The Role of Minimally Invasive Techniques in Scoliosis Correction Surgery

Objective. Recently, minimally invasive surgery (MIS) has been included among the treatment modalities for scoliosis. However, literature comparing MIS to open surgery for scoliosis correction is limited. The objective of this study was to compare outcomes for scoliosis correction patients undergoing MIS versus open approach. Methods. We retrospectively collected data on demographics, procedure characteristics, and outcomes for 207 consecutive scoliosis correction surgeries at our institution between 2009 and 2015. Results. MIS patients had lower number of levels fused (p<0.0001), shorter surgeries (p=0.0023), and shorter overall lengths of stay (p<0.0001), were less likely to be admitted to the ICU (p<0.0001), and had shorter ICU stays (p=0.0015). On multivariable regression, number of levels fused predicted selection for MIS procedure (p=0.004), and multiple other variables showed trends toward significance. Age predicted ICU admission and VTE. BMI predicted any VTE, and DVT specifically. Comorbid disease burden predicted readmission, need for transfusion, and ICU admission. Number of levels fused predicted prolonged surgery, need for transfusion, and ICU admission. Conclusions. Patients undergoing MIS correction had shorter surgeries, shorter lengths of stay, and shorter and fewer ICU stays, but there was a significant selection effect. Accounting for other variables, MIS did not independently predict any of the outcomes

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

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