Prediction of radiologically confirmed hospital-acquired venous thromboembolism: machine learning versus Padua and IMPROVE, with external validation
Year: 2026
Type: preprint
Abstract: Objective The guideline-endorsed Padua and IMPROVE scores have only moderate discrimination for hospital-acquired venous thromboembolism (VTE). We developed and externally validated machine learning models for radiologically confirmed in-hospital VTE, benchmarked against electronic health record (EHR)-computed scores. Approach and Results We analyzed 379,720 adult MIMIC-IV admissions (radiology-report-covered 2008–2019), retaining prior VTE. The outcome was imaging-confirmed pulmonary embolism or deep-vein thrombosis diagnosed >24 hours after admission. Static and dynamic landmark models were compared with the scores using patient-grouped cross-validation, cluster-bootstrap inference and decision curve analysis; frozen models were externally validated in 29,144 high-risk admissions in Beijing, China. VTE occurred in 674 admissions (0.178%); ICD-based ascertainment of the same population yields 2.7%, roughly 15-fold higher. Because discharge-coded flags are unavailable at prediction time, the primary deployment-ready model used 42 admission-window features: out-of-fold AUC 0.7864 (ΔAUC +0.0795 versus Padua; 95% CI +0.054 to +0.104). The information-complete 57-feature models reached out-of-fold AUCs of 0.8090 (XGBoost) and 0.7893 (logistic regression) versus 0.7073 (Padua) and 0.6884 (IMPROVE); ΔAUC +0.1021 and +0.1211 for XGBoost versus Padua and IMPROVE (95% CIs excluding zero). Dynamic updating added +0.0953 AUC; a sequence model did not (ΔAUC −0.026). Externally, the admission-safe model transported better (AUC 0.706 vs. 0.678), exceeding both scores (95% CIs excluding zero). Conclusion Machine learning significantly improves on EHR-computed Padua and IMPROVE scores for in-hospital VTE prediction; the advantage persists on transport to a separate tertiary hospital’s high-risk admissions at lower absolute discrimination. Deployment requires admission-window features, local recalibration, and prospective validation.
Source: medRxiv
Authors: Jun Feng, Yiyao Li, Simiao Yu, Juhong Shi, Xuefeng Sun
Language: English
Subfield: Internal Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7204540627