COLIBRI : optimiser la pratique clinique et produire des données scientifiques pertinentes

Insight into the relationship between forced vital capacity and transfer of the lungs for carbon monoxide in patients with idiopathic pulmonary fibrosis

BACKGROUND

Forced vital capacity (FVC) is routinely used to quantify the severity and identify the progression of idiopathic pulmonary fibrosis (IPF). Although less commonly used, lung transfer of carbon monoxide (TLCO) correlates better with the severity of IPF than does FVC.

METHODS

Aiming at studying how FVC behaves in relation to TLCO, we analysed cross-sectional data from 430 IPF patients, of which 221 had at least 2 assessments (performed 2.4 ± 1.9 years apart) available for longitudinal analyses. Thresholds for identifying "abnormal" FVC and TLCO values were the statistically-defined lower limits of normal (LLN). For patients with longitudinal data, mean annual absolute declines of FVC and TLCO were calculated.

RESULTS

The correlation between FVC and TLCO (%predicted) was weak (R2=0.21). FVC was "abnormal" (i.e.,

CONCLUSION

In IPF, a "normal" FVC should be viewed with caution as it is most often associated with an "abnormal" TLCO, a parameter that is strongly correlated with the morphological extent of the disease. Only 1/3 of the patients with a FVC-based progression criterion also had a TLCO progression criterion. In contrast, 2/3 of patients with a TLCO progression criterion also had a FVC progression criterion.

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Key Words

• Forced vital capacity
• Idiopathic pulmonary fibrosis
• Pulmonary function tests
• Transfer of the lungs for carbon monoxide

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MESH Headings

• Humans
• Carbon Monoxide
• Cross-Sectional Studies
• Lung
• Vital Capacity
• Idiopathic Pulmonary Fibrosis/diagnosis

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Affiliation(s)

Thibaud Soumagne Service de Pneumologie et Soins intensifs respiratoires, Hôpital Européen Georges-Pompidou, Assistance Publique des hôpitaux de Paris, Paris, France
Sébastien Quétant Service Hospitalier Universitaire Pneumologie Physiologie, Pôle Thorax et Vaisseaux, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
Alicia Guillien Épidemiologie environnementale appliquée à la reproduction et à la santé respiratoire, INSERM, CNRS, Université Grenoble Alpes, Institut pour l'Avancée des Biosciences (IAB), U1209, Grenoble, France
Loïc Falque Service Hospitalier Universitaire Pneumologie Physiologie, Pôle Thorax et Vaisseaux, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France
David Hess Programme Colibri, Association pour la complémentarité des connaissances et des pratiques de la pneumologie (aCCPP), 19 avenue Marcelin Berthelot, 38100, Grenoble, France
Bernard Aguilaniu Programme Colibri, Association pour la complémentarité des connaissances et des pratiques de la pneumologie (aCCPP), 19 avenue Marcelin Berthelot, 38100, Grenoble, France; Université Grenoble Alpes, Grenoble, France
Bruno Degano Service Hospitalier Universitaire Pneumologie Physiologie, Pôle Thorax et Vaisseaux, Centre Hospitalier Universitaire Grenoble Alpes, Grenoble, France; Laboratoire HP2, INSERM U1300, Université Grenoble Alpes, Grenoble, France.

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A machine learning approach to predict extreme inactivity in COPD patients using non-activity-related clinical data

Facilitating the identification of extreme inactivity (EI) has the potential to improve morbidity and mortality in COPD patients. Apart from patients with obvious EI, the identification of a such behavior during a real-life consultation is unreliable. We therefore describe a machine learning algorithm to screen for EI, as actimetry measurements are difficult to implement. Complete datasets for 1409 COPD patients were obtained from COLIBRI-COPD, a database of clinicopathological data submitted by French pulmonologists. Patient- and pulmonologist-reported estimates of PA quantity (daily walking time) and intensity (domestic, recreational, or fitness-directed) were first used to assign patients to one of four PA groups (extremely inactive [EI], overtly active [OA], intermediate [INT], inconclusive [INC]). The algorithm was developed by (i) using data from 80% of patients in the EI and OA groups to identify ‘phenotype signatures’ of non-PA-related clinical variables most closely associated with EI or OA; (ii) testing its predictive validity using data from the remaining 20% of EI and OA patients; and (iii) applying the algorithm to identify EI patients in the INT and INC groups. The algorithm’s overall error for predicting EI status among EI and OA patients was 13.7%, with an area under the receiver operating characteristic curve of 0.84 (95% confidence intervals: 0.75–0.92). Of the 577 patients in the INT/INC groups, 306 (53%) were reclassified as EI by the algorithm. Patient- and physician- reported estimation may underestimate EI in a large proportion of COPD patients. This algorithm may assist physicians in identifying patients in urgent need of interventions to promote PA.

Are there specific clinical characteristics associated with physician's treatment choices in COPD?

Background

The number of pharmacological agents and guidelines available for COPD has increased markedly but guidelines remain poorly followed. Understanding underlying clinical reasoning is challenging and could be informed by clinical characteristics associated with treatment prescriptions.

Methods

To determine whether COPD treatment choices by respiratory physicians correspond to specific patients’ features, this study was performed in 1171 patients who had complete treatment and clinical characterisation data. Multiple statistical models were applied to explain five treatment categories: A: no COPD treatment or short-acting bronchodilator(s) only; B: one long-acting bronchodilator (beta2 agonist, LABA or anticholinergic agent, LAMA); C: LABA+LAMA; D: a LABA or LAMA + inhaled corticosteroid (ICS); E: triple therapy (LABA+LAMA+ICS).

Results

Mean FEV1 was 60% predicted. Triple therapy was prescribed to 32.9% (treatment category E) of patients and 29.8% received a combination of two treatments (treatment categories C or D); ICS-containing regimen were present for 44% of patients altogether. Single or dual bronchodilation were less frequently used (treatment categories B and C: 19% each). While lung function was associated with all treatment decisions, exacerbation history, scores of clinical impact and gender were associated with the prescription of > 1 maintenance treatment. Statistical models could predict treatment decisions with a < 35% error rate.

Conclusion

In COPD, contrary to what has been previously reported in some studies, treatment choices by respiratory physicians appear rather rational since they can be largely explained by the patients’ characteristics proposed to guide them in most recommendations.

Electronic supplementary material

The online version of this article (10.1186/s12931-019-1156-1) contains supplementary material, which is available to authorized users.

COPD treatment pathways in France: a retrospective analysis of electronic medical record data from general practitioners

Background

Increasing availability of therapeutic options for COPD may drive new treatment pathways. This study describes COPD treatment in France, focusing on identifying initial treatment modifications in patients with COPD who either initiated long-acting bronchodilator (LABD)-based therapy or escalated to triple therapy (long-acting muscarinic antagonist [LAMA] + long-acting β₂-agonist [LABA] + inhaled corticosteroid [ICS]).

Methods

This retrospective analysis of patients with COPD in a large general practitioner database (IQVIA Longitudinal Patient Database) in France included two cohorts: Cohort 1 – new initiators of LABD-based therapy (LAMA, LABA, LAMA + LABA, LAMA + ICS, LABA + ICS or LAMA + LABA + ICS); Cohort 2 – patients escalating to triple therapy from mono- or dual-bronchodilator-based maintenance treatment. Both cohorts were indexed on the date of initiation/escalation (January 2008–December 2013), and the first treatment modification (at class level) within the 18-month post-index observational period was described. Five mutually exclusive outcomes were defined: continuous use (no modification), discontinuation (permanent [≥91 days with no restart] or temporary [≥91 days with subsequent restart]), switch, and augmentation (Cohort 1 only). Exploratory analysis of Cohort 1 explored potential drivers of treatment initiation.

Results

Overall, 5,065 patients initiated LABD-based therapy (Cohort 1), and 501 escalated to triple therapy (Cohort 2). In Cohort 1, 7.0% of patients were continuous users, 46.5% discontinued permanently, 28.5% discontinued temporarily, 2.8% augmented (added LAMA and/or LABA and/or ICS), and 15.2% switched therapy. In Cohort 2, 18.2% of patients were continuous users, 7.2% discontinued permanently, 27.9% discontinued temporarily, and 46.7% switched therapy. Exploratory analyses showed that time since COPD diagnosis was first recorded, pre-index exacerbation events, and concomitant medical conditions were potential drivers of initial maintenance treatment choices.

Conclusion

Discontinuation among new initiators of LABD-based therapy was high in France, whereas few switched or augmented treatment. In comparison, permanent discontinuation within 18 months was low in patients escalating to triple therapy.

[Personalised COPD care: Where are we going?]

The concept of personalised medicine is recent but the underlying notions are not new: knowing how to adapt care to patients' characteristics is one of the components of the "art of medicine". The advances of science allow to refine considerably the applications of the concept in many fields of medicine including COPD: research has identified phenotypes, endotypes and treatable traits. Personalisation can be applied to all components of care. For instance, the decision to perform screening spirometry relies not only on risk factors (age, smoking, other exposures) but also on symptoms. Assessment of comorbidities often associated with COPD is based on risk factors and their combinations, variable between individuals. Rehabilitation and its components are in essence highly individualised, which a major condition for their success. Last but not least, personalisation of pharmacological therapy, which has long been rather poor, could not benefit from biomarkers of interest (predictive of response), such as blood eosinophil count. Practical strategies using these still need to be established, and new biomarkers may usefully enrich the collection!