene workflow, rushed decision-making, and ongoing dramatic interventions make inferring the patient’s phenotype impossible for EMS providers without support. One possible way to bridge this gap is to implement the algorithm developed to derive phenotypes in EMS electronic medical records. In this way, in real time and at the bedside, the EMS provider could have access to the information, supporting the decision-making process. This a priori difficult adoption of phenotyping could follow the example of scores, which are routinely employed in health services, e.g., body mass index, Glasgow coma scale, CHA2DS2-VASc score for atrial fibrillation stroke risk, etc. EMSs are not an exception since the use of early warning scores is a reality and mandatory for the decision-making process. Therefore, since EMS professionals are accustomed to work with scores, the implementation of phenotyping systems in the EMS could be a straightforward process.

The main strength of this study is that, by means of a free-scale machine learning methodology, we identified a phenotype, alpha , which comprises medically challenging conditions, with some degree of frailty and evident clinical disorders (impaired respiratory capacity, hemodynamic unsteadiness, neurological deterioration, lactic acidosis, hyperglycemia, etc.). Sixteen percent of patients, those typically requiring several advanced life support interventions on-scene, with a large proportion of inpatients admitted to the ICU, were ultimately associated with elevated mortality. Additionally, this method allowed to characterize patients which are not easy to identify such as those from beta, gamma, and even gamma subclusters, increasing the capability of the EMS to identify true negative patients. This zero-minute flagging of high-risk patients, based not only on standard vital signs but also on optimal support from blood test biomarkers, empowers the EMS system to recognize patients potentially compromised and to proactively implement the necessary interventions 27 . In this sense, artificial intelligence represented a breakthrough, emerging phenotyping as a flexible and useful solution with a proven risk-based case matching capability, allowing massive data analysis to classify high-risk patients as sentinel events 26 . Other strengths of our study include its large size, population size based on few exclusion criteria, and real-world setting.

Limitations of the study

However, a number of limitations are worth considering. First, a convenience sample was compiled. To minimize bias, a dual strategy was employed. All adult patients were screened for eligibility on a 24/7/365 basis; in addition, patients with various ALS types from urban and rural locations and from hospitals with diverse capabilities (one minor general district hospital and three university tertiary hospitals) were included in the study. Second, the data extractors were unblinded. To prevent crossover connections, the EMS providers had no access to the hospital follow-up data; vice versa, the hospital investigators were unaware of the prehospital care data; only the principal investigator and the data manager received full access to the master database and the phenotyping output. Third, the EMS medical records are still paper-based and not yet electronic. Manual review of the patient medical records attended by the EMS (the current reference standard for identifying patient cohorts) demands a significant amount of time and resources. Considerable efforts are being made by the Public Health system to implement a prehospital electronic health record system involving both the BLS and ALS, with operational capacity for real-time transmission of all the information to the ED. Fourth, despite the rapid expansion of POCT in numerous EMS systems around us, this technology has not been regularly implemented in all ambulances or all ALS wards. Finally, the study was carried out before and concurrently with the ongoing COVID-19 pandemic. At the peak of the first wave of the pandemic, EMS activation for acute life-threatening diseases declined drastically, and the extent of the effect of COVID-19 on the physiological and psychosocial reserve of surviving patients is unclear. More research is needed to determine the excess mortality due to non-COVID-19 pathology in the prevaccination stage, and in addition, the role of COVID-19 in increasing clinical vulnerability in the medium and long term should be explored.

In summary, based on data collected exclusively in prehospital care, unselected acute disease patients managed by EMS and transferred to the ED can be categorized into three phenotypes with different clinical and prognostic implications. At the first point of care, EMS staff can identify the risk level, avoid underrated hidden unresolved health conditions and characterize complex or atypical clinical presentations. Identifying patients with an alpha phenotype from the initial moments of assistance allows the development of a personalized strategy, tailoring the level of support and resources to individual situations, or even determining the most appropriate course of action for each patient. This knowledge provides valuable information for bedside decision-making from the outset to design the best possible care strategy tailored to the individual case.

Study design and setting

A prospective, multicenter, EMS-delivered, ambulance-based cohort study was conducted with adults with unselected acute diseases (assistance by an advanced life support unit -ALS-) managed by EMS who were evacuated with priority discharge to the ED from January 1, 2020, to June 30, 2023.

The study involved the use of a 1–1–2 emergency coordination center, six ALS units, 38 basic life support (BLS) units, and four hospitals in Salamanca, Segovia and Valladolid (Spain), comprising a population of 995,137 inhabitants and comprising urban and rural communities. The public health system managed and coordinated all the facilities. BLSs include two emergency medical technicians (EMTs); ALSs are made by an emergency registered nurse (ERN) and a physician, operating all EMS providers in compliance with life support guidelines.

Patients were prospectively included uninterruptedly from two studies conducted under identical research protocols, the “HITS study” (ISRCTN48326533) and the preBIO study” (ISRCTN49321933), which were approved by the institutional review board of the Public Health Service and followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement (supplementary material Note 1 ) 28 . Informed consent was obtained from all human participants.

Adults (>18 years) with unselected acute illnesses were screened for eligibility consecutively 24/7/365 by the EMS. Additionally, following an evaluation by an ALS physician, to be included in the study, patients had to be mandatorily referred to the emergency department (ED), either at the BLS or at the ALS.

Minors, pregnant women (evident or probable), cardiac arrest without recovery of spontaneous circulation on-scene, end-stage patients (documented by a report), impossibility to obtain prehospital blood tests (e.g., difficulty to establish venous access, breakdown of blood testing device), and no informed consent were excluded. Patients requiring prehospital care and already registered in the database for previous care were excluded.

The principal outcome was cumulative mortality (all-cause) at 2, 7, and 30 days. The secondary variables considered included on-scene life support interventions (advanced airway management, defibrillation or pacemaker application, and intravenous medication delivery), suspected prehospital diagnoses (29 different subcategories), hospital outcomes (inpatient, intensive care unit admission), and 17 comorbidities needed to calculate the age-adjusted Charlson comorbidity index (aCCI).

Data collection and processing

The EMS providers received prior face-to-face training on the implementation of the research protocol and standardized data entry into the database.

Covariates included sociodemographic variables (sex at birth and age); on-scene vital signs (respiratory rate, oxygen saturation, blood pressure, heart rate, temperature, and Glasgow coma scale); and prehospital blood analysis (pH, bicarbonate, excess bases, sodium, potassium, chloride, calcium, hemoglobin, hematocrit, creatinine, blood urea nitrogen, glucose, lactate, osmolarity, GAP anion, urinary anion, and potassium anion), which were obtained by the ERN. Measurements were collected immediately upon starting prehospital care on the first patient encounter. Vital signs were obtained via a LifePAK® 15 monitor-defibrillator (Physio-Control, Inc., Redmond, USA), and blood tests were performed by means of an Epoc® analyzer (Siemens Healthcare GmbH, Erlangen, Germany). The respiratory rate was monitored by direct observation and counting of breathing cycles for half a minute; in the case of very shallow or difficult breathing, the respiratory rate was measured by direct auscultation. Oxygen therapy (by any method) was also administered at the time of the patient’s diagnosis of ALS; once the fraction of inspired oxygen was known, the pulse oximetry saturation/fraction of inspired oxygen ratio (SaFi) was calculated.

