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Artificial intelligence to differentiate asthma from
COPD in medico-administrative databases

Hassan Joumaa, Raphaël Sigogne, Milka Maravic, Lucas Perray, Arnaud

Bourdin, Nicolas Roche

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► To cite this version:

Hassan Joumaa, Raphaël Sigogne, Milka Maravic, Lucas Perray, Arnaud Bourdin, et al. Artificial intelligence to differentiate asthma from COPD in medico-administrative databases. BMC Pulmonary Medicine, 2022, 22 (1), pp.357. 10.1186/s12890-022-02144-2. hal-03782952

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# Artificial intelligence to differentiate asthma from COPD in medico-administrative databases

Hassan Joumaa<fnref n="1" />*, Raphaël Sigogne², Milka Maravic²,³, Lucas Perray², Arnaud Bourdin⁴,⁵ and Nicolas Roche<fnref n="1" />,⁶

**Abstract**

**Introduction:** Discriminating asthma from chronic obstructive pulmonary disease (COPD) using medico-administrative databases is challenging but necessary for medico-economic analyses focusing on respiratory diseases. Artificial intelligence (AI) may improve dedicated algorithms.

**Objectives:** To assess performance of different AI-based approaches to distinguish asthmatics from COPD patients in medico-administrative databases where the clinical diagnosis is absent. An "Asthma COPD Overlap" category was defined to further test whether AI can detect complexity.

**Methods:** This study included 178,962 patients treated by two "R03" treatment prescriptions at least from January 2016 to December 2018 and managed by either a general practitioner and/or a pulmonologist participating in a permanent longitudinal observatory of prescription in ambulatory medicine (LPD). Clinical diagnoses are available in this database and were used as gold standards to develop diagnostic rules. Three types of AI approaches were explored using data restricted to demographics and treatment dispensations: multinomial regression, gradient boosting and recurrent neural networks (RNN). The best performing model (based on metric properties) was then applied to estimate the size of asthma and COPD populations based on a database (LRx) of treatment dispensations between July, 2018 and June, 2019.

**Results:** The best models were obtained with the boosting approach and RNN, with an overall accuracy of 68%. Performance metrics were better for asthma than COPD. Based on LRx data, the extrapolated numbers of patients treated for asthma and COPD in France were 3.7 and 1.2 million, respectively. Asthma patients were younger than COPD patients (mean, 49.9 vs. 72.1 years); COPD occurred mostly in men (68%) compared to asthma (33%).

**Conclusion:** AI can provide models with acceptable accuracy to distinguish between asthma, ACO and COPD in medico-administrative databases where the clinical diagnosis is absent. Deep learning and machine learning (RNN) had similar performances in this regard.

**Keywords:** Algorithms, COPD, Chronic obstructive pulmonary disease, Asthma, ICD code, Epidemiology, Prevalence, Healthcare administrative databases

**Introduction**

When they rely on adequate data, database studies can provide useful insights on disease burden as well as treatment effectiveness and safety in real-life, thereby contributing to guide decision-makers. However, such studies provide reliable disease-specific data only if the criteria applied to select patients' populations are sufficiently robust to differentiate one disease from the other. This

<footnote>*Correspondence: hassan.joumaa@aphp.fr</footnote>

<footnote n="1">¹ Department of Respiratory Medicine, Cochin Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France</footnote>
concern is particularly relevant in the asthma/COPD field, since these two diseases share some similarities but also exhibit many differences that have (or should have) a major impact on clinical decision-making.

Originally described under the Dutch vs British hypotheses, similarities and differences in the origins and mechanisms underlying asthma and COPD are still debated and a patient's multi-criteria follow-up is probably the best ultimate way of discriminating the two diseases in difficult diagnostic situations. The main risk of misdiagnosis is to prevent a patient with asthma features from receiving inhaled corticosteroids: this may overtake the risks of unnecessary inhaled corticosteroids (ICS) use in COPD [1, 2], which appears frequent. In addition, identifying each entity could help identifying specific pathways and their corresponding treatment targets in the future, whereas merging the two may compromise this opportunity. A treatable trait approach has been proposed to guide treatment individualization but is still often perceived as complex to address for non-specialist clinicians and epidemiologists as well as payers.

