Machine Learning Predictions of Subjective Function, Symptoms, and Psychological Readiness at 12 Months After ACL Reconstruction Based on Physical Performance in the Early Rehabilitation Stage: Retrospective Cohort Study

Abstract

Background: Anterior cruciate ligament (ACL) reconstruction (ACLR) aims to restore knee stability and function; however, recovery outcomes vary widely, highlighting the need for predictive tools to guide rehabilitation and patient readiness.

Purpose: To identify the most effective machine learning models for predicting the successful recovery of Patient Acceptable Symptom State (PASS) in terms of subjective function, symptoms, and psychological readiness 12 months after ACLR using physical performance measures obtained 3 months after ACLR.

Study design: Cohort study; Level of evidence, 3.

Methods: The authors retrospectively analyzed the data of 113 patients who underwent single-bundle anatomic ACLR. Physical performance measures at 3 months after ACLR included the Y-balance and isokinetic muscle strength tests. The successful recovery of PASS outcomes at 12 months were assessed using the International Knee Documentation Committee (IKDC) and the ACL-Return to Sport after Injury (ACL-RSI) scale. Five machine learning algorithms were assessed: logistic regression, decision tree, random forest, gradient boosting, and support vector machines.

Results: The gradient boosting model demonstrated the highest area under the curve (AUC) scores for predicting SRPAS of the IKDC (AUC, 0.844; F1, 0.889), and the random forest model demonstrated the highest AUC scores for predicting the successful recovery of PASS of the ACL-RSI (AUC, 0.835; F1, 0.732) during test models. Key predictors of the successful recovery of PASS outcomes included young age and low deficits in the 60 deg/s flexor and extensor peak torque for the IKDC, low 180 deg/s extensor and flexor mean power deficit, and low 60 deg/s flexor peak torque deficits for the ACL-RSI.

Conclusion: Machine learning showed that younger age and greater 3-month isokinetic strength at 60 deg/s predicted attainment of the successful recovery of PASS of the IKDC at 1 year after ACL. Greater 3-month isokinetic strength at 180 deg/s was most predictive of attaining the successful recovery of PASS of the ACL-RSI at 12 months.

Keywords: ACL; Patient Acceptable Symptom State; machine learning; physical therapy/rehabilitation.

Figure. 1.

Flowchart of patient selection and machine learning algorithm development. ACL, anterior cruciate ligament; ACL-RSI, ACL–Return to Sport after Injury; ACLR, ACL reconstruction; AUC, area under the curve; BMI, body mass index; IKDC, International Knee