Background: Low vaccination rates against influenza and Streptococcus (S.) pneumoniae infections in COPD could impair outcomes. Understanding underlying factors could help improving implementation. Objectives: To describe vaccination rates at inclusion in COPD cohorts and analyze associated factors. Methods: Between 2012 and 2018, 5927 patients with sufficient data available were recruited in 3 French COPD cohorts (2566 in COLIBRI-COPD, 2653 in PALOMB and 708 in Initiatives BPCO). Data at inclusion were pooled to describe vaccination rates and analyze associated factors. Results: Mean age was 66 years, 34 % were women, 35 % were current smokers, mean FEV1 was 58 % predicted, 22 % reported >= 2 exacerbations in the year prior to inclusion, mMRC dyspnea grade was >= 2 in 59 %, 52 % had cardiovascular comorbidities and 9 % a history of asthma. Vaccinations rates in the year prior to study entry were 34.4% for influenza + S. pneumoniae, 17.5 % for influenza alone and 8.9 % for S. pneumoniae alone. In multivariate analyses, influenza vaccination rate was greater in older age, smoking status, low FEV1, exacerbation history, mMRC dyspnea>2, asthma history, hypertension, diabetes mellitus, and the year of inclusion. SP vaccination was associated with type of practice of the respiratory physician, age, smoking status, FEV1, exacerbation history, dyspnea grade, asthma history and the year of inclusion. Conclusion: Rates of vaccination against influenza and S. pneumoniae infection at inclusion in COPD cohorts remain insufficient and vaccination appears restricted to patients with specific features especially regarding severity and comorbidities, which is not consistent with current recommendations. (c) 2024 SPLF and Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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., 38% of patients while 84% of patients had an "abnormal" TLCO. A large majority of the 268 patients with a "normal" FVC had nevertheless an "abnormal" TLCO (n = 209; 78%). On longitudinal analysis, 67/221 patients had an annual absolute decline in FVC >= 5%, 34/221 had an annual absolute decline in TLCO >= 10%, and 22 had both.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.(c) 2023 Published by Elsevier Masson SAS.

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.

Based on the hypothesis that many continuous and categorical variables collected during the consultation could be affected (as cause or consequence) by excessively sedentary and physically inactive behaviour (ESPI), we developed a predictive method based on all the information collected during the COPD digital consultation (n=5040). The variable to be predicted, i.e. the ESPI, was defined by crossing 2 estimates among several categories of activities proposed to the doctor and the patient. At the other end of the spectrum, we defined also an ACTIVE status. Methods: we first verified that the ESPI variable and its variability were well correlated with a set of continuous and categorical variables measured by performing a factor analysis on mixed data. A scree plot was then used to determine the statistically significant factors of the components to be included in more elaborate individual predictive models. To ensure the robustness of the prediction, we compared two predictive models (a multiple logistic regression and a random forest), and to generalize their predictive power through estimates of the uncertainty of their predictions, these models were improved by sequentially adding several random effects such as the identity of the doctor, the hospital centre, etc. The models were then used to estimate the uncertainty of the predictions. Results: The set of methods used allows to predict the ESPI and ACTIVE status with an error rate of 33% and 7% respectively. Conclusion: This proof-of-concept study demonstrates the ability to detect ESPI status using a machine learning powered by common variables from a digital consultation including a wide range of COPD patients.

Background: Suboptimal vaccination against influenza and Streptococcus pneumoniae (SP) infections is reported in patients with COPD and could impair outcomes. Understanding underlying factors could help targeting future campaigns. Objectives: To describe vaccination rates in COPD patients followed by respiratory physicians and analyse associated factors. Methods: Between 2012 and 2018, 6523 patients were recruited in 3 French COPD cohorts (3067 in COLIBRI, 2653 in PALOMB and 803 in Initiatives BPCO). Data at entry were pooled to describe the population and vaccination rates and perform univariate and multivariate analyses of associated factors. Results: Population characteristics: female: 34%, mean age: 66 years, current smokers: 35%, mean FEV1: 58% predicted, ≥2 exacerbations in the previous year: 22%, mMRC dyspnea grade ≥2: 59%, cardiovascular comorbidities: 52%, history of asthma: 9%. Vaccinations rates were 34% for flu+SP, 16% for flu alone and 7% for SP alone. In multivariate analyses, flu vaccination was more frequent in older patients, past vs current smokers, patients with comorbidities (cardiovascular, diabetes mellitus, asthma) and mMRC ≥ 1. SP vaccination was associated with hospital-based physicians, past vs current smoking, more exacerbations, presence of chronic bronchitis. FEV1 was not independently associated with vaccination. Conclusion: Vaccination against influenza and SP infection remains insufficient even in patients followed by respiratory physicians. It appears modulated by specific clinical features while it should be systematic. Method: COLIBRI: AgiraDOM, AZ, BI, Chiesi, GSK, Novartis; Initiatives BPCO: BI; PALOMB: Fondation Bordeaux Université, Novartis, GSK, Isis, BI; Present analyses: Pfizer.

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.

INTRODUCTION:Over the last decade, new evidence and many guidelines have been published on COPD pharmacological treatments; prescriptions are often not in accordance with guidelines. MATERIALS AND METHODS:Trends in physician treatment choices from February 2012 to November 2018 (Feb.2012/Nov.2018) were analyzed using data from COPD patients (spirometry-confirmed diagnosis) included in the COLIBRI-COPD cohort. Inhaled drug treatments (short- or long-acting β2-agonist [SABA or LABA], short- or long-acting anticholinergic [SAMA or LAMA], or corticosteroid [ICS]) were classified into 5 treatment categories: "No initial maintenance treatment (IMT)" (untreated, or only SAMA or SABA); "1 long-acting bronchodilator (LABD)" (LABA or LAMA); "2 LABDs" (LABA + LAMA); "1 LABD + ICS" (LABA or LAMA + ICS); "2 LABDs + ICS" (LABA + LAMA + ICS). For the purpose of the study, 4 periods were defined to achieve balanced samples (T1-T4). RESULTS:Data from 4537 patients were collected. Over time, 3 major changes were observed: (1) an increase in treatment category "No IMT", mostly for GOLD 1 or GOLD A categories (GOLD A: from 19.1% at T1 to 41.2% at T4); (2) an increase in treatment category "2 LABDs" for GOLD 2 to 4 and GOLD A to D categories (GOLD B: from 15.4% to 29.7%); (3) a decrease in ICS use ("1 LABD + ICS" or "2 LABDs + ICS"), mostly for GOLD 1 to 3 and GOLD A categories (GOLD A, 2 LABDs + ICS: from 35.3% to 11.1%). CONCLUSION:Changes over time in therapeutic profiles suggest that new evidence from scientific publications and recommendations may have had a rapid impact on clinical practice.