One mission devoted to pharmaco-epidemiologists is to provide policy makers and other stakeholders with correct estimations of epidemiological trends, disease burden and resource consumption, clinical practice and size of medication-specific target populations. Databases can also be used for effectiveness and comparative effectiveness studies in real-life patients. The required precision of estimates and their specificity actually depend on the goal of the analyses. Currently, the various sources of data available for pharmaco-epidemiological studies face different strengths and weaknesses depending on whether they rely on prescriptions, international classification of diseases (ICD) coding or registries and databases specifically dedicated to asthma and COPD. The French "Système National des Données de Santé" (national health data hub) is reported as the world largest health database but diagnoses are not provided and thus need to be indirectly deduced from medication use and/or from refunded acts. In the United-Kingdom, general practitioners (GPs) databases are less exhaustive but enriched by diagnostic codes and clinical information. However, diagnoses provided by GPs sometimes change over time and do not always match with a secondary care diagnosis. Thus, at the end establishing the correct diagnosis based on a minimal amount of information collected in non-specialized clinics remains a real challenge despite the numerous available guidelines documents on asthma and COPD diagnosis and treatment [3].

Nowadays, artificial intelligence tools and computer based methods are on the rise and are gradually improving the quality of care by supporting physicians for the diagnosis as well as for the management and follow up

[4–6]. Many types of algorithms have been used since the 1990s, such as artificial neural networks (ANNs), fuzzy logic (FL), Random Forests, Gradient Boosting and Logistic Regression. In the respiratory field they have been used, e.g., for the detection and classification of different types of pulmonary diseases and to predict the risk of exacerbation [7–9]. Machine learning and deep learning can both be used to build algorithms using data from medico-administrative databases [10]. Machine learning can use logistic regression models (or multinomial for multiclass classification) or boosting models (which are more adapted to assess interactions between variables but also more complex) [11]. Deep learning and especially recurrent neural networks (RNN) is adapted to longitudinal repetition of data, and therefore to sequences of health care delivery [12].

The objective of this study was to develop an algorithm able to identify asthma and COPD patients using a minimum set of data shared by medico-administrative databases. The performance of AI was tested against clinical data provided by general practitioners and pulmonologists, used as reference.

# Methods

## Study design

This was a retrospective observational database study using a medicalized and a non medicalized data source; the first represented by longitudinal patient database (LPD) and the second by lifelink treatment dynamics (LRx). A diagram illustration of the study design is represented in Fig. 1.

## Data sources

The first data source is LPD (longitudinal patient database), a database of electronic medical records from a representative computerized sample of 2,500 general physicians (GPs) and 70 pulmonologists, all office-based in private clinical practice and representative of the overall French population according to age, gender, type of practice (partial/global private activity), and geographical area of activity. Data were collected in real time during the consultation via a dedicated medical software [13]. The second source of data contains all anonymized medication dispenses prescribed in ambulatory care in a panel of 10,000 French retails pharmacies since 2012, named LRx database (Lifelink Treatment dynamics). The panel represents nearly 45% of the French retails pharmacies and is representative in terms of geographical spread in continental France and age of population coverage [14], allowing extrapolation to the overall French population. Table 1 describes the information available in each database and shared by both databases. Medical diagnoses contained in the LPD database have been used
<figure><img src="image_4.png" /></figure>

as reference to develop and validate an algorithm (using
only demographics and treatment patterns) that could be
subsequently applied to LRx data (where medical diag-
noses are absent) to identify and differentiate patients
treated for asthma and COPD in France.

LPD dataset

Selected patients were those with at least 2 R03 treat-
ment prescriptions (i.e. treatments for obstructive air-
way diseases, https://www.whocc.no/) over a period of
365 consecutive days from 2016/01/01 to 2018/12/31.
<table><caption>**Table 1** Information available in LPD and LRx database</caption><thead><tr><th></th><th>Only available in LPD database</th><th>Only available in LRx Database</th><th>Shared by both databases</th></tr></thead><tbody><tr><td>Prescribers' characteristics</td><td>Age, gender, geographical area</td><td>Geographical area</td><td>Specialty</td></tr><tr><td>Patients' characteristics</td><td>Unique identification number</td><td>Unique identification number</td><td>Year of birth, gender</td></tr><tr><td>Visit</td><td>Date, diagnosis</td><td>–</td><td>–</td></tr><tr><td>Treatments' characteristics</td><td>Number of packs, duration of the prescription and renewal</td><td>Dispensing date, dispensed volume</td><td>Prescription date, packaging, dosage, drug code (CIP)</td></tr></tbody></table>