After a 30-day follow-up period, data on mortality, comorbidities and hospital admissions were collected by reviewing the patients’ electronic medical records. The data were recorded electronically in a database specifically designed for this purpose, recording the prehospital care variables. Access was provided by individual passwords and double authentication. After the data were cleaned (logic, range and consistency tests), a total of 54 variables were analyzed. Once the data were linked, patient identifiers were anonymized.

Statistical analysis

Descriptive and bivariate statistics for the outcome variables were assessed by the t test, the Mann‒Whitney U test or the chi-square test, whenever appropriate. Absolute values and percentages were used for categorical variables, and median interquartile ranges (IQRs) were used for continuous variables that were not normally distributed. The clustering procedure was as follows: First, a reduction in dimensionality (principal component analysis) was used to reduce the number of variables. The most parsimonious clustering model was selected by the Bayesian information criterion (BIC) to perform Gaussian mixture modeling for model-based clustering. Since the clusters were obtained from the same unsupervised method, all resulted from the same set of variables. The number of clusters was fixed to three based on clinical criteria. Finally, each cluster was explored by including the outcomes, life support interventions, suspected prehospital diagnoses, and aCCI. Finally, a survival analysis was performed according to each phenotype; this was the univariate comparison between each independent variable and the outcome, assessed by the log-rank test, and the survival curve according to clusters was obtained using the Kaplan‒Meier method (KM).

The data were collected and registered in a database generated with the IBM SPSS Statistics for Apple version 20.0 software. (IBM Corp, Armonk, NY, USA). The caseload entry system was tested to delete unclear or ambiguous values and to verify the adequacy of the data gathering system. Missing values were random; therefore, a listwise deletion method was used since it does not induce biased means, variances or regression weight modifications. The sample size needed for the clustering studies has been recently estimated 29 . Due to the characteristics of the clustering procedure, the phenotypes derived from clustering are driven by large effect sizes or by the accumulation of small effect sizes among the multiple variables analyzed, and there is no effect of the covariance structure difference. Therefore, a small sample size (e.g., N  = 20), as stated in ref. 29 , allows large cluster separations.

All calculations and analyses were performed by our own codes, R packages (mclust 30 ) and base functions in R, version 4.2.2 ( http://www.R-project.org ; the R Foundation for Statistical Computing, Vienna, Austria).

Inclusion and ethics statement

All collaborators of this study have fulfilled the criteria for authorship required by Nature Portfolio journals have been included as authors, as their participation was essential for the design and implementation of the study. Roles and responsibilities were agreed among collaborators ahead of the research. This work includes findings that are locally relevant, which have been determined in collaboration with local partners. This research was not severely restricted or prohibited in the setting of the researchers, and does not result in stigmatization, incrimination, discrimination, or personal risk to participants. Local and regional research relevant to our study was taken into account in citations.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

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

Code availability

The underlying code for this study [and training/validation datasets] is not publicly available but may be made available to qualified researchers upon reasonable request from the corresponding author.

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Acknowledgements

This work was supported by the Gerencia Regional de Salud, Public Health System of Castilla y León (Spain) [grant numbers GRS 1903/A/19 and GRS 2131/A/20] for FM-R. Sponsor role: None.

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C.P.V. and F.M.-R. conceptualized the project, managed and coordinated the project, assisted with the design of the methodology, analyzed the data, and prepared the initial and final drafts of the manuscript. A.S.-G. and A.M.I. take responsibility for the data and their analysis. M.A.C.V., E.S.A., S.G.V., L.A.D.L., S.A.O., and R.C.I. contributed to the management and coordination of the project, assisted with the design of the methodology, and helped review the manuscript. R.L.I. and F.M.-R. conceptualized the project and helped review and comment on the initial and final drafts of the manuscript. All the authors performed a critical review and approved the final manuscript for interpretation of the data and important intellectual input.

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López-Izquierdo, R., del Pozo Vegas, C., Sanz-García, A. et al. Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs. npj Digit. Med. 7 , 197 (2024). https://doi.org/10.1038/s41746-024-01194-6

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• Published: 16 July 2024

Low dose of morphine to relieve dyspnea in acute respiratory failure: the OpiDys double-blind randomized controlled trial

• Robin Deleris 1 ,
• Côme Bureau 1 , 2 ,
• Saïd Lebbah 3 , 4 ,
• Maxens Decavèle 1 , 2 ,
• Martin Dres 1 , 2 ,
• Julien Mayaux 1 ,
• Thomas Similowski 2 , 5 ,
• Agnès Dechartres 4 &
• Alexandre Demoule 1 , 2  

Respiratory Research volume  25 , Article number:  280 ( 2024 ) Cite this article

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Metrics details

Morphine relieves dyspnea in various clinical circumstances. Whether or not this applies to patients admitted to intensive care units (ICUs) for acute respiratory failure (ARF) is unknown. We evaluated the efficacy and safety of low-dose morphine on dyspnea in patients admitted to the ICU for ARF.

In this single-center, double-blind, phase 2, randomized, controlled trial, we assigned non-intubated adults admitted to the ICU for ARF with severe dyspnea, defined by a visual analog scale for dyspnea (dyspnea-VAS) from zero (no dyspnea) to 100 mm (worst imaginable dyspnea) ≥40 mm, to receive a low dose of Morphine Hydrochloride (intravenous titration followed by subcutaneous relay) or Placebo. All patients received standard therapy, including etiological treatment and non-invasive respiratory support.

Twenty-two patients were randomized, 11 in each group. The average dyspnea (median [interquartile range]) over 24 hours did not significantly differ between the two groups (40 [25 – 43] mm in the Morphine group vs. 40 [36 – 49] mm in the Placebo group, p =0.411). Dyspnea-VAS was lower in the Morphine group than in the Placebo group at the end of intravenous titration (30 [11 – 30] vs. 35 [30 – 44], p =0.044) and four hours later (18 [10 – 29] vs. 50 [30 – 60], p =0.043). The cumulative probability of intubation was higher in the Morphine group than in the Placebo group ( p =0.046)

In this phase 2 pilot trial, morphine did not improve 24-hour average dyspnea in adult patients with ARF, even though it had a statistically significant immediate effect. Of concern, Morphine use was associated with a higher intubation rate.

Trial registration

The protocol was declared on the ClinicalTrial.gov database (no. NCT04358133) and was published in September 2022.

Introduction

Dyspnea is one of the most distressing experiences a human being can endure [ 1 ]. Approximately half of patients admitted to the intensive care unit (ICU) for acute respiratory failure (ARF) report moderate to severe dyspnea [ 2 ]. Average dyspnea intensity in this population is 40 mm on a visual analog scale (VAS) ranging from zero (no dyspnea) to 100 mm (worst imaginable dyspnea) [ 2 , 3 ]. Patients undergoing non-invasive ventilation report dyspnea as one of the worst experiences of their ICU stay [ 4 ]. In this population, there is a strong association between dyspnea and anxiety [ 5 ]. Finally, dyspnea is associated with a higher intubation rate [ 4 , 6 ] and a higher mortality [ 6 ]. It should be noted that in intubated patients, dyspnea is associated with an increased prevalence of post-traumatic stress disorder [ 5 ]. For all these reasons, controlling dyspnea in ARF patients is a major goal of care [ 7 ].

