BackgroundThe objective of this study is to develop predictive models for persistent opioid use following lower extremity joint arthroplasty and determine if ensemble learning and an oversampling technique may improve model performance.MethodsWe compared various predictive models to identify at-risk patients for persistent postoperative opioid use using various preoperative, intraoperative, and postoperative data, including surgical procedure, patient demographics/characteristics, past surgical history, opioid use history, comorbidities, lifestyle habits, anesthesia details, and postoperative hospital course. Six classification models were evaluated: logistic regression, random forest classifier, simple-feedforward neural network, balanced random forest classifier, balanced bagging classifier, and support vector classifier. Performance with Synthetic Minority Oversampling Technique (SMOTE) was also evaluated. Repeated stratified k-fold cross-validation was implemented to calculate F1-scores and area under the receiver operating characteristics curve (AUC).ResultsThere were 1042 patients undergoing elective knee or hip arthroplasty in which 242 (23.2%) reported persistent opioid use. Without SMOTE, the logistic regression model has an F1 score of 0.47 and an AUC of 0.79. All ensemble methods performed better, with the balanced bagging classifier having an F1 score of 0.80 and an AUC of 0.94. SMOTE improved performance of all models based on F1 score. Specifically, performance of the balanced bagging classifier improved to an F1 score of 0.84 and an AUC of 0.96. The features with the highest importance in the balanced bagging model were postoperative day 1 opioid use, body mass index, age, preoperative opioid use, prescribed opioids at discharge, and hospital length of stay.ConclusionsEnsemble learning can dramatically improve predictive models for persistent opioid use. Accurate and early identification of high-risk patients can play a role in clinical decision making and early optimization with personalized interventions.

BACKGROUND: Days before surgery, add-ons may be scheduled to fill unused surgical block time at an outpatient surgery center. At times, outpatient surgery centers have time limitations for end of block time and discharge from the postanesthesia care unit (PACU). The objective of our study was to develop machine learning models that predicted the following composite outcome: (1) surgery finished by end of operating room block time and (2) patient was discharged by end of recovery room nursing shift. We compared various machine learning models to logistic regression. By evaluating various performance metrics, including F1 scores, we hypothesized that models using ensemble learning will be superior to logistic regression. METHODS: Data were collected from patients at an ambulatory surgery center. The primary outcome measurement was determined to have a value of 1 (versus 0) if they met both criteria: (1) surgery ends by 5 pm and (2) patient is discharged from the recovery room by 7 pm . We developed models to determine if a procedure would meet both criteria if it were scheduled at 1 pm , 2 pm , 3 pm , or 4 pm . We implemented regression, random forest, balanced random forest, balanced bagging, neural network, and support vector classifier, and included the following features: surgery, surgeon, service line, American Society of Anesthesiologists score, age, sex, weight, and scheduled case duration. We evaluated model performance with Synthetic Minority Oversampling Technique (SMOTE). We compared the following performance metrics: F1 score, area under the receiver operating characteristic curve (AUC), specificity, sensitivity, precision, recall, and Matthews correlation coefficient. RESULTS: Among 13,447 surgical procedures, the median total perioperative time (actual case duration and PACU length stay) was 165 minutes. When SMOTE was not used, when predicting whether surgery will end by 5 pm and patient will be discharged by 7 pm , the average F1 scores were best with random forest, balanced bagging, and balanced random forest classifiers. When SMOTE was used, these models had improved F1 scores compared to no SMOTE. The balanced bagging classifier performed best with F1 score of 0.78, 0.80, 0.82, and 0.82 when predicting our outcome if cases were to start at 1 pm , 2 pm , 3 pm , or 4 pm , respectively. CONCLUSIONS: We demonstrated improvement in predicting the outcome at a range of start times when using ensemble learning versus regression techniques. Machine learning may be adapted by operating room management to allow for a better determination whether an add-on case at an outpatient surgery center...

Background Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration. Objective The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. Methods We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance. Results A total of 3189 patients who underwent spine surgery were included. The institution’s current method of predicting case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. Conclusions Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.

BACKGROUND Estimating surgical case duration accurately is an important operating room efficiency metric. OBJECTIVE The primary objective of this 4-year, single academic center retrospective study was to utilize an ensemble learning approach to improve the accuracy of scheduled case duration for spine surgery. The primary outcome measure was case duration. METHODS We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustment as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost and calculated average R2, root mean squared error (RMSE), and mean absolute error (MAE) using stratified k-folds cross-validation. We then used the Shapley Additive exPlanations (SHAP) explainer model to determine feature importance. RESULTS 3,315 patients who underwent spine surgery were included. The institution’s current method of predicting case times had poor coefficient of determination with actual times (R2 = 0.19). On k-folds cross-validation, the linear regression model had an R2 of 0.34, RMSE of 165.3, and MAE of 128.4. Among all models, the XGBoost regressor performed the best with an R2 of 0.70, RMSE of 110.9, and MAE of 75.8. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model. CONCLUSIONS Utilizing ensemble learning-based predictive models, specifically XGBoost regression, can improve accuracy of the estimation of spine surgery times. CLINICALTRIAL N/A