Dataset Information

Predictive modeling of COPD exacerbation rates using baseline risk factors.

ABSTRACT:

Background

Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual's rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD.

Methods

Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for validation.

Results

Prior exacerbations, eosinophil count, forced expiratory volume in 1 s percent predicted, prior maintenance treatments, reliever medication use, sex, COPD Assessment Test score, smoking status, and region were significant predictors of exacerbation risk, with response to inhaled corticosteroids (ICSs) increasing with higher eosinophil counts, more prior exacerbations, or additional prior treatments. Model fit was similar in the training and test set. Prediction metrics were ~10% better in the full model than in a simplified model based only on eosinophil count, prior exacerbations, and ICS use.

Conclusion

These models predicting rates of moderate/severe exacerbations can be applied to a broad range of patients with COPD in terms of airway obstruction, eosinophil counts, exacerbation history, symptoms, and treatment history. Understanding the relative and absolute risks related to these factors may be useful for clinicians in evaluating the benefit: risk ratio of various treatment decisions for individual patients.Clinical trials registered with www.clinicaltrials.gov (NCT02465567, NCT02497001, NCT02766608, NCT02727660, NCT01854645, NCT01854658, NCT02343458, NCT03262012, NCT02536508, and NCT01970878).

SUBMITTER: Singh D

PROVIDER: S-EPMC9340368 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature

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Publications

Predictive modeling of COPD exacerbation rates using baseline risk factors.

Singh Dave D Hurst John R JR Martinez Fernando J FJ Rabe Klaus F KF Bafadhel Mona M Jenkins Martin M Salazar Domingo D Dorinsky Paul P Darken Patrick P

Therapeutic advances in respiratory disease 20220101

<h4>Background</h4>Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual's rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD.<h4>Methods</h4>Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNA ...[more]

PMID: 35815359

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Project description:BackgroundStudies suggest that up to 70% of chronic obstructive pulmonary disease (COPD) cases globally are undiagnosed worldwide. Some of these undiagnosed patients may present with severe exacerbation and factors associated with underdiagnosis in this population are unknown. We investigated the key factors associated with underdiagnosis in two cohorts of patients hospitalised for severe COPD exacerbation at different time points.MethodsThis retrospective, multicentre study analysed data from patients hospitalised for COPD exacerbation at two independent centres during two distinct time periods: between 1 January 2017 and 31 December 2018 in the Aquitaine region and between 1 January and 31 December 2022 in Paris. Undiagnosed COPD was defined as the absence of pulmonary function testing before the index exacerbation. Multivariate logistic regression was used to evaluate associations between underdiagnosis and patient characteristics.ResultsAmong the 424 patients, 93 (21.9%) were undiagnosed before hospitalisation, with a stable rate over time (22% in 2017-2018 and 21% in 2022). Multivariate analysis revealed that underdiagnosis was related to higher forced expiratory volume in one second (FEV1; adjusted OR (aOR)=1.02, p=0.043) and female sex (aOR=1.91, p=0.015). Patients with undiagnosed COPD had significantly lower rehospitalisation and mortality rates. After the initial severe exacerbation, higher mortality was associated with a higher Charlson Comorbidity Index (HR=1.24, p=0.007) and older age (HR=1.05, p=0.008).ConclusionThis retrospective, multicentre study demonstrated that about 20% of patients admitted with severe exacerbation were undiagnosed for COPD. Higher FEV1 and female sex were associated with underdiagnosis, emphasising the need for special attention to this population. These findings highlight the need to improve training and access to spirometry and develop new diagnostic tools that facilitate earlier detection and management of COPD.

Project description:BackgroundThe Swiss COPD cohort was established in 2006 to collect data in a primary care setting. The objective of this study was to evaluate possible predictive factors for exacerbation and re-exacerbation.MethodsIn order to predict exacerbation until the next visit based on the knowledge of exacerbation since the last visit, a multistate model described by Therneau and Grambsch was performed.ResultsData of 1,247 patients (60.4% males, 46.6% current smokers) were analyzed, 268 (21.5%) did not fulfill spirometric diagnostic criteria for COPD. Data of 748 patients (63% males, 44.1% current smokers) were available for model analysis. In order to predict exacerbation an extended Cox Model was performed. Mean FEV1/FVC-ratio was 53.1% (±11.5), with a majority of patients in COPD GOLD classes 2 or 3. Hospitalization for any reason (HR1.7; P = 0.04) and pronounced dyspnea (HR for mMRC grade four 3.0; P < 0.001) at most recent visit as well as prescription of short-acting bronchodilators (HR1.7; P < 0.001), inhaled (HR1.2; P = 0.005) or systemic corticosteroids (HR1.8; P = 0.015) were significantly associated with exacerbation when having had no exacerbation at most recent visit. Higher FEV1/FVC (HR0.9; P = 0.008) and higher FEV1 values (HR0.9; P = 0.001) were protective. When already having had an exacerbation at the most recent visit, pronounced dyspnea (HR for mMRC grade 4 1.9; P = 0.026) and cerebrovascular insult (HR2.1; P = 0.003) were significantly associated with re-exacerbation. Physical activity (HR0.6; P = 0.031) and treatment with long-acting anticholinergics (HR0.7; P = 0.044) seemed to play a significant protective role. In a best subset model for exacerbation, higher FEV1 significantly reduced and occurrence of sputum increased the probability of exacerbation. In the same model for re-exacerbation, coronary heart disease increased and hospitalization at most recent visit seemed to reduce the risk for re-exacerbation.ConclusionOur data confirmed well-established risk factors for exacerbations whilst analyzing their predictive association with exacerbation and re-exacerbation. This study confirmed the importance of spirometry in primary care, not only for diagnosis but also as a risk evaluation for possible future exacerbations.Trial registrationOur study got approval by local ethical committee in 2006 (EK Nr. 170/06) and was registered retrospectively on ClinicalTrials.gov (NCT02065921, 19th of February 2014).

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Dataset Information

Predictive modeling of COPD exacerbation rates using baseline risk factors.

Background

Methods

Results

Conclusion

Publications

Predictive modeling of COPD exacerbation rates using baseline risk factors.

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