Using machine learning and big data for the prediction of venous thromboembolic events after spine surgery: A single-center retrospective analysis of multiple models on a cohort of 6869 patients

Benjamin S Hopkins; Michael B Cloney; Ekamjeet S Dhillon; Pavlos Texakalidis; Jonathan Dallas; Vincent N Nguyen; Matthew Ordon; Najib El Tecle; Thomas C Chen; Patrick C Hsieh; John C Liu; Tyler R Koski; Nader S Dahdaleh

doi:10.4103/jcvjs.jcvjs_69_23

Using machine learning and big data for the prediction of venous thromboembolic events after spine surgery: A single-center retrospective analysis of multiple models on a cohort of 6869 patients

J Craniovertebr Junction Spine. 2023 Jul-Sep;14(3):221-229. doi: 10.4103/jcvjs.jcvjs_69_23. Epub 2023 Sep 18.

Affiliations

¹ Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
² Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
³ Department of Orthopaedics and Sports Medicine, University of Washington, Seattle, WA, USA.

Abstract

Objective: Venous thromboembolic event (VTE) after spine surgery is a rare but potentially devastating complication. With the advent of machine learning, an opportunity exists for more accurate prediction of such events to aid in prevention and treatment.

Methods: Seven models were screened using 108 database variables and 62 preoperative variables. These models included deep neural network (DNN), DNN with synthetic minority oversampling technique (SMOTE), logistic regression, ridge regression, lasso regression, simple linear regression, and gradient boosting classifier. Relevant metrics were compared between each model. The top four models were selected based on area under the receiver operator curve; these models included DNN with SMOTE, linear regression, lasso regression, and ridge regression. Separate random sampling of each model was performed 1000 additional independent times using a randomly generated training/testing distribution. Variable weights and magnitudes were analyzed after sampling.

Results: Using all patient-related variables, DNN using SMOTE was the top-performing model in predicting postoperative VTE after spinal surgery (area under the curve [AUC] =0.904), followed by lasso regression (AUC = 0.894), ridge regression (AUC = 0.873), and linear regression (AUC = 0.864). When analyzing a subset of only preoperative variables, the top-performing models were lasso regression (AUC = 0.865) and DNN with SMOTE (AUC = 0.864), both of which outperform any currently published models. Main model contributions relied heavily on variables associated with history of thromboembolic events, length of surgical/anesthetic time, and use of postoperative chemoprophylaxis.

Conclusions: The current study provides promise toward machine learning methods geared toward predicting postoperative complications after spine surgery. Further study is needed in order to best quantify and model real-world risk for such events.

Keywords: Big data; machine learning; venous thromboembolic events; venous thromboembolism.