Using Machine Learning (ML) Models to Predict Risk of Venous Thromboembolism (VTE) Following Spine Surgery

Study Design: A retrospective cohort study. Objectives: Venous thromboembolism (VTE) is a potentially high-risk complication for patients undergoing spine surgery. Although guidelines for assessing VTE risk in this population have been established, development of new techniques that target different aspects of the medical history may prove to be of further utility. The goal of this study was to develop a predictive machine learning (ML) model to identify nontraditional risk factors for predicting VTE in spine surgery patients. Summary of Background Data: A cohort of 63 patients was identified who had undergone spine surgery at a single center from 2015 to 2021. Thirty-one patients had a confirmed VTE, while 32 had no VTE. A total of 113 attributes were defined and collected via chart review. Attribute categories included demographics, medications, labs, past medical history, operative history, and VTE diagnosis. Methods: The Waikato Environment for Knowledge Analysis (WEKA) software was used in creating and evaluating the ML models. Six classifier models were tested with 10-fold cross-validation and statistically evaluated using t tests. Results: Comparing the predictive ML models to the control model (ZeroR), all predictive models were significantly better than the control model at predicting VTE risk, based on the 113 attributes ( P <0.001). The Random Forest model had the highest accuracy of 88.89% with a positive predictive value of 93.75%. The Simple Logistic algorithm had an accuracy of 84.13% and defined risk attributes to include calcium and phosphate laboratory values, history of cardiac comorbidity, history of previous VTE, anesthesia time, selective serotonin reuptake inhibitor use, antibiotic use, and antihistamine use. The J48 model had an accuracy of 80.95% and it defined hemoglobin laboratory values, anesthesia time, beta-blocker use, dopamine agonist use, history of cancer, and Medicare use as potential VTE risk factors. Conclusion: Further development of these tools may provide high diagnostic value and may guide chemoprophylaxis treatment in this setting of high-risk patients.