PMID- 34671691 OWN - NLM STAT- PubMed-not-MEDLINE LR - 20220427 IS - 2325-9671 (Print) IS - 2325-9671 (Electronic) IS - 2325-9671 (Linking) VI - 9 IP - 10 DP - 2021 Oct TI - Application of Machine Learning Algorithms to Predict Clinically Meaningful Improvement After Arthroscopic Anterior Cruciate Ligament Reconstruction. PG - 23259671211046575 LID - 10.1177/23259671211046575 [doi] LID - 23259671211046575 AB - 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