ArticleTherapeutic advances in respiratory disease
A multifactorial model for chronic obstructive pulmonary disease risk in smokers with positive bronchodilation: a retrospective cohort study.
Article in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundChronic obstructive pulmonary disease (COPD) remains underdiagnosed in smoking populations, as conventional risk stratification relies heavily on spirometry and fails to integrate dynamic imaging markers and type 2 inflammation biomarkers.
objectivesTo develop and validate a multifactorial prediction model integrating bronchodilator responsiveness, clinical features, imaging markers, and inflammatory biomarkers to identify, among smokers already undergoing routine spirometric assessment, those at highest risk for underdiagnosed or potentially progressive COPD.
designRetrospective cohort study.
methodsIn this retrospective cohort study, 440 smokers treated between August 2015 and October 2024 were stratified into bronchodilator test-positive and test-negative groups (
resultsPositive bronchodilator test, age, smoking index, chronic bronchitis/emphysema, airway wall thickening, and elevated eosinophil counts were significantly associated with COPD. The model demonstrated strong diagnostic accuracy. A positive test independently conferred a 3-fold increased risk and identified a high-risk subgroup with a 3-year cumulative incidence of 45%. The integrated model detected 78.5% of high-risk cases an average of 2.3 years earlier, reduced unnecessary spirometry referrals by 27%, and increased early intervention rates by 19%. Bootstrap validation yielded an optimism-corrected C-index of 0.86. DISCUSSION: A positive bronchodilator test, when combined with a panel of clinical and inflammatory markers, constitutes a valuable multidimensional indicator for stratifying COPD risk among smokers, beyond what is captured by baseline spirometry alone. Integrating these factors into a predictive model may facilitate earlier detection of cases prone to being missed by standard diagnostic algorithms and enable more targeted monitoring and interventions.
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