Interpretable machine learning model for early prediction of deep vein thrombosis in elderly ICU patients: multicenter development and external validation

Abstract

Deep vein thrombosis (DVT) is a frequent complication in elderly intensive care unit (ICU) patients, while reliable prediction tools for this population remain limited. This study aimed to develop and externally validate an interpretable machine learning model for early prediction of DVT in critically ill elderly patients. In this multicenter retrospective study, ICU patients aged ≥ 75 years from two tertiary medical centers in China were enrolled. Patients with pre-existing DVT were excluded. A development cohort (Suzhou, n  = 1,717) and an external validation cohort (Xi’an, n  = 533) were established. Clinical variables collected within 24 h after ICU admission were used to predict incident DVT during ICU stay. Least absolute shrinkage and selection operator regression was applied for feature selection, and four machine learning models were developed and compared. Model discrimination, calibration, and interpretability were evaluated using area under the curve (AUC), calibration analysis, and SHapley Additive exPlanations (SHAP). DVT occurred in 15.2% and 21.0% of patients in the development and external validation cohorts, respectively. Among the evaluated models, XGBoost achieved the best performance, with AUCs of 0.843 in internal validation and 0.811 in external validation. Calibration analysis demonstrated good agreement between predicted and observed risks. Key predictors included D-dimer, C-reactive protein, platelet-to-lymphocyte ratio, procalcitonin, mechanical ventilation, and mechanical thromboprophylaxis. SHAP analysis quantified the contribution of individual predictors. We developed and externally validated an interpretable machine learning model for early DVT prediction in elderly ICU patients. The model may support risk stratification and clinical decision-making in critical care settings.