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A study of a machine learning-based visual prediction tool for the risk of venous thrombosis in critically ill patient

A study of a machine learning-based visual prediction tool for the risk of venous thrombosis in critically ill patient

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500099341
Enrollment
Unknown
Registered
2025-03-21
Start date
2025-04-01
Completion date
Unknown
Last updated
2025-03-24

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

Conditions

Venous thrombosis

Interventions

Control group:None
Experimental group:None

Sponsors

The First Affiliated Hospital of Chongqing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: (1) Age >= 18 years old; (2) The patient was not diagnosed with VTE before admission to ICU.

Exclusion criteria

Exclusion criteria: (1) ICU stay <= 24h or death within ICU24h; (2) Patient data lacked key information (such as ultrasound results, etc.).

Design outcomes

Primary

MeasureTime frame
Incidence of VTE within 28 days of ICU admission;—

Secondary

MeasureTime frame
VTE normalized prevention rate;Length of ICU Stay;ambulation;User satisfaction of the web-based VTE prediction model;—

Countries

China

Contacts

Public ContactZhang Chuanlin

The First Affiliated Hospital of Chongqing Medical University

zhangchuanlinhl@163.com+86 156 8388 4103

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026