CIP code identifiant de présentation (an unique code taking into account drug packaging), LPD longitudinal patient database, LRx Lifelink treatment dynamics

Each patient was assigned to one of the four following categories: patients diagnosed with both asthma and COPD: label “Both” diseases, those with asthma only, label “Asthma”, those with COPD only: label “COPD”, and those with neither asthma nor COPD diagnosis: label “other”. The dataset of each category was split into 3 parts, 80% used to train the model, 10% to validate it, and 10% to test it. We checked that data were homogeneous in each data partition thanks to a stratify split used on the patients’ label.

## Models development and selection

As mentioned previously, we built diagnostic algorithms using LPD, based on the information common to both databases, i.e., patient's profile (age, gender), prescriber's profile (medical specialty), and prescription's information (date, product prescribed). Importantly, models are built based on longitudinal data collected during a 2-year follow-up. Three types of models were used: two using classic machine learning approaches, i.e. logistic regression and boosting models, and the last based on a deep learning approach, i.e. RNN. A confusion matrix was established for each model. We then selected the best model based on predictive accuracy in the test set. Based on this model, we described the demographic characteristics of the population and the top 5 treatments for each label (asthma, COPD, both, other). Additional information regarding the methodological approach is available in the supplementary material (Additional file 1: Technical Appendix).

## Prediction in the LRx database

The best model identified using LPD was applied to LRx to estimate asthma and COPD populations in this database over the period from 2018/07/01 to 2019/06/31.

## Statistical analysis

Performance metrics of best models are described using recall (i.e. sensitivity), specificity, precision (i.e. positive predictive value), negative predictive value, and F1 score. The recall is the ratio of true positives found within the population (recall = $\frac{tp}{tp+fn}$ where tp is

the number of true positives and fn is the number of false negatives). The precision is the ratio of true positives within the positive predicted population (precision = $\frac{tp}{tp+fp}$ where tp is the number of true positives and fp is the number of false positives). F1 Score is a weighted average of both the recall and precision ($f1score = 2\frac{recall*precision}{recall+precision}$).

We performed demographic description of the predicted asthma and COPD patients in France on LRx.

## Results

### Patients

In LPD, selection criteria provided a training dataset of 178,962 patients with 1,706,130 prescriptions. Among these patients, 43%, 16%, 4%, and 37% had a diagnostic of asthma, COPD, both, and other respiratory conditions. The patients' demographic characteristics and the top 5 treatments belonging to the R03 treatment prescriptions were described in Table 2.

### Model selection

Figure 2 and Table 3 showed confusion matrices of each model and performance metrics, respectively. The best models were obtained with the boosting approach and RNN, with an overall accuracy of 68%. Recall, precision and F1 score were better for asthma than COPD, while patients with other diagnoses have the worst performance metrics. Indeed, considering boosting or RNN approach, the recall was 83% and 64% for asthma and COPD, respectively; precision was 71% and 66%, respectively. As RNN did not provide any additional performance, the boosting approach was further selected in order to predict asthma and COPD populations on LRx.

### Predicted asthma and COPD populations (LRx)

Based on LRx data, the extrapolated numbers of patients treated for asthma and COPD in France were 3.7 and 1.2 million, respectively. Patients classified as asthma were younger than COPD patients, 49.6 (25.3) years vs 72.1 (11.7) years. A male predominance was observed in COPD (68%), while men represented only 33% of patients
<table><caption>**Table 2** LPD training data: Demographics and the top 5 treatment prescription (178,962 patients)</caption><thead><tr><th></th><th>Asthma</th><th>COPD</th><th>Both</th><th>Other</th></tr></thead><tbody><tr><td><em>Demographics characteristics</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Number of patients</td><td>76,953</td><td>28,635</td><td>7,158</td><td>66,216</td></tr><tr><td>Mean age (SD):</td><td>44 (25)</td><td>67 (12)</td><td>63 (16)</td><td>51 (25)</td></tr><tr><td>% female</td><td>56</td><td>40</td><td>53</td><td>54</td></tr><tr><td><em>Top 5 classes of treatments (% of patients)</em></td><td></td><td></td><td></td><td></td></tr><tr><td>1</td><td>ICS/LABA: 68</td><td>ICS/LABA: 54</td><td>ICS/LABA: 76</td><td>ICS monotherapy: 54</td></tr><tr><td>2</td><td>ICS monotherapy: 39</td><td>LAMA: 47</td><td>LAMA: 47</td><td>ICS/LABA: 47</td></tr><tr><td>3</td><td>LTRA: 25</td><td>LABA/LAMA: 33</td><td>ICS monotherapy: 32</td><td>LTRA: 14</td></tr><tr><td>4</td><td>LABA: 7</td><td>LABA: 21</td><td>LTRA: 22</td><td>LAMA: 10</td></tr><tr><td>5</td><td>LAMA: 7</td><td>ICS monotherapy: 18</td><td>LABA/LAMA: 26</td><td>LABA: 7</td></tr></tbody></table>

