Predicting lower extremity deep venous thrombosis in patients with aneurysmal subarachnoid hemorrhage: a machine learning study.

ABSTRACT:

Background

Lower extremity deep venous thrombosis (LEDVT) is a frequent and serious complication after aneurysmal subarachnoid hemorrhage (aSAH). Existing risk scores poorly discriminate LEDVT risk in this population.

Objective

To develop and externally validate machine learning (ML) models for early prediction of LEDVT in aSAH patients treated with endovascular therapy.

Methods

We performed a retrospective multicenter study including an internal cohort (n = 593) for model development and internal validation and an external cohort (n = 142) for external validation. Thirty-seven clinical and laboratory variables were considered. Variable selection used LASSO followed by multivariable logistic regression. Seven ML algorithms (XGBoost, LightGBM, random fore

SUBMITTER: Jiang F 

PROVIDER: S-EPMC12682691 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature