Open AccessUsing Predictive Modeling and Supervised Machine Learning to Identify Patients at Risk for Venous Thromboembolism Following Posterior Lumbar Fusion
Author(s) -
Kevin Y. Wang,
Ijezie Ikwuezunma,
Varun Puvanesarajah,
Jacob Babu,
Adam Margalit,
Micheal Raad,
Amit Jain
Publication year - 2021
Publication title -
global spine journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.398
H-Index - 26
eISSN - 2192-5690
pISSN - 2192-5682
DOI - 10.1177/21925682211019361
Subject(s) - medicine , pulmonary embolism , logistic regression , perioperative , venous thrombosis , receiver operating characteristic , retrospective cohort study , lumbar , surgery , thrombosis
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 0.05).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.