Unfortunately, dyspnea can persist in spite of the optimal treatment of the condition causing ARF, oxygen supplementation and non-invasive ventilatory support or the correction of metabolic abnormalities [ 8 ]. Opioids, well known to relieve dyspnea [ 9 ], could help in controlling dyspnea in ARF patients [ 10 ]. The fear of overdose with respiratory depression has historically been the main obstacle to the widespread use of morphine for the relief of dyspnea. However, several meta-analyses have shown the benefit of morphine on long-term persistent dyspnea, but also its safety in patients with end-stage onco-hematological disease, chronic obstructive pulmonary disease or advanced heart failure [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. In addition, recent guidelines from the American Thoracic Society advocate oral or parenteral administration of opioids for persistent dyspnea [ 26 ].

The objective of this trial was to determine whether the administration of low-dose titrated morphine, compared to placebo, in patients admitted to the ICU for ARF with moderate to severe dyspnea decrease dyspnea without increasing adverse events.

Trial design

This is a single-center phase 2 double-blind randomized controlled trial conducted in a 22-bed medical ICU within La Pitié-Salpêtrière University Hospital in Paris, France. The trial was approved by the Institutional Review Board (South Mediterranean III Comité de Protection des Personnes on December 5, 2019, no. 19.10.24.60836). All patients or relatives provided written informed consent. The protocol was declared on the ClinicalTrial.gov database (no. NCT04358133) and was published in September 2022 [ 27 ].

Participants

Eligibility criteria were patients on standard oxygen, high-flow oxygen or non-invasive ventilation who fulfilled all the following criteria, 1) admitted to the ICU for an ARF defined as a respiratory rate >24/min or signs of respiratory distress such as labored breathing or paradoxical inspiration, or SpO2 <90% in ambient air; 2) with dyspnea ≥40 mm on a VAS for dyspnea (dyspnea-VAS) from zero (no dyspnea) to 100 mm (worst imaginable dyspnea) despite the department's usual measures: analgesic and anxiolytic treatment, reassurance, etiological treatment of ARF, and non-invasive respiratory support; 3) with age between 18 and 75 years; 4) Richmond agitation and sedation scale (RASS) between 0 and +2; 5) who presented no confusion, as defined by the Confusion Assessment Method for ICU (CAM-ICU) [ 28 ]; 6) who provided informed consent or for whom consent could be obtained from a relative or through emergency consent procedure.

Non-inclusion criteria were intubated and tracheotomized patients or patients whose intubation was planned upon admission; patients unable to communicate verbally and self-report dyspnea on a VAS (hearing or visual impairment, insufficient command of French, previous known psychiatric or cognitive disorders; moribund patients; contraindication to opioids (known hypersensitivity to opioids, creatinine clearance <30 ml/min, severe hepatocellular insufficiency defined by factor V <50%); pregnant or breastfeeding woman; opioid use within the 24 hours before inclusion; protected adult; not affiliated to the French public health insurance: previous inclusion in this trial; exclusion period due to inclusion in another clinical trial.

Randomization

After informed consent had been obtained, participants were included in the study and randomly assigned in a 1:1 ratio to the intervention or control group using a computer sequence with random permuted blocks. Randomization was performed on the electronic case report form (eCRF) (Cleanweb, Télémédecine Technologies, Boulogne-Billancourt, France). Sequentially numbered containers of identical appearance prepared by the pharmacy and containing morphine or placebo were stored in the ICU. The container with the smallest serial number available in the department's stock was assigned to the newly included patient.

Intervention

All management decisions other than the administration of morphine were made by the managing physician according to the department's usual practices.

The experimental group received an intravenous titration of morphine hydrochloride at a concentration of 1 mg per ml of NaCl 0.9%. The titration consisted of an initial bolus of 2 ml (2 mg), followed by a 1 ml (1 mg) bolus every 3 minutes until dyspnea-VAS was <40 mm, with a maximum safety dose of 8 ml (8 mg). Once the target (either dyspnea-VAS <40 mm or safety dose of 8 mg) was reached, morphine hydrochloride (1 mg per ml) was administered subcutaneously. A first dose of 5 ml (5 mg) was administered immediately after the intravenous titration and then every 4 hours for 24 hours. At each 4-hour time point, if dyspnea-VAS was ≥40 mm, the dose of morphine was increased from the previous one by increments of 2.5 ml, without exceeding the maximum dose of 10 ml (10 mg) every 4 hours. If Dyspnea-VAS was <40 mm, the dose of morphine administered every 4 hours was reduced by 2.5 ml (2.5 mg) (Fig. 1 ).

Procedure for administering morphine hydrochloride 1 mg/mL or placebo. The experimental group received an intravenous titration of morphine hydrochloride at a concentration of 1 mg per ml of NaCl 0.9%. The titration consisted of an initial bolus of 2 ml, followed by a 1 ml bolus every 3 minutes until dyspnea-VAS was <40 mm, with a maximum safety dose of 8 mg. Once the target (either dyspnea-VAS <40 mm or safety dose of 8 ml) was reached, morphine hydrochloride (1 mg per ml) was administered subcutaneously. A first dose of 5 ml was administered immediately after intravenous titration and then every 4 hours for 24 hours. At each 4-hour time point, if dyspnea-VAS was ≥40 mm, the dose of morphine was increased from the previous one by increments of 2.5 ml, without exceeding the maximum dose of 10 ml every 4 hours. If Dyspnea-VAS was <40 mm, the dose of morphine administered every 4 hours was reduced by 2.5 ml. The control group received NaCl 0.9%, which was administered according to the same protocol as the experimental arm

The control group received NaCl 0.9%, which was administered according to the same protocol as the experimental arm (Fig. 1 ).

Primary outcome was the average of the dyspnea ratings gathered every 4 hours over the 24 hours following inclusion or until intubation. The following secondary outcomes were measured over the first 24 hours following randomization: intensity of dyspnea-VAS at the end of the intravenous titration and every 4 hours; average anxiety-VAS, respiratory rate and Glasgow coma scale measured every 4 hours; incidence of moderate-to-severe dyspnea and anxiety, defined by a VAS ≥40 mm; intubation rate; incidence of Glasgow coma scale ≤12; incidence of delirium defined by the CAM-ICU, duration and quality of sleep during the first night as assessed by the patients themselves (informally) at the end of the first night by a VAS (from 0, worst to 100 mm, best); proportion of patients requiring the transition from one oxygenation technique to another; number of non-invasive ventilation sessions; total duration of standard oxygen, non-invasive ventilation and high-flow nasal oxygen; tolerance of standard oxygen, high-flow nasal and non-invasive ventilation (VAS from 0, worst to 100 mm).

Constipation, nausea and severity of dry eye, dry nose and feeling of gastric distension were evaluated at the end of the 24-hour study period (VAS from 0, worst to 100 mm, best). Nurses’ adherence to and satisfaction with the protocol were evaluated at the end of the 24-hour study period (VAS from 0, worst to 100 mm, best).

The following adverse events considered medically significant occurring within the first 48 hours were collected: intubation; nausea ≥grade 3; constipation ≥grade 4; bradypnea <12 cycles per minute; coma defined by a Glasgow coma scale ≤9, pruritus grade ≥4; [ 29 ] worsening of respiratory condition requiring intubation.

Statistical analysis

Based on previous data, we hypothesized that mean dyspnea-VAS over the first 24 hours would be 37 mm in the control arm with a standard deviation of 26 mm [ 2 , 3 ]. We hypothesized that mean dyspnea-VAS over the first 24 hours would be 12 mm in the experimental arm, which makes a difference of 25 mm, which is which is more than twice the minimally clinical important difference (10 mm) for dyspnea-VAS in other clinical contexts [ 30 ]. Therefore, with a power of 80% and a one-sided alpha risk of 10%, we calculated that 22 patients should be recruited (11 per group). The choice of a one-sided alpha risk of 10% is justified by the fact that we did not want to miss a potential signal of an effect of morphine on dyspnea in this phase 2 pilot study.

