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

Beijing Tsinghua Chang Gung Hospital12,061 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

12,061

Conditions

Venous Thromboembolism Prediction Models

Interventions

no interventionOTHER

no intervention

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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

Locations (1)

Beijing Tsinghua Changgung Hospital

Beijing, Beijing Municipality, China

Outcomes

Primary Outcomes

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

Time frame: the first day after the patients leaf ICU

Data from ClinicalTrials.gov

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