COPD chronic obstructive pulmonary disease, ICS inhaled corticosteroids, LABA long-acting beta-2 agonist, LAMA long-acting muscarinic antagonist, LPD longitudinal patient database, LTRA leukotriene receptor antagonist

*ICS/LABA considered as a single drug including both product

<figure><img src="image_5.png" /><figcaption>**Table 3** Performance metrics for each disease category – Boosting model/RNN</figcaption></figure>

<table><thead><tr><th></th><th>Recall (= sensibility) (%)</th><th>Specificity (%)</th><th>Precision (= positive predictive value) (%)</th><th>Negative predictive value (%)</th><th>F1 score (%)</th></tr></thead><tbody><tr><td><em>Boosting model</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Asthma</td><td>83</td><td>63</td><td>71</td><td>77</td><td>76</td></tr><tr><td>COPD</td><td>65</td><td>90</td><td>61</td><td>90</td><td>63</td></tr><tr><td>Other</td><td>54</td><td>76</td><td>66</td><td>65</td><td>60</td></tr><tr><td><em>RNN model</em></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Asthma</td><td>83</td><td>62</td><td>71</td><td>77</td><td>76</td></tr><tr><td>COPD</td><td>65</td><td>91</td><td>66</td><td>92</td><td>65</td></tr><tr><td>Other</td><td>64</td><td>84</td><td>53</td><td>77</td><td>58</td></tr></tbody></table>

COPD chronic obstructive pulmonary disease, RNN recurrent neural network

with asthma. The number of patients considered as having both diseases was 0.4 million (mean age 69.9 (14.2), 52% male).

## Discussion

The purpose of the present project was to explore whether artificial intelligence applied to a minimal
medico-administrative dataset limited to demograph-
ics and treatments could provide accurate identifica-
tion of patients with asthma and COPD. First, three
artificial intelligence tools (multinomial regression,
gradient boosting and recurrent neural networks) were
applied to a medicalized database (LPD), as a reference,
to develop and test diagnostic models with the clin-
cal diagnosis as a gold standard. The overall accuracy
was similar (68%) for all models, suggesting that deep
learning does not perform better than machine learn-
ing for that purpose. Second, applying the best model
to a large representative non-medicalized database
(LRx), allowed predicting the number of patients who
receive treatments for either asthma, COPD, or both in
the whole French population.

The differential diagnosis between asthma and COPD
is made difficult by the lack of a single test individually
allowing to differentiate these two diseases reliably. More
specifically, lung function criteria (bronchial hyperreac-
tivity for asthma, not fully reversible airflow limitation for
COPD) are not sufficiently discriminative. Consequently,
the diagnosis relies on a combination of clinical and lung
function criteria. Among these, the clinical history plays
a major role, although most questionnaires developed
to date do not perform fully satisfactorily [15, 16]. Com-
plicating diagnostic challenges even more, asthma and
COPD can coexist. In 2015, the strategy updates of the
Global Initiatives for COPD (GOLD) and asthma (GINA)
even identified the “asthma COPD overlap”, a group of
patients characterized by persistent airflow limitation
with common features of both asthma and COPD like a
history of allergy, exposure to tobacco smoking or air pol-
lution and persistent respiratory symptoms with notice-
able variability [17, 18]. In their most recent updates,
GOLD and GINA don’t put forward anymore the use of
the term “ACO”, instead stressing that asthma and COPD
are different disorders even if they may share common
features and be associated [2, 19, 20].