The analysis used the intent-to-treat approach, ie, all patients were analyzed in the group allocated by randomization, with no exclusion after randomization except exclusions for withdrawn consent according to the French regulation. Categorical variables were described as frequency and percentage and quantitative variables were described as median and interquartile range.

For the primary outcome, the comparison between the two treatment groups of the average dyspnea during the first 24 hours was performed by a Wilcoxon’s rank-sum test, taking a one-sided alpha risk of 10% to limit the risk of missing a difference.

For secondary outcomes, quantitative variables were compared between the two arms with a Wilcoxon’s rank-sum test. Categorical variables were compared between the two arms with a Fisher’s exact test. Cumulative probability of intubation was compared with the log rank test. All analyses were carried out with a unilateral alpha risk of 10%, using R software Version 4.1.1.

Patients characteristics and intervention

From December 16, 2020 to October 7, 2022, 1696 patients were admitted for ARF, and 22 patients were randomized: 11 in the Placebo group and 11 in the Morphine group. Because of the particular feature of dyspnea in COVID-19 patients, the study was interrupted during the pandemic. Figure 2 shows the study flow chart and reasons for not including patients. Baseline characteristics were evenly distributed between the two groups (Table 1 ).

Study flowchart

Dyspnea-VAS upon inclusion was severe in both groups (70 [51 – 74] mm in the Placebo group and 70 [60 – 80] mm in the Morphine group). During titration, patients in the Morphine group received 3 [ 2 , 3 , 4 , 5 , 6 ] mL of morphine hydrochloride 1 mg/ml vs. 5 [ 4 , 5 , 6 , 7 ] ml in the Placebo group. The proportion of patients who reached a dyspnea-VAS <40 mm at the end of the titration was 91% ( n =10) in the Morphine group vs. 73% ( n =8) in the Placebo group. Time to reach a dyspnea-VAS <40 mm or the maximum intravenous dose of 8 ml was 6 [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] min in the Morphine group and 13 [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ] min in the Placebo group ( p =0.431). Over the 24 hours following titration, patients in the Morphine group received 8 [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ] ml of morphine hydrochloride 1 mg/ml vs. 28 [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] ml in the Placebo group.

Intervention was discontinued in four patients of the Morphine group because of respiratory failure requiring intubation and in one patient of the Placebo group because of a transfer to another ICU. In the Morphine group, two of the four intubations occurred during the intravenous administration phase, while the two others occurred during the subcutaneous administration phase.

During the follow-up, 3 patients (27%) in the Morphine group and 2 patients (18%) in the Placebo group received anxiolytics ( p >0.999), 1 patients (9%) in the Morphine group and 4 patients (36%) in the Placebo group received non-opioid analgesics ( p =0.31), and 3 patients (27%) in the Morphine group and 6 patients (55%) in the Placebo group received either anxiolytics, non-opioid analgesics or both ( p =0.39).

Primary outcome

Average dyspnea-VAS over the 24 hours following inclusion was 40 [25 – 43] mm in the Morphine group and 40 [36 – 49] mm in the Placebo group ( p =0.411) (Table 2 ).

Secondary outcomes

Figure 3 shows dyspnea-VAS and the proportion of patients exhibiting moderate to severe dyspnea at baseline, at the end of intravenous titration and every 4 hours during the 24 hours of follow-up. Dyspnea-VAS was lower in the Morphine group than in the Placebo group at the end of intravenous titration (30 [11 – 30] ml vs. 35 [30 – 44] ml, p =0.044) and four hours later (18 [10 – 29] ml vs. 50 [30 – 60] ml, p =0.043). There was no significant difference between the two groups at other time points in terms of dyspnea-VAS. At the end of intravenous titration, the proportion of patients exhibiting moderate to severe dyspnea was lower in the Morphine group than in the Placebo group (9% vs 30%, p < 0.001). There was no difference between the two groups at other time points in terms of proportion of patients exhibiting moderate to severe dyspnea.

Visual analog scale for dyspnea (upper panel) and prevalence of moderate to severe dyspnea (lower panel) in the Morphine and the Placebo group on enrolment, at the end of intravenous titration and every four hours during the 24 hours following randomization

Table 2 shows main secondary outcomes over the 24-hour study period (see also Table E1 in the Online Supplement for all other planned secondary outcomes). The average respiratory rate over the 24 hours was higher in the Morphine group than in the Placebo group. There was no significant difference between the two groups in the incidence of moderate to severe dyspnea or anxiety and in terms of average anxiety-VAS and Glasgow coma scale over the 24 hours, proportion of patients with a Glasgow coma scale ≤12, constipation and nausea. Nurses’ satisfaction with the protocol was higher in the Placebo group than in the Morphine group (8 [ 8 , 9 ] mm vs 7 [ 5 , 6 , 7 ] mm p = 0.011).

Adverse events

Table 3 shows adverse events over the 48 hours following randomization. The incidence of delirium, Glasgow coma scale ≤9, and severe pruritus, nausea and constipation was not significantly different between the two groups. In the Morphine group, 45% ( n =5) of patients were intubated vs. 9% ( n =1) in the Placebo group ( p =0.149).

The cumulative probability of intubation was higher in the Morphine group than in the Placebo group (Log rank, p =0.046, Fig. 4 ).

Cumulative risk for intubation over the 48 hours following randomization in the Morphine and in the Placebo group

Three patients died in the Morphine group and one in the Placebo group ( p =0.586).

42Discussion

In patients admitted to the ICU for ARF, this phase 2 pilot randomized controlled trial found no significant benefit of low-dose morphine on average dyspnea over 24 hours, although intravenous morphine titration significantly reduced dyspnea during the first four hours. Of concern was the fact that morphine was associated with an increased risk of intubation.

In recent years, dyspnea has become a matter of concern in ICU patients [ 7 ]. Indeed, dyspnea is frequent and severe in patients admitted for ARF [ 3 , 31 ]. Dyspnea is associated with anxiety [ 3 , 5 , 32 ]. It is also associated with a higher prevalence of post-traumatic stress disorders. Relieving dyspnea should be a major target, such as controlling pain. When significant dyspnea persists despite treating the cause of ARF and administering a non-invasive respiratory support, it is permissible to consider the administration of opioids, in the absence of any other pharmacological approach, and based on their known effect on dyspnea in other clinical contexts. Original research and subsequent meta-analyses have shown that morphine successfully relieves dyspnea in patients with terminal cancer [ 13 ], cardiac failure [ 14 ], idiopathic pulmonary fibrosis [ 16 ] and chronic obstructive pulmonary disease [ 20 , 25 ] In the ICU, recent data suggest that morphine successfully relives dyspnea in intubated patients [ 33 ], but no randomized trial had been conducted in ICU patients exhibiting moderate to severe dyspnea upon admission. This is the reason why we decided to conduct the present study. Morphine did improve dyspnea transiently, but failed to show any significant effect on the 24-hour average dyspnea as we defined it. One of the potential explanations is that the subcutaneous dosage of the repeated administrations was not high enough. Another explanation is that in both groups, patients received etiological and symptomatic treatment of dyspnea with bronchodilators, hydro-sodium depletion, anti-infectious therapies and ventilatory support with NIV, high-flow oxygen through nasal cannula or standard oxygen therapy. These therapies have a known effect in relieving dyspnea which, although inconstant, can lead to a floor effect, with dyspnea decreasing more rapidly in the Morphine group [ 34 ]. For instance, from the 8 th hour after inclusion, dyspnea-VAS was less than 40 mm in both groups, with prevalence of severe dyspnea that was less than 50%. With this in mind, we acknowledge that the choice of the 24-hour average dyspnea as the primary outcome of this study might have been a mistake: to draw a crude analogy, morphine is expected to be effective before fracture reduction, and much less so 24 hours afterwards. Therefore, regarding patients' comfort, the "end-of-titration" and "four-hour" dyspnea outcomes might be more relevant than the 24-hour average outcome. Another hypothesis would be that there was too much time between dyspnea ratings, possibly combined with too low a dose of morphine, which meant dyspnea was already increasing again at the time of the rating. It is also interesting to note that there is a wide dispersion (visible in Fig. 3 ), possibly due to significant variations in volume of distribution or pharmacokinetic effects, since morphine worked for the first hour, so the subcutaneous form dispensed afterwards may not be the most suitable.

