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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07596264

Enrollment

12061

Registered

2026-05-19

Start date

2022-01-01

Completion date

2025-12-31

Last updated

2026-05-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Prediction Models, Venous Thromboembolism

Brief summary

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.

Interventions

OTHERno intervention

no intervention

Study design

Observational model

CASE_ONLY

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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

Design outcomes

Primary

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

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 20, 2026