Machine learning used to determine features of importance linked to overnight stay after patellar tendon repair

Abstract

Purpose: The purpose of this study is to determine if machine learning is an effective method to identify features of patients who may need a longer postoperative stay following a patellar tendon repair. Methods: The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) was used to collect 1173 patients who underwent patellar tendon repair. Machine learning (ML) was then applied to determine features of importance in this patient population. Several algorithms were used: Random Forest, Artificial Neural Network, Gradient Boosting, and Support Vector Machine. These were then compared to the American Society of Anesthesiologists (ASA) classification system based logistic regression as a control. Results: Random Forest (RF) was determined to be the best performing algorithm, with an AUC of 0.72, accuracy of 77.66 %, and precision of 0.79, and recall of 0.96. All other algorithms performed similarly to the control. RF gave the highest permutation feature importance to age (PFI 0.25), BMI (PFI 0.19), ASA classification (PFI 0.14), hematocrit (PFI 0.12), and height (PFI 0.11). Conclusions: This study shows that machine learning can be used as a tool to identify features of importance for length of postoperative stay in patients undergoing patellar tendon repair. RF was found to be a better performing model than logistic regression at determining patients predisposed to longer length of stay as determined by AUC. This supported the study's hypothesis that ML can provide an effective method for identifying features of importance in patients requiring a longer postoperative stay after patellar tendon repair.