Machine Learning Approach to Predict Venous Thromboembolism Among Patients Undergoing Multi-Level Spinal Posterior Instrumented Fusion

Introduction: The absence of consensus for prophylaxis of venous thromboembolism (VTE) in spine surgery underscores the importance of identifying patients at risk. This study incorporated machine learning (ML) models to assess key risk factors of VTE in patients who underwent posterior spinal instrumented fusion.

Methods: Data was collected from the IBM MarketScan Database (2009-2021) for patients >18 years old who underwent spinal posterior instrumentation (3-6 levels), excluding traumas, malignancies, and infections. VTE incidence (deep vein thrombosis and pulmonary embolism) was recorded 90-days post-surgery. Risk factors for VTE were investigated and compared through several machine learning models including logistic regression, linear support vector machine (LSVM), random forest, extreme gradient boosting (XGBoost), and neural networks.

Results: Among the 140,951 patients who underwent spinal fusion with posterior instrumentation (3-6 levels), the overall 90-day VTE rate was 4.17%. The LSVM model demonstrated the best prediction with an area under the curve (AUC) of 0.66. The most important features for prediction of VTE included prior history of VTE, diagnosis of chronic hypercoagulability, hemiplegia, chronic renal disease, and chronic pulmonary disease. Patients who did not have these five key risk factors had a 90-day VTE rate of 3.15%. Patients who had an increasing number of key risk factors had subsequently higher risks of postoperative VTE.

Conclusion : The analysis of the data with different machine learning models identified 5 key variables that are most closely associated with VTE. Using these variables, we have developed a simple risk model with additive odds ratio ranging from 1.56 (1 risk factor) to 23.70 (4 risk factors) over 90 days after posterior spinal fusion surgery. These findings can help surgeons risk-stratify their patients for VTE risk, and potentially guide subsequent chemoprophylaxis.