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OpenTrials
Completed

NCT Number: NCT07596264

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

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.

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Key information

Conditions

Venous Thromboembolism Cardiovascular Diseases Embolism and Thrombosis Prediction Models Thromboembolism Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Beijing Tsinghua Changgung Hospital

Beijing, Beijing Municipality, 102218, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

No intervention

Other

no intervention

Primary outcomes

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

    Time frame: the first day after the patients leaf ICU

Sponsors and collaborators

Lead sponsor

Beijing Tsinghua Chang Gung Hospital

Other

Registry information

Important dates

Study start
2022
Primary completion
2025
Study completion
2025
First posted
May 19, 2026
Registry last updated
May 19, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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