We were struck by the magnitude of the effect observed in the placebo arm of the study. This efficacy had already been found in other studies looking at the relief of dyspnea, one of which failed to demonstrate the efficacy of sertraline in relieving chronic dyspnea [ 35 ] and the other the inability of nefopam to relieve experimental dyspnea in healthy volunteers [ 36 ]. In these studies, the placebo effect could modify the anticipation processes recognized as determinants of the experience of dyspnea [ 37 ]. Another mechanism involved could be the Hawthorne effect: participation in a clinical trial focusing on dyspnea would be sufficient to generate clinical benefits, by enabling patients to realize that their condition is being observed and is therefore no longer ignored [ 38 ]. It is therefore not surprising that participation in a clinical trial focusing on dyspnea should be sufficient to generate clinical benefits.

Of major concern was the higher proportion of patients intubated in the Morphine group. Beyond the efficiency of opioids in relieving dyspnea, several studies have pointed to their safety in patients with respiratory disorders, which is why guidelines from the American College of Chest Physicians [ 26 ], the Canadian Thoracic Society [ 39 ] and the American Thoracic Society [ 40 ] advocate the use of opioids for persistent dyspnea. Although opioids are known to depress respiratory drive, most studies conducted in dyspneic patients without ARF have shown that their use was not associated with a significant decrease in respiratory rate and pulsed oxygen saturation or an increase in PaCO 2 [ 16 , 18 , 21 , 41 , 42 ]. Unfortunately, our observations do not go in this direction. Although morphine was not associated with a significant decrease in the level of consciousness (assessed by the Glasgow Coma scale), we observed a higher incidence of intubation in the Morphine group than in the Placebo group, noting that two of the patients intubated in the morphine group were intubated during the titration period. This strongly tempers the idea that could derive from our results that morphine could be useful not over 24 hours but at the very initial phase of ARF: finding the amount of opioids that may relieve dyspnea without worsening ARF and precipitating intubation might well be impossible. Of notice, patients in the Morphine group were more likely to have a chronic respiratory disease and had a higher baseline respiratory rate, which may suggest that they were more severe and hence may explain why the intubation rate was higher in the Morphine group. Finally, our study raised the potential interest of non-pharmacologic interventions such as sensory interventions targeting the brain rather than the respiratory system. The principle of these interventions is to modulate the emotional and affective component of dyspnea. Recent data in mechanically ventilated patients experiencing dyspnea show that exposure to relaxing music and exposure to facial air flux delivered by a fan significantly decrease dyspnea [ 43 ]. These interventions have no toxicity.

The strength of our study is to be the first randomized controlled trial to evaluate the potential benefit of opioids on dyspnea in patients admitted to the ICU with ARF. We used a double-blind design to limit bias, in particular classification bias, with a primary outcome that could suffer from subjective assessment. This study has several major limitations. First, due to the small sample size, the study is clearly underpowered. We designed it as a pilot phase 2 study and therefore our results should be considered as exploratory. We calculated the sample size based on the benefit of morphine in non-critically ill patients. We acknowledge that the small sample size may limit the capacity to account for variables such as underlying diseases, concomitant therapies, or patient anxiety levels. However, the aim of randomization is to balance characteristics between groups. In addition, the exclusion rate was high due to very stringent non-inclusion criteria, with the purpose of enrolling patients corresponding as much as possible to our target population, which we found crucial for a pilot phase 2 trial. Second, although we showed an increased incidence of intubation in the Morphine group, it is important to keep in mind that there were no predefined criteria for intubation. Third, the switch from intravenous titration to subcutaneous administration seemed to be associated with a relapse of dyspnea. Intravenous patient-controlled analgesia might be a promising alternative to subcutaneous administration.

In conclusion, this single-center phase 2 pilot randomized controlled trial not only failed to show a benefit of morphine in relieving dyspnea over 24 hours in patients with ARF and severe dyspnea admitted to the ICU trial, but also showed that morphine was associated with a higher intubation rate. Because dyspnea is a major issue in critically ill patients, future studies should search for a protocol of opioid administration that relieves dyspnea without worsening ARF severity.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Acknowledgements

Ilham Ghezzou and Zia Ounsa, clinical research assistants; Fredy Pene, clinical research manager; and Anne Bissery, study coordinator in the Clinical Research Unit of La Pitié-Salpêtrière in Paris. Nick Walker, who has edited the manuscript for English.

This work was supported by a grant from Assistance Publique – Hôpitaux de Paris (CRC no. 18023).

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Contributions

TS, ADec and ADem designed the study. RD, CB, JM and AD coordinated the study. RD, CB, MDe, MDr, JM and AD were responsible for patient screening, enrolment and follow-up. RD, SL, ADec and ADem analyzed the data. RD, SL, ADec and ADem wrote the manuscript. All authors had full access to all of the study data, contributed to drafting the manuscript or revised it critically for important intellectual content, approved the final version of the manuscript, and take responsibility for the integrity of the data and the accuracy of the data analysis.

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The trial was approved by the Institutional Review Board (South Mediterranean III Comité de Protection des Personnes on December 5, 2019, no. 19.10.24.60836). All patients or relatives provided written informed consent.

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Robin Deleris has nothing to disclose. Côme Bureau has nothing to disclose. Saïd Lebbah has nothing to disclose. Maxens Decavèle reports support for attending meetings and/or travel from Isis medical, outside the submitted work. Martin Dres reports personal fees and grants from Lungpacer unrelated to the work submitted. Julien Mayaux reports support for attending meetings and/or travel from Gilead, outside the submitted work. Thomas Similowski reports grants from Lungpacer, consulting fees from ADEP Assistance, AstraZeneca France, Chiesi France, KPL consulting, Lungpacer Inc, Novartis France, TEVA France and Vitalaire, outside the submitted work. Agnès Dechartres reports grants from the French Ministry of Health. Alexandre Demoule reports grants, personal fees and non-financial support from Philips, personal fees from Baxter, personal fees and non-financial support from Fisher & Paykel, grants from the French Ministry of Health, grants from Assistance Publique – Hôpitaux de Paris, personal fees from Getinge, grants, personal fees and non-financial support from Respinor, grants, personal fees and non-financial support from Lungpacer, personal fees from Lowenstein and personal fees from Gilead, outside the submitted work.

