Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study

Background Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations. Methods We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores. Findings Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63 center dot 1 years (SD 9 center dot 2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0 center dot 854 (95% CI 0 center dot 852-0 center dot 855) for at least one severe exacerbation within 3 years and 0 center dot 931 (0 center dot 930-0 center dot 933) for consistent exacerbations (defined as =1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0 center dot 121 for at least one severe exacerbation; 0 center dot 039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0 center dot 854 [0 center dot 852-0 center dot 855]) than exacerbation history (0 center dot 823 [0 center dot 822-0 center dot 825]) and BODE index 0 center dot 812 [0 center dot 811-0 center dot 814]). 6965 participants were included in the external validation cohort, with a mean age of 60 center dot 5 years (SD 8 center dot 9). In this cohort, AUC for at least one severe exacerbation was 0 center dot 768 (0 center dot 767-0 center dot 769; Brier score 0 center dot 088). Interpretation CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations.