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Construction of a Machine Learning-Based Deep Venous Thrombosis Risk Prediction Model for Lung Cancer Patients

Construction of a Machine Learning-Based Deep Venous Thrombosis Risk Prediction Model for Lung Cancer Patients

Status
Active, not recruiting
Phases
Early Phase 1
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400080466
Enrollment
Unknown
Registered
2024-01-30
Start date
2024-02-15
Completion date
Unknown
Last updated
2024-02-05

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

Conditions

Venous Thromboembolism

Interventions

Experimental group:Patients with lung cancer who develop VTE during hospitalization
Control group:Lung cancer inpatients who did not develop VTE during the same period

Sponsors

The First Affiliated Hospital , Army Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1.The diagnosis of lung cancer is confirmed through pathology. The staging of lung cancer is performed using the detailed and accurate staging criteria established by the 8th edition of the Union for International Cancer Control. 2.The diagnosis of venous thromboembolism refers to the "Diagnosis, Treatment, and Prevention Guidelines for Pulmonary Thromboembolism" published by the Chinese Medical Association in 2022. 3. The inclusion criteria for patients are age = 18 years old and hospitalization for more than 48 hours.

Exclusion criteria

Exclusion criteria: 1.Patients who died within 48 hours of admission or were discharged automatically. 2.Patients with hypercoagulability, severe coagulation disorders, or those receiving ongoing anticoagulant therapy. 3. Patients with concurrent hematologic disorders or autoimmune diseases. 4.Venous thromboembolism may be related to other triggers such as car accidents or fractures. 5.Patients with concomitant primary tumors in other locations.

Design outcomes

Primary

MeasureTime frame
Venous Thromboembolism;

Countries

China

Contacts

Public ContactLei Liu

The First Affiliated Hospital , Army Medical University

ttcrystalma@163.com+86 130 3830 0661

Outcome results

None listed

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