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

May 18, 2026 updated by: Weiwei Wu, Beijing Tsinghua Chang Gung Hospital

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 Overview

Status

Completed

Conditions

Intervention / Treatment

Other: no intervention

Study Type

Observational

Enrollment (Actual)

12061

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

China
- Beijing Municipality
  - Beijing, Beijing Municipality, China, 102218
    - Beijing Tsinghua Changgung Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

Adult
Older Adult

Accepts Healthy Volunteers

Sampling Method

Non-Probability Sample

Study Population

Patients admitted to ICU were included in the study cohort from January 2022 to October 2025. Inclusion criteria were: (1) age ≥18 years; (2) ICU length of stay ≥48 hours; (3) only the first ICU admission per patient was retained. Exclusion criteria were: (1) VTE diagnosed prior to ICU admission; (2) VTE diagnosed within 24 hours of ICU admission; (3) >20% missing values in key variables. The final cohort comprised 12,061 patients, of whom 587 (4.9%) developed VTE during ICU hospitalization.

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
validate an interpretable machine learning (ML) model to predict VTE in ICU patients Time Frame: the first day after the patients leaf ICU	the first day after the patients leaf ICU

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Beijing Tsinghua Chang Gung Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2022

Primary Completion (Actual)

December 31, 2025

Study Completion (Actual)

December 31, 2025

Study Registration Dates

First Submitted

May 12, 2026

First Submitted That Met QC Criteria

May 18, 2026

First Posted (Actual)

May 19, 2026

Study Record Updates

Last Update Posted (Actual)