Asthma and COPD are considered by some authors
as two extremes of a single continuum [21, 22], along
which patients could be rather characterized using treat-
able traits than disease labels. From a clinical perspective,
differentiating asthma from COPD has long been [23]
and remains for now crucial considering the differences
between these two conditions in terms of pathogenesis,
natural history, prognosis and therapeutic targets [24].
Differentiating the two conditions in medico-administra-
tive databases is therefore of interest, e.g., to estimate the
prevalence of physician-diagnosed and treated asthma/
COPD and the corresponding health care costs, as well as
to describe and follow over time real-life clinical care for
these two conditions. Such information can prove valu-
able to clinicians, health policy makers, health coverage

systems and insurance companies, and to understand the
impact of such diseases on the population.

Unfortunately, detailed clinical data is not available in most medico-administrative databases to differentiate these two diseases. An accuracy of 68% can be considered as acceptable for a tool designed to differentiate between asthma and COPD without entering in all the complexity of a medical file or patient's diagnostic data. As such, this algorithm could be utilized as a surrogate to ICD codes when they are not available to identify asthma and COPD patients [25].

Extrapolation allowed to estimate the prevalence and
demographics of asthma and COPD in the general popu-
lation, with figures consistent with previous epidemiolog-
ical studies using reference methods [26–29]. Our study
found gender-related heterogeneity in the prevalence of
COPD, asthma and both, which is also consistent with
previous publications [30, 31] and could be explained by
gender-related differences in the prevalence of cigarette
smoking and exposure to other environmental risk fac-
tors, as well as in susceptibility [32].

Since there is a great overlap regarding recommended
pharmacological treatments between asthma and COPD,
it is not surprising that the medications included in
Table 2 retrieved from LRx are quite similar for asthma,
COPD or both. As expected, only long-acting broncho-
dilators without inhaled corticosteroids ICS (long-acting
beta-agonists (LABA) and long-acting muscarinic antag-
onists (LAMA), LABA/LAMA) proved more specific for
COPD, whereas leukotriene receptor antagonists (LTRA)
are used only for asthma management.

Our results are consistent with those of Riccardo Di
Domenicantonio et al. [35] who performed a systematic
review of case identification algorithms based on the Ital-
ian health care administrative database for asthma and
COPD [35]. They found that age class and chronic treat-
ment were the main disease-specific traits that emerged
from the algorithms with a lower accuracy of algorithms
based on drug prescriptions for COPD patients [35].
However, validation of these algorithms for asthma was
limited and provided highly variable results, while no
algorithm was clearly validated for COPD. Gothe et al.
[25], in another systematic review, also found that phar-
macotherapy data is the most reliable and richest source
of information available to identify COPD patient in an
outpatient setting when ICD codes are unavailable [25].
In the validation study of healthcare administrative data-
base algorithms to identify COPD published by Gershon
et al., age was also an important criterion [36].

In several studies, the performance of AI and machine
learning for the diagnosis of asthma and COPD was
higher than in our study. This difference can be eas-
ily explained by the content of the databases used: our
purpose was to develop an algorithm applicable to databases with no clinical data or diagnostic label. Conversely, studies that found better performance used more extensive data such as: results of spirometry, smoking status, physical examination and imaging [6, 33, 34]. For instance, Spathis and Valmos found 97.7 per cent diagnostic precision using random forest in COPD, relying on multiple elements such as smoking, age and spirometric data (FEV1 and FVC) [34].

Our results show that deep learning did not perform better than machine learning to build diagnostic algorithms. This represents important information for future medico-economic database studies in airway diseases. Indeed, RNN is usually adapted to longitudinal repetition of data, and sequences of health care delivery [37]. However, in our study, this approach did not bring additional value, underlining that the information used for the classical machine learning approach was enough to discriminate asthma from COPD. This could be explained by a training based on a dataset limited to outpatient care provided by a GP only or a pulmonologist only. Indeed, RNN could provide additional value when all the sequences of health care delivery are taken into consideration, but this remains to be further tested.

Our study has several strengths that contribute to its originality. Firstly, with only few data (i.e., sex, age and patients' medications), we were able to develop an algorithm with an acceptable accuracy for correct identification of asthma and COPD patients. In addition, the size and representation of the LRx database confer generalizability to the present results.

The study also faces several limitations. The relatively high proportion of patients receiving R03 treatment who are not classified by physicians illustrates the diagnostic difficulties and potentially decreases the relevance of our gold standard population. This observation also needs to be put in the perspective of the marked under-diagnosis of COPD and of the lack of individual verification of medical files. In addition, models trained using asthma only or COPD only patients could have a better accuracy (i.e. > 90%, data not shown), but the challenge of overtraining needs to be questioned regarding the reality of clinical practice. The arbitrarily-defined cutoff of 2 R03 prescriptions to identify the population used to develop the algorithm could also be questioned. A sensitivity analysis was performed with cutoffs of 3 or 4 R03 prescriptions; this did not have any impact on the confusion matrix whatever the method used (data not shown). As in many health services research, case verification using chart abstraction was used to validate case definitions of asthma, COPD or both [36]. Knowing that most charts came from GP's, the validity of our gold standard can

be questioned [38]. However, in this context, the gold standard needs to be a sequence of validation processes based on history and medical course. Indeed, both our gold standard and algorithm were not based only on a single medical visit and prescription, but on a continuum of follow up over 2 years.

Moreover, LPD database describes only the patient trajectory seen by a general practitioner or a pulmonologist while LRx database describes the overall management whatever the physician and type of clinical setting. Although this could introduce some heterogeneity, it does not decrease the value of results.

Finally, our algorithm was not tested against an expert clinical diagnosis. The purpose of real-life studies performed using such databases can be to analyze data from patients “treated as” asthma or COPD, or to consider patients with a confirmed expert diagnosis of asthma or COPD. Our results apply only to the first of these types of populations, and additional studies are needed to explore their relevance in patients with a “gold standard” diagnosis.

In conclusion, this database study showed that an algorithm with acceptable accuracy can be developed to identify asthma and COPD in medico-administrative databases from which the medical diagnosis is absent. Deep learning and machine learning had similar performances in this regard. Applied to such databases, the algorithm could prove useful to estimate the burden of these diseases and to analyze clinical practice over time. Further studies are required to test the model in other populations and refine the diagnostic criteria proposed here.

**Acknowledgements**

We thank Stéphane Gaiffas (Université Paris Diderot, Laboratoire de Probabilités, Statistique et Modélisation, Paris France) and Emmanuel Bacry (CEREMADE Université Paris-Dauphine, Paris, France) for their expertise and assistance throughout the methodological approach of this study.

**Author contributions**

AB, LP, MM, NR, and RS contributed to the design. RS and LP contributed to the implementation of the research. All authors contributed to the analysis of the results and to the writing of the manuscript.

**Funding**

No funding was given.
# Declarations

## Ethics approval and consent to participate

Access to and processing of data is done in compliance with applicable laws and regulations including General Data Protection Regulation (GDPR). LRx database was authorized by the French Data Protection Authority (CNIL) on the 21st of October 2011 [reference: DE-2011-097] and updated on July 2018 for compliance with the GDPR [reference: DE-2018-289] [Délibération 2018-289 du 12 septembre 2018—Légifrance (legifrance.gouv.fr), Deliberation no. 2018-289 of 12 July 2018 authorising IQVIA Opération France to implement automated processing of personal data for the purpose of creating a personal data warehouse for research, studies or evaluation purposes in the health sector, known as LRx database] LPD database is declared to the French Data Protection Authority in accordance with applicable laws and regulations (including GDPR) and was submitted to the CNIL for a specific authorization as a data warehouse in 2019, and was authorized on the February 4th 2021 [reference: DE-2021-015, CNIL, Délibération du 4 février 2021, no. 2021-015, Deliberation N°. 2021-015 of 4 February 2021 authorising IQVIA OPERATIONS FRANCE to implement automated processing of personal data for the purpose of a health data warehouse, called EMRJ. As mentioned in both authorizations, all data are pseudonymized before being used.

## Consent for publication

Not applicable.

## Competing interests

The authors declare no conflicts of interest.

## Author details

¹Department of Respiratory Medicine, Cochin Hospital, Assistance Publique - Hôpitaux de Paris (APHP), Paris, France. ²IQVIA, La Défense, France. ³Hôpital Lariboisière, Rhumatologie, Paris, France. ⁴PhyMedExp, INSERM U1046, CNRS UMR 9214, University of Montpellier, Montpellier, France. ⁵Department of Respiratory Medicine, Arnaud de Villeneuve Hospital, CHU Montpellier, Montpellier, France. ⁶University Paris Descartes (EA2511), Paris, France.

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