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Machine Learning Can Accurately Predict Overnight Stay, Readmission, and 30-Day Complications Following Anterior Cruciate Ligament Reconstruction

      ABSTRACT

      Purpose

      This study aimed to develop machine learning models to predict hospital admission (overnight stay) as well as short-term complications and readmission rates following ACLR. Furthermore, we sought to compare the ML models with logistic regression models in predicting ACLR outcomes.

      Methods

      The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent elective ACLR from 2012-2018. Artificial neural network (ANN) ML and logistic regression models were developed to predict overnight stay, 30-day postoperative complications, and ACL-related readmission, and model performance was compared using the area under the ROC curve (AUC). Regression analyses were used to identify variables that were significantly associated with the predicted outcomes.

      Results

      A total of 21,636 elective ACLR cases met inclusion criteria. Variables associated with hospital admission include white race, obesity, hypertension, and ASA classification 3 and greater, anesthesia other than general, prolonged operative time, and inpatient setting. The incidence of hospital admission (overnight stay) was 10.2%, 30-day complications was 1.3%, and 30-day readmission for ACLR-related causes was 0.9%. Compared to logistic regression models, ANN models reported superior AUC values in predicting overnight stay (0.835 vs. 0.589), 30-day complications (0.742 vs. 0.590), reoperation (0.842 vs. 0.601), ACLR-related readmission (0.872 vs. 0.606), DVT (0.804 vs. 0.608), and surgical site infection (SSI) (0.818 vs. 0.596).

      Conclusions

      The ML models developed in this study demonstrate an application of ML in which data from a national surgical patient registry was used to predict hospital admission and 30-day postoperative complications after elective ACLR. Machine learning models developed performed well, outperforming regression models in predicting hospital admission and short-term complications following elective ACLR. ML models performed best when predicting ACLR-related readmissions and reoperations, followed by overnight stay.

      Level of Evidence

      IV, retrospective comparative prognostic trial.

      Keywords

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