Multimodal Deep Learning Integrating Chest CT Lung-Component Segmentation and Clinical Variables for Prediction of 1-Year Composite Cardiovascular Events in COPD: A Single-Centre Retrospective Cohort Study

Abstract Background Patients with chronic obstructive pulmonary disease (COPD) are at increased risk of cardiovascular disease (CVD) events. We aimed to develop and internally validate a multimodal deep learning model that integrates chest CT–derived lung-component information with clinical variables to predict 1-year composite CVD events in COPD. Methods We enrolled COPD patients between January 2023 and January 2025 who underwent baseline thin-slice non-contrast chest CT and completed 12-month follow-up. The primary endpoint was the first occurrence within 12 months of a composite CVD event (acute myocardial infarction, ischaemic stroke, hospitalisation for unstable angina, or hospitalisation for heart failure). CT images were standardised and resampled to 1.0×1.0×1.0 mm voxels. Lung parenchyma was segmented using a U-Net–based model; emphysema regions were identified by thresholding (HU < − 950); pulmonary vessels were enhanced using a Frangi filter and extracted via connected component analysis. A three-branch 3D convolutional neural network (3D-CNN) was constructed to extract imaging features from normal lung tissue, emphysematous regions, and pulmonary vascular structures, which were fused with clinical features (including age, smoking pack-years, and FEV1%) using an attention mechanism. The dataset was split into training/validation/test sets (8:1:1) with stratified sampling. Discrimination and classification metrics were reported on the independent test set; calibration was assessed using a calibration curve, Brier score, and calibration intercept/slope. Results A total of 306 COPD patients were included (training n = 244, validation n = 30, test n = 32). Within 12 months, 182 composite CVD events occurred (59.5%). On the independent test set (events = 17/32), the model achieved an AUC of 0.8588 with an accuracy of 78.12%, precision of 81.25%, recall/sensitivity of 76.47%, specificity of 80.00%, and F1-score of 0.7879. The Brier score was 0.187, with a calibration slope of 0.435 and an intercept of − 0.606. Conclusions A multimodal deep learning framework combining lung-component CT features and clinical variables demonstrated good discrimination for predicting 1-year composite CVD events in COPD. Further external validation and calibration refinement are warranted before clinical deployment.