CompletedNCT07596264

Development and Validation of an Interpretable Machine Learning Model for Predicting Venous Thromboembolism（VTE）in Intensive Care Unit (ICU) Patients

Sponsor: Beijing Tsinghua Chang Gung Hospital

Trial Details

Condition	Venous Thromboembolism, Prediction Models
Intervention	no intervention
Status	Completed
Participants	12,061
Est. Completion	31 Dec 2025
Last Updated	19 May 2026

Primary Outcome

validate an interpretable machine learning (ML) model to predict VTE in ICU patients

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * age ≥18 years; * ICU length of stay ≥48 hour * the first ICU admission Exclusion Criteria: * VTE diagnosed prior to ICU admission * VTE diagnosed within 24 hours of ICU admission * \>20% missing values in key variables

Trial Sites

Countries

China

View Registry Entry

Related Analysis

Completed Study Validates Machine Learning Model for VTE Prediction in ICU Patients — Clinical Decision Support Tool Shows Promise

A completed study of 12,061 ICU patients has validated a machine learning model for predicting venous thromboembolism risk. This ICU-specific tool could improve risk stratification and thromboprophylaxis decisions in critically ill patients.

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