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Construction and validation of a machine learning model for predicting the risk of venous thromboembolism in patients with surgical malignant tumors

Construction and validation of a machine learning model for predicting the risk of venous thromboembolism in patients with surgical malignant tumors

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600123435
Enrollment
Unknown
Registered
2026-04-27
Start date
2026-04-28
Completion date
Unknown
Last updated
2026-05-04

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

Conditions

Surgical malignancies scheduled for surgical treatment include those with pancreatic cancer, gastric cancer, esophageal cancer, colorectal cancer, thyroid cancer, lung cancer, bladder cancer, kidney cancer, prostate cancer, liver cancer, brain tumors, and bone metastases.

Interventions

Non-VTE Group :None
VTE Group :None

Sponsors

Peking Union Medical College Hospital, Chinese Academy of Medical Sciences
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. Age >= 18 years; 2. Patients with malignant tumors undergoing surgical treatment, including pancreatic cancer, gastric cancer, esophageal cancer, colorectal cancer, thyroid cancer, lung cancer, bladder cancer, kidney cancer, prostate cancer, liver cancer, brain tumors, bone metastases, etc.; 3. Underwent lower extremity venous ultrasound examination during the perioperative period; 4. Complete clinical information records.

Exclusion criteria

Exclusion criteria: 1. Patients undergoing surgery for non-malignant tumors, or whose lesions are confirmed to be benign during surgery; 2. Patients with active bleeding, coagulation disorders, or contraindications to anticoagulation; 3. Pregnant or lactating women.

Design outcomes

Primary

MeasureTime frame
Venous thromboembolism ;

Secondary

MeasureTime frame
Demographic characteristics ;tumor-related variables;previous medication history;previous treatments;Laboratory test indicators;Past medical history ;Caprini Scale ;Khorana Scale ;

Countries

China

Contacts

Public ContactYifeng Guo

Peking Union Medical College Hospital, Chinese Academy of Medical Sciences

guoyf1987@sina.com+86 10 6915 2200

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: May 7, 2026