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Opportunistic Chest CT-Derived Body Composition for Predicting 90-Day Adverse Outcomes After Hospitalization for
1Department of Pulmonary and Critical Care Medicine, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, Fujian, China.
Background:
Patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remain at risk of readmission and death after discharge. Opportunistic chest computed tomography (CT) body-composition metrics may provide additional prognostic information beyond conventional clinical scores.
Objective:
To develop and validate a 90-day adverse-outcome prediction model for hospitalized AECOPD using admission clinical variables and opportunistic chest CT body-composition metrics.
Methods:
A retrospective modelling cohort of 203 AECOPD admissions from the index centre was analysed. Admissions from 2021 to 2024 formed the development cohort (n = 152), and 2025 admissions formed the temporal-validation cohort (n = 51). An external-validation cohort from another centre included 103 admissions from records screened between 1 January 1 and 1 January 2025 after the model was locked. The primary outcome was 90-day readmission or death. LASSO was used for variable screening in the development cohort. A feature-count AUC plateau analysis and prespecified multialgorithm screening were used to lock the final model before validation. DECAF and BAP-65 were retained as comparator scores.
Results:
The 90-day adverse outcome occurred in 66 of 152 development patients (43.4%), 18 of 51 temporal-validation patients (35.3%) and 35 of 103 external-validation patients (34.0%). LASSO retained prior AECOPD admissions, home oxygen before admission, diabetes mellitus, intermuscular adipose tissue area, long-term NIV before admission, heart rate and coronary artery disease. Feature-count analysis supported this seven-predictor set, and multialgorithm screening selected HistGradientBoosting for validation. In temporal validation, the locked model achieved AUC 0.80 (0.64-0.95), sensitivity 0.78 (0.59-0.94), specificity 0.88 (0.76-0.97) and Brier score 0.15 (0.09-0.21), with imperfect calibration (Hosmer-Lemeshow p < 0.001). In external validation, the locked model achieved AUC 0.77 (0.66-0.87), sensitivity 0.66 (0.49-0.80), specificity 0.79 (0.71-0.88) and Brier score 0.18 (0.15-0.21); the Hosmer-Lemeshow p value was 0.209.
Conclusions:
A 90-day AECOPD prediction model combining clinical and opportunistic CT body-composition variables showed consistent discrimination across validation cohorts, but calibration remained a key implementation boundary. Formal multicentre validation and calibration updating are needed before routine clinical use.
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