Evaluating the Risk of Postoperative Venous Thromboembolism in Cervical Cancer Patients

Haike Lei

Status: Completed

Conditions: Venous Thromboembolism

Study type: Observational

Funder types: Other

Identifiers

NCT06556953

CZLS2023337-A

Details and patient eligibility

About

The aim of this study is to develop a machine learning model to accurately predict the risk of venous thromboembolism in patients with cervical cancer after surgery.

Full description

Venous thromboembolism (VTE) is a common and life-threatening complication in patients with cervical cancer following surgery. The objective of this study is to develop a machine learning model with the potential to predict the risk of VTE in these patients postoperatively. We plan to employ partial dependence (PD) curves, breakdown (BD) curves, Ceteris-paribus (CP), and SHapley additive exPlanations (SHAP) values for a comprehensive analysis. The goal is to explore how different machine learning algorithms can be utilized as tools for personalized postoperative VTE risk assessment in cervical cancer patients.

Enrollment: 1,174 patients

Sex: Female

Ages: 20 to 89 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Confirmation of cervical cancer through pathological examination.
• Receipt of surgical treatment for cervical cancer at Chongqing University Cancer Hospital in China.
• Provision of comprehensive case information.

Exclusion criteria

• Patients under the age of 18.
• History of VTE caused by other reasons before surgery.
• Secondary cervical cancer or accompanying primary malignant tumor.