Machine learning-based preoperative prediction of perioperative venous thromboembolism in Chinese lung cancer patients: a retrospective cohort study
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View abstract on PubMed
Summary
This summary is machine-generated.Machine learning accurately predicts venous thromboembolism (VTE) risk in lung cancer surgery patients. An extreme gradient boosting model identifies high-risk individuals for personalized prevention strategies.
Area Of Science
- Oncology
- Medical Informatics
- Cardiovascular Medicine
Background
- Perioperative venous thromboembolism (VTE) is a significant risk for lung cancer patients undergoing surgery.
- Conventional prediction models struggle with complex clinical data, necessitating advanced approaches.
- Machine learning (ML) presents a promising avenue for improving VTE risk prediction accuracy.
Purpose Of The Study
- To develop and validate a machine learning (ML)-based model for preoperative risk assessment of VTE in lung cancer patients.
- To identify key predictors of VTE in this patient cohort.
- To create a clinical tool for early VTE risk stratification.
Main Methods
- Retrospective analysis of 1,013 lung cancer patients undergoing surgery.
- Identification of six key predictors (age, MCV, MCH, fibrinogen, D-dimer, albumin) using univariate and Lasso regression.
- Training and evaluation of eight ML models, including XGBoost, Random Forest, and Logistic Regression, using AUC, AUPRC, and calibration curves.
Main Results
- Venous thromboembolism (VTE) occurred in 17.3% of patients.
- The extreme gradient boosting (XGB) model exhibited the highest predictive performance (AUC: 0.99 training, 0.66 validation).
- Age and mean corpuscular volume were identified as the most influential predictors; an online prediction tool was developed.
Conclusions
- The ML-based XGB model offers a reliable method for preoperative VTE risk assessment in lung cancer surgery.
- This model facilitates early risk stratification and the implementation of personalized thromboprophylaxis.
- The developed prediction tool can aid clinicians in managing VTE risk.
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