Venous ThromboembolismNCT07596264
Development and Validation of an Interpretable Machine Learning Model for Predicting Venous Thromboembolism（VTE）in Intensive Care Unit (ICU) Patients
An observational study of Venous Thromboembolism and Prediction Models, run by Beijing Tsinghua Chang Gung Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Venous Thromboembolism, Prediction Models
- How many people
- 12,061 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2022to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- age ≥18 years;
- ICU length of stay ≥48 hour
- the first ICU admission
Who cannot join
- VTE diagnosed prior to ICU admission
- VTE diagnosed within 24 hours of ICU admission
- >20% missing values in key variables
Trial locations and contacts
1 site in 1 country.
China1 site
Beijing Tsinghua Changgung Hospital
Beijing, Beijing Municipality, 102218
About this study
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.
- Conditions
- Venous Thromboembolism, Prediction Models
- Being tested
- no intervention
- Medical subject headings
- Venous Thromboembolism
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
What is being measured
The primary outcome is the main question the study is designed to answer.
validate an interpretable machine learning (ML) model to predict VTE in ICU patients
Measured: the first day after the patients leaf ICU
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2026
- Study start1 Jan 2022
- Primary completion31 Dec 2025
- Study completion31 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Venous ThromboembolismStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 12,061 participants (actual)
Sponsor and investigators
- Lead sponsor
- Beijing Tsinghua Chang Gung Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Weiwei Wu, Beijing Tsinghua Chang Gung Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07596264
- Sponsor's protocol ID
- 19242-2-01
- Status last verified
- Jan 2022
- First submitted
- 12 May 2026
- Last updated
- 19 May 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Interpretable Machine Learning Model for Predicting Venous Thromboembolism（VTE）in Intensive Care Unit (ICU) Patients
- ClinicalTrials.gov ID
- NCT07596264
- Acronym / sponsor ID
- 19242-2-01
- Conditions
- Venous Thromboembolism, Prediction Models
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 12,061 participants (actual)
- Lead sponsor
- Beijing Tsinghua Chang Gung HospitalUniversity, hospital or other
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 31 Dec 2025 (actual)
- First posted
- 19 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case only
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- age ≥18 years;
- ICU length of stay ≥48 hour
- the first ICU admission
Key exclusion criteria (3 listed)
- VTE diagnosed prior to ICU admission
- VTE diagnosed within 24 hours of ICU admission
- >20% missing values in key variables
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| no interventionno intervention | Other | – |
Primary outcome
validate an interpretable machine learning (ML) model to predict VTE in ICU patients
Time frame: the first day after the patients leaf ICU
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 May 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Venous Thromboembolism and Prediction Models, run by Beijing Tsinghua Chang Gung Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 19 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
validate an interpretable machine learning (ML) model to predict VTE in ICU patients (time frame: the first day after the patients leaf ICU). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 May 2026.