Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction. Issue 10 (13th October 2021)

• Record Type: Journal Article
• Authors: Kunze, Kyle N.; Polce, Evan M.; Ranawat, Anil S.; Randsborg, Per-Henrik; Williams, Riley J.; Allen, Answorth A.; Nwachukwu, Benedict U.; Pearle, Andrew; Stein, Beth S.; Dines, David; Kelly, Anne; Kelly, Bryan; Rose, Howard; Maynard, Michael; Strickland, Sabrina; Coleman, Struan; Hannafin, Jo; MacGillivray, John; Marx, Robert; Warren, Russell; Rodeo, Scott; Fealy, Stephen; O'Brien, Stephen; Wickiewicz, Thomas; Dines, Joshua S.; Cordasco, Frank; Altcheck, David
• Is Part Of: Orthopaedic journal of sports medicine. Volume 9:Issue 10(2021)
• Publication Date: 2021-10-13
• Subjects: anterior cruciate ligament -- reconstruction; machine learning -- artificial intelligence -- clinically meaningful -- MCID -- IKDC; Sports medicine -- Periodicals; Orthopedics -- Periodicals; Arthroscopy -- Periodicals; Arthroplasty -- Periodicals; Knee -- Surgery -- Periodicals; 616.7
• DOI: 10.1177/23259671211046575
• Languages: English
• ISSNs: 2325-9671

Abstract

Background: Understanding specific risk profiles for each patient and their propensity to experience clinically meaningful improvement after anterior cruciate ligament reconstruction (ACLR) is important for preoperative patient counseling and management of expectations. Purpose: To develop machine learning algorithms to predict achievement of the minimal clinically important difference (MCID) on the International Knee Documentation Committee (IKDC) score at a minimum 2-year follow-up after ACLR. Study Design: Case-control study; Level of evidence, 3. Methods: An ACLR registry of patients from 27 fellowship-trained sports medicine surgeons at a large academic institution was retrospectively analyzed. Thirty-six variables were tested for predictive value. The study population was randomly partitioned into training and independent testing sets using a 70:30 split. Six machine learning algorithms (stochastic gradient boosting, random forest, neural network, support vector machine, adaptive gradient boosting, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and internally validated on the independent set of patients. Algorithm performance was assessed using discrimination, calibration, Brier score, and decision-curve analysis. Results: A total of 442 patients, of whom 39 (8.8%) did not achieve the MCID, were included. The 5 most predictive features of achieving the MCID were body mass index ≤27.4, grade 0 medial collateral ligament examination (compared with other grades), intratunnel femoral tunnel fixation (compared with suspensory), no history of previous contralateral knee surgery, and achieving full knee extension preoperatively. The ENPLR algorithm had the best relative performance (C-statistic, 0.82; calibration intercept, 0.10; calibration slope, 1.15; Brier score, 0.068), demonstrating excellent predictive ability in the study's data set. Conclusion: Machine learning, specifically the ENPLR algorithm, demonstrated good performance for predicting a patient's propensity to achieve the MCID for the IKDC score after ACLR based on preoperative and intraoperative factors. The femoral tunnel fixation method was the only significant intraoperative variable. Range of motion and medial collateral ligament integrity were found to be important physical examination parameters. Increased body mass index and prior contralateral surgery were also significantly predictive of outcome.