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Deleris, R., Bureau, C., Lebbah, S. et al. Low dose of morphine to relieve dyspnea in acute respiratory failure: the OpiDys double-blind randomized controlled trial. Respir Res 25 , 280 (2024). https://doi.org/10.1186/s12931-024-02867-2

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DOI : https://doi.org/10.1186/s12931-024-02867-2

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The Medical Research Council dyspnea scale in the estimation of disease severity in idiopathic pulmonary fibrosis

Affiliation.

• 1 Department of Critical Care and Pulmonary Services, National and Capodistrian University of Athens, Evangelismos Hospital, 45-47 Ipsilantou Street, Athens 10675, Greece. [email protected]
• PMID: 15878493
• DOI: 10.1016/j.rmed.2004.10.018

Background: Medical Research Council (MRC) chronic dyspnea scale, used for the estimation of disability due to dyspnea, may serve as a simple index of disease severity and extent in patients with idiopathic pulmonary fibrosis (IPF). However, its relationship with other commonly used measures has not been evaluated.

Methods: The association of MRC chronic dyspnea scale with lung function indices and high-resolution computerized tomography (HRCT) scores such as the total interstitial disease score (TIDs) and the fibrosis score (Fs) was examined in 26 untreated patients with IPF sequentially recruited over a period of 3 years. The aim of this observational study was to explore the relationship between dyspnea, impairment of lung function and CT estimation of disease severity in patients with IPF.

Results: The MRC dyspnea score was significantly associated with FVC, FEV1, TLC, DLCO, PaO2, and PaCO2 and with both HRCT scores. In multiple regression analysis only the FVC (OR = 0.85, 95% CI = 0.75-0.95, P = 0.004) and PaCO2 (OR = 0.69, 95% CI = 0.50-0.95, P = 0.02) correlated with dyspnea. Furthermore, both TIDs and Fs were negatively associated with FVC, FEV1, TLC and PaO2. In multiple regression analysis only the FVC correlated with both TIDs (r2 = 0.57, P = 0.0001) and Fs (r2 = 0.46, P = 0.0005).

Conclusions: These observations suggest that the MRC dyspnea scale could offer useful information about the estimation of severity in patients with IPF. Furthermore among functional indices the FVC seems to be the best estimator of disease severity and extent.

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mMRC dyspnoea scale indicates impaired quality of life and increased pain in patients with idiopathic pulmonary fibrosis

Kaisa rajala.

1 Helsinki University Hospital, Comprehensive Cancer Center, Dept of Palliative Care, Helsinki, Finland

2 Faculty of Medicine, University of Helsinki, Helsinki, Finland

Juho T. Lehto

3 Dept of Oncology, Palliative Care Unit, Tampere University Hospital, Tampere, Finland

4 Faculty of Medicine and Life Sciences, University of Tampere, Tampere, Finland

Eva Sutinen

Hannu kautiainen.

5 Primary Health Care Unit, Kuopio University Hospital, Kuopio, Finland

6 Folkhälsan Research Center, Helsinki, Finland

Marjukka Myllärniemi

7 University of Helsinki and Helsinki University Hospital, Heart and Lung Center, Dept of Pulmonary Medicine, Helsinki, Finland

Tiina Saarto

Associated data.

J.T. Lehto 00084-2017_Lehto

M. Myllärniemi 00084-2017_Myllarniemi

K. Rajala 00084-2017_Rajala

E. Sutinen 00084-2017_Sutinen

This study was undertaken to investigate idiopathic pulmonary fibrosis (IPF) patients' health-related quality of life (HRQoL) and symptoms in a real-life cross-sectional study. Our secondary aim was to create a simple identification method for patients with increased need for palliative care by studying the relationship between modified Medical Research Council (mMRC) dyspnoea scale, HRQoL and symptoms.

We sent a self-rating HRQoL questionnaire (RAND-36) and modified Edmonton Symptom Assessment Scale (ESAS) to 300 IPF patients; 84% of the patients responded to these questionnaires.

The most prevalent (>80%) symptoms were tiredness, breathlessness, cough and pain in movement. An increasing mMRC score showed a linear relationship (p<0.001) to impaired HRQoL in all dimensions of RAND-36 and the severity of all symptoms in ESAS. Dimensions of RAND-36 fell below general population reference values in patients with mMRC score ≥2. The intensity of pain in movement (p<0.001) and at rest (p=0.041), and the prevalence of chest pain (p<0.001) had a positive linear relationship to increased mMRC score.

An increasing mMRC score reflects impaired HRQoL and a high symptom burden. In clinical practice, the mMRC scale could be used for screening and identification of IPF patients with increased need for palliative care.

Short abstract

mMRC indicates impaired HRQoL and pain in IPF http://ow.ly/oRB430gIW7U

Introduction

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive and severe disease of unknown cause, seen primarily in older adults [ 1 ]. Even with recent advances in pharmacological treatment, IPF is still a disease with a high morbidity and poor survival [ 2 – 4 ]. As the disease trajectory in IPF is comparable to many advanced malignant disorders, guidelines recommend early-integrated palliative care in addition to pharmacological treatment and referral for lung transplantation [ 5 , 6 ].

Patients with IPF suffer from difficult symptoms, of which breathlessness and cough are the most common ones [ 7 , 8 ]. In addition, there is some evidence that IPF patients frequently experience pain, although the location and mechanism of the pain have not been reported [ 7 ]. Comorbidities are frequently reported in IPF patients, as shown in a recent study, where 88% of the patients had at least one and 30% more than four other diagnoses [ 9 ]. The total number of comorbidities and especially the occurrence of cardiovascular disease are associated with increased mortality [ 9 – 12 ].

There exist a limited number of studies on the heath-related quality of life (HRQoL) of IPF patients in a real-life setting [ 13 ]. Most recent studies have either concentrated on pharmaceutical treatment or have included a very limited number of patients [ 7 , 13 ]. However, there are clear indications of a decreased HRQoL in IPF patients [ 13 , 14 ].

The primary aim of this cross-sectional study was to describe the HRQoL and symptom burden among IPF patients derived from a national IPF registry (FinnishIPF). The secondary aim was the identification of patients with increased need for palliative care by investigating the relationship between dyspnoea score and HRQoL.

Materials and methods

Study population.

The FinnishIPF study is a prospective national clinical registry study of IPF patients initiated in 2012. IPF diagnosis is made according to the American Thoracic Society/European Respiratory Society/Japanese Respiratory Society/Latin American Thoracic Society 2011/2015 criteria [ 1 , 6 ]. In Finland, practically all IPF patients are initially evaluated in public hospitals (university and central hospitals). The FinnishIPF registry consists of all IPF patients from specialist centres who have given their informed consent to participate to the study. K aunisto et al . [ 2 ] have published a detailed description of the FinnishIPF study. Overall, 76% of confirmed IPF patients have given consent to participate to the study [ 2 ].

This study was initiated in April 2015, when all 300 patients registered to FinnishIPF study at that time were contacted and asked for a written informed consent to participate in this substudy. The questionnaires were sent to the patients with the consent form. The patients who did not respond within 2 weeks were contacted by telephone and reminded to answer to the questionnaire.

Data collection and questionnaires

Sociodemographic and disease characteristics were collected from patient records and by a separate questionnaire. Collected data included age, sex, date of birth, marital status, living conditions, education, physical activity, the need for help in daily activities, the date of IPF diagnosis, comorbidities and smoking status. The participants' exercise habits during the preceding 6 months (≥30 min at least moderate-intensity leisure time physical exercise, i.e. causing breathless and sweating) were asked.

The specific questionnaires of symptoms and HRQoL were modified Edmonton Symptom Assessment Scale (ESAS), modified Medical Research Council (mMRC) dyspnoea scale and the RAND 36-Item Health Survey (RAND-36).

The ESAS is a self-rated, numeric-rating, symptom-based scale developed for assessing the symptoms of cancer patients [ 15 ]. ESAS measures different symptoms on a scale from 0 (no symptoms) to 10 (the worst possible symptoms) [ 16 , 17 ]. In this study, we used a modified version, including 12 questions on symptoms, one question on general wellbeing and a standardised body diagram on which patients could mark the areas of pain.

The mMRC scale is a self-rating tool to measure the degree of disability that breathlessness poses on day-to-day activities on a scale from 0 to 4: 0, no breathlessness except on strenuous exercise; 1, shortness of breath when hurrying on the level or walking up a slight hill; 2, walks slower than people of same age on the level because of breathlessness or has to stop to catch breath when walking at their own pace on the level; 3, stops for breath after walking ∼100 m or after few minutes on the level; and 4, too breathless to leave the house, or breathless when dressing or undressing [ 18 , 19 ].

The RAND-36 [ 20 ] is a general HRQoL measurement tool, for which Finnish general population reference values exist [ 21 ]. The Short Form-36, which is commonly used in IPF patients, is similar to RAND-36 [ 21 ]. It is divided into eight health concepts, as explained by H ays et al. [ 20 ] and A alto [ 21 ], with scale from 0 to 100 (lower score meaning worse HRQoL). The concepts are: “physical functioning” (10 questions from ability to move and exercise to the ability to take care of personal hygiene), “role physical” (four questions on role limitations due to physical health), “bodily pain” (two questions), “general health” (five questions), “vitality” (four questions on energy level and tiredness), “social functioning” (two questions), “role emotional” (three questions on role limitations due to emotional problems) and “mental health” (five questions on anxiety, depression and mood) during the past 4 weeks [ 20 , 21 ].

Statistics and ethical aspects

The data are presented as mean± sd or n (%). The statistical significance for the hypothesis for linearity across groups in RAND-36 domains and symptoms were determined by ANCOVA and logistic regression analysis with an appropriate contrast (orthogonal polynomial). In the case of violation of the assumptions ( e.g. non-normality), a bootstrap-type test was used. The normality of the variables was tested by using the Shapiro–Wilk W-test. Stata 14.1 (StataCorp LP, College Station, TX, USA) was used for the analysis.

The ethical committee of Helsinki University Central Hospital (Helsinki, Finland) approved this study (381/13/03/01/2014). Permission to screen hospital registries for patients with IPF was approved by the Finnish National Institute for Health and Welfare (Dnro THL/1161/5.05.01/2012). All patients who participated to this study gave a written informed consent to participate this substudy.

Of 300 registered patients, 47 were excluded: 42 did not want to participate or did not answer our questionnaire; one received lung transplantation and one was found not to be IPF patient, so these two also were excluded; three patients died before they answered.

Patient characteristics

The patient characteristics are shown in table 1 . The mean duration of IPF at the time of the study entry was 3.9 years. At least one comorbidity was reported in 79% (n=200) and more than two comorbidities in 30% (n=77) of the patients, respectively. 37% of the patients had performed at least moderate-intensity leisure time physical exercise for ≥30 min a week during the last 6 months, whereas 21% had not been engaged in any physical exercise. A majority (65%) of the patients did not need help in everyday life, whereas 26% had received assistance in their daily routines. The remaining patients ( 9% ) did not receive help but considered themselves to be in need of it.

TABLE 1

Data are presented as mean± sd unless otherwise stated. IPF: idiopathic pulmonary fibrosis; FVC: forced vital capacity; COPD: chronic obstructive pulmonary disease. # : smoking status and FVC were recorded at the time of diagnosis, and other factors at the time of questionnaire; ¶ : including three patients with lung cancer.

mMRC for breathlessness

The severity of breathlessness on exertion reported by mMRC score was 0 (no breathlessness) in 33 (13%), 1 (breathless when hurrying or walking up a hill) in 88 (35%), 2 (breathless when walking slower than people of same age or has to stop when walking) in 75 (30%), 3 (breathlessness stops walking after ∼100 m or a few minutes) in 34 (13%) and 4 (breathless when dressing or not able to leave the house) in 23 (9%) of the patients.

RAND-36 for HRQoL

The different dimensions of HRQoL measured by RAND-36 are presented in table 2 . There was a linear relationship between impaired HRQoL and all RAND-36 dimensions and a higher mMRC score (linearity p<0.001) ( figure 1 ). All HRQoL dimensions of RAND-36 were significantly impaired in patients with mMRC 2–4 as compared to the general population except “bodily pain”, which was significantly below the general population level only in patients with mMRC score 4 ( figure 1 ). Physical dimensions (“physical functioning” and “role physical”) were the most impaired ones. “Role physical” derives from four questions in the questionnaire and reflects limitations in everyday life due physical health problems [ 20 , 21 ].

TABLE 2

Symptoms by Edmonton Symptom Assessment Scale (ESAS) questionnaire and health-related quality of life by RAND 36-Item Health Survey (RAND-36)

Data are presented as mean± sd unless otherwise stated. # : numeric rating scale, 0–10.

Health-related quality of life measured by the RAND 36-Item Health Survey according to modified Medical Research Council (mMRC) dyspnoea scale groups. Data are presented as mean values with 95% confidence intervals. Values adjusted for age, sex, comorbidities, education and living status. Dashed lines mark Finnish general population levels.

ESAS for symptoms

The prevalence and mean intensity of symptoms as measured by ESAS are shown in table 2 . There was positive linear relationship between the intensity of all symptoms in ESAS questionnaire and increasing mMRC breathlessness score ( figure 2 ).

Symptoms measured by Edmonton Symptom Assessment Scale according to modified Medical Research Council (mMRC) dyspnoea scale groups. Data are presented as mean numeric rating scale (NRS) values with 95% confidence intervals. Values adjusted for age, sex, comorbidities, education and living status.

A striking increase in pain intensity in movement (p<0.001) and, to lesser extent, at rest (p=0.041) was found with an increased mMRC score ( figure 2 ). The prevalence of pain in different locations of body diagram according to mMRC groups is shown in table 3 . The prevalence of chest pain and increasing mMRC score showed a positive linear relationship (linearity p<0.001).

TABLE 3

Localisation of pain

mMRC: modified Medical Research Council dyspnoea scale. # : for linearity, adjusted for age, sex, comorbidities, education and living status; ¶ : four or more of the seven pain areas marked by the patient.

This was a cross-sectional, real-life study of the quality of life and symptoms of IPF patients. Our results show that increased breathlessness as measured by the mMRC questionnaire is related to impaired HRQoL and symptom burden. In addition to breathlessness and cough, pain in movement was detected in a majority of the patients. However, only chest pain had a linear relationship with increased mMRC breathlessness score. We suggest that pain and, more importantly, chest pain, may be an underdiagnosed symptom of IPF.

In our study, the HRQoL of IPF patients with at least moderate shortness of breath (mMRC ≥2), was impaired in all areas of HRQoL, especially physical function, when compared to the Finnish general population [ 21 ]. Our findings are in line with an American Internet survey of 220 IPF patients in which HRQoL was measured with PROMIS-29 [ 22 ]. A correlation between mMRC scores and all domains except sleep disturbance was found [ 22 ]. In a small, cross-sectional, longitudinal study of 32 Japanese IPF patients, lower scores were reported in all eight domains (HRQoL questionnaire SF-36) when compared to the general population [ 13 ]. Similarly, in another small observational validation study of 34 IPF patients, a decline was seen in seven of the eight measured domains of SF-36 compared to sex- and age-matched controls [ 14 ]. That particular study also showed correlation between baseline dyspnoea index and five SF-36 components: physical functioning, general health perceptions, vitality, social functioning and mental health [ 14 ]. Even though there was a significant correlation between baseline dyspnoea index and pulmonary function parameters, dyspnoea index seemed to predict HRQoL more sensitively than pulmonary function parameters [ 14 ]. Dyspnoea in daily living, measured by mMRC, is also stronger prognostic parameter than most physiological markers in the diagnostic phase of IPF [ 23 ]. N ishiyama et al. [ 23 ] showed that low arterial oxygen saturation in a 6-min walk test and mMRC score were the strongest predictors of IPF patient's survival.

In line with the American Internet survey, increasing mMRC score was related to the symptom burden of IPF patients in our study [ 22 ]. The three most common symptoms in our study were tiredness, shortness of breath and cough, which are in line with earlier findings [ 7 ]. Interestingly, however, pain in movement was the next most common symptom reported by the majority of our patients, and pain in rest was the sixth most common symptom, present in two thirds of the patients. In a Swedish register study of oxygen-dependent interstitial lung disease patients, pain was reported in 51% of the patients [ 7 ]. Similarly to our findings, Y ount et al . [ 22 ] demonstrated an association between dyspnoea severity in mMRC score and intensity of pain. In another small observational study, no correlation between baseline dyspnoea index and pain index was found [ 14 ]. These differences could be related to different stage of the disease in different study populations.

In our study, every third patient reported chest pain, which also had linear relationship to the intensity of breathlessness measured by mMRC. Unspecified thoracic pain has been reported in pulmonary sarcoidosis and chronic obstructive pulmonary disease but, to our knowledge, this is the first study to report chest pain in IPF [ 24 , 25 ]. The exact aetiology of chest pain in IPF falls beyond the scope of our study, and should be an aim of further studies. However, as the relationship between chest pain and breathlessness was maintained after adjusting for comorbidities and age, the results suggest that chest pain may be a symptom related to IPF itself. This finding should be taken into account when considering diagnostic tests and treatment strategies for patients with advanced IPF.

Study limitations

The cross-sectional nature of the study limits our results to a single time-point and does not allow us to describe the changes in symptoms or HRQoL over time. Our cohort may be subjected to some selection bias, as some patients at a very advanced stage of the disease or close to death are likely to be lost from the cohort. Another limitation is that although the diagnosis of IPF was made by pulmonologists according to international guidelines, there was no central confirmation of the diagnoses. The strength of our study is a relatively large population of IPF patients in different phases of disease trajectory, evaluated by several assessment tools in real-life setting, and a high response rate.

Conclusions

Pain is a relatively common symptom in IPF. In particular, chest pain is related to increasing mMRC score. This could indicate a causal relationship between chest pain and progressive IPF, but further studies are necessary to confirm and explain these findings. Our results show that mMRC not only reflects breathlessness in patients with IPF but indicates HRQoL and overall symptom burden. The HRQoL was significantly deteriorated and symptom burden rose in patients with mMRC score ≥2. Thus, mMRC could be used as a simple screening tool for palliative care needs of IPF patients.

Disclosures

Acknowledgements.

We are grateful for the patients that consented to participate in this study. The authors express gratitude to the participants of the FinnishIPF consortium: R. Kaarteenaho (University of Oulu, Oulu, Finland), S. Saarelainen (Tampere University Hospital, Tampere, Finland), H. Kankaanranta (Seinäjoki Central Hospital, Seinäjoki, Finland), A. Böök (Satakunta Central Hospital, Pori, Finland), E.R. Salomaa (University of Turku, Turku, Finland), J. Kaunisto (University of Turku, Turku, Finland), U. Hodgson (Helsinki University Central Hospital, Helsinki, Finland) and M. Purokivi (Kuopio University Hospital, Kuopio, Finland). The authors are also immensely grateful to the numerous pulmonary physicians who have contributed to the study by including patients and seeking informed consent: J. Vaden (Hämeenlinna Hospital, Hämeenlinna, Finland), M. Pekonen (Kanta-Häme Central Hospital, Hämeenlinna, Finland), H. Tapanainen (Hyvinkää Hospital, Hyvinkää, Finland), H. Lajunen (Jämsä Hospital, Jämsä, Finland), A. Saarinen (Seinäjoki Central Hospital, Seinäjoki, Finland), U. Suuronen (Etelä-Karjala Central Hospital, Lappeenranta, Finland), L. Lammi (Päijät-Häme Central Hospital, Lahti, Finland), K. Lehtonen (Pohjois-Kyme Hospital, Kouvola, Finland), J. Männistö (Kymeenlaakso Central Hospital, Kotka, Finland), I. Salmi (Pohjois-Karjala Central Hospital, Joensuu, Finland), M. Torkko (Etelä-Savo Central Hospital, Mikkeli, Finland), P. Torkko (Etelä-Savo Central Hospital, Mikkeli, Finland), M. Erkkilä (Savonlinna Central Hospital, Savonlinna, Finland), H. Andersen (Vaasa Central Hospital, Vaasa, Finland), J. Jaakkola (Pietarsaari Hospital, Pietarsaari, Finland), H. Rinne (Keski-Pohjanmaa Central Hospital, Kokkola, Finland), M-L. Alho (Rauma Hospital, Rauma Finland), M. Pietiläinen (Satkunta Central Hospital, Pori, Finland), T. Toljamo (Lapland Central Hospital, Rovaniemi, Finland), M. Palomäki (Kainuu Central Hospital, Kajaani, Finland), E. Nylund (Kainuu Central Hospital, Kajaani, Finland), E. Ahonen (Kainuu Central Hospital, Kajaani, Finland), P. Impola (Oulaskangas Hospital, Oulainen, Finland), S. Saviaro (Länsi-Pohja Central Hospital, Kemi, Finlan), L. Pusa (Raasepori Hospital, Raasepori, Finland), S. Vilkman (Porvoo Hospital, Porvoo, Finland), H. Ekroos (Porvoo Hospital, Porvoo, Finland), P. Vuori (Lohja Hospital, Lohja, Finland), J. Hedman (Etelä-Karjala Central Hospital, Lappeenranta, Finland), M. Lahti (Jokilaakso Hospital, Jämsä, Finland) and A. Mursu (City Hospital of Oulu, Oulu, Finland).

K. Rajala, J.T. Lehto, E. Sutinen, T. Saarto and M. Myllärniemi designed this study. K. Rajala, E. Sutinen and M. Myllärniemi were responsible for data collection. All authors analysed the data, drafted the manuscript, and read and approved the final manuscript. K. Rajala takes responsibility for the whole work.

A part of the results presented in this article have been presented as an abstract and poster at the 15th world Congress of the European Association for Palliative Care, May 18–20, 2017, Madrid, Spain.

Support statement: The Academy of Finland, the Sigrid Jusélius Foundation, the Foundation of the Finnish Anti-Tuberculosis Association and a governmental subsidy for health sciences research have supported Lung Factor research group. The sponsors had no role in the design of the study, the collection and the analysis of the data, or the preparation of the manuscript. Funding information for this article has been deposited with the Crossref Funder Registry .

Conflict of interest: Disclosures can be found alongside this article at openres.ersjournals.com