Dynamic Prediction of Venous Thromboembolism in Gynecological Surgery Using Perioperative Biomarkers and an Extremely Randomized Trees Model: A Retrospective Case-Control Study.

Authors

Yingsha Yao, Huizhen Lin, Chuhan Wang, Danli Ma, Yanhong Fu, Ting Wang, Huimin Yu, Ruoan Jiang

Published in

Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis. Volume 32. Pages 10760296261468442. Epub Jul 10, 2026.

Abstract

BackgroundHospital-associated venous thromboembolism (HA-VTE) is the leading preventable cause of death among patients undergoing gynecological surgery. Standard risk assessment methodologies, such as the Caprini score, which is a commonly utilized tool for risk assessment, do not sufficiently account for the unique vulnerabilities inherent to this specialty. To address these deficiencies, we aimed to develop a preliminary machine learning framework for predicting venous thromboembolism (VTE) in gynecological contexts.MethodsIn this retrospective case-control investigation, we identified 75 patients who were consecutively diagnosed with postoperative VTE after undergoing gynecological surgery at Ningbo NO.2 Hospital between March 2020 and February 2025. Controls were chosen using a matching protocol at a ratio of 1:3, resulting in the random selection of 225 patients free from VTE within the same surgical cohort during the same period. An Extremely Randomized Trees (Extratrees) machine learning classifier was constructed, incorporating 22 clinically relevant predictors identified by univariate analyses. We utilized the new model to compare it against the Caprini score.ResultsThe model was trained and validated with a class-stratified 70:30 split and exhibited remarkable discriminative ability, achieving an area under the curve (AUC) of 0.907 [95% confidence interval (CI): 0.833-0.972], significantly exceeding the performance of the Caprini score (AUC 0.731, 95% CI: 0.660-0.803). The model showed consistent discrimination across validation groups, yielding an accuracy of 0.833.ConclusionThis preliminary machine learning framework, tailored specifically for gynecology, provides enhanced risk stratification for VTE.

PMID:
42429068
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

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