Using Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion. Issue 4 (May 2023)

• Record Type: Journal Article
• Authors: Wang, Kevin Y.; Ikwuezunma, Ijezie; Puvanesarajah, Varun; Babu, Jacob; Margalit, Adam; Raad, Micheal; Jain, Amit
• Is Part Of: Global spine journal. Volume 13:Issue 4(2023)
• Page Start: 1097
• Page End: 1103
• Publication Date: 2023-05
• Subjects: spine -- posterior lumbar fusion -- venous thromboembolism -- predictive modeling -- machine learning; Spine -- Diseases -- Periodicals; Spine -- Diseases -- Treatment -- Periodicals; Spine -- Abnormalities -- Periodicals; Spine -- Surgery -- Periodicals; 616.73
• DOI: 10.1177/21925682211019361
• Languages: English
• ISSNs: 2192-5682

Abstract

Study Design: Retrospective review. Objective: To use predictive modeling and machine learning to identify patients at risk for venous thromboembolism (VTE) following posterior lumbar fusion (PLF) for degenerative spinal pathology. Methods: Patients undergoing single-level PLF in the inpatient setting were identified in the National Surgical Quality Improvement Program database. Our outcome measure of VTE included all patients who experienced a pulmonary embolism and/or deep venous thrombosis within 30-days of surgery. Two different methodologies were used to identify VTE risk: 1) a novel predictive model derived from multivariable logistic regression of significant risk factors, and 2) a tree-based extreme gradient boosting (XGBoost) algorithm using preoperative variables. The methods were compared against legacy risk-stratification measures: ASA and Charlson Comorbidity Index (CCI) using area-under-the-curve (AUC) statistic. Results: 13, 500 patients who underwent single-level PLF met the study criteria. Of these, 0.95% had a VTE within 30-days of surgery. The 5 clinical variables found to be significant in the multivariable predictive model were: age > 65, obesity grade II or above, coronary artery disease, functional status, and prolonged operative time. The predictive model exhibited an AUC of 0.716, which was significantly higher than the AUCs of ASA and CCI (all, P < 0.001), and comparable to that of the XGBoost algorithm ( P > 0.05). Conclusion: Predictive analytics and machine learning can be leveraged to aid in identification of patients at risk of VTE following PLF. Surgeons and perioperative teams may find these tools useful to augment clinical decision making risk stratification tool.