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

Beijing Tsinghua Chang Gung Hospital

Status: Completed

Conditions: Venous Thromboembolism; Prediction Models

Treatments: Other: no intervention

Study type: Observational

Funder types: Other

Identifiers

NCT07596264

19242-2-01

Details and patient eligibility

About

Venous thromboembolism remains a leading cause of preventable mortality in intensive care unit (ICU) patients. Existing risk-stratification tools were developed in general medical populations and lack ICU-specific predictors. This study was to develop and validate an interpretable machine learning (ML) model to predict VTE in ICU patients.

Enrollment: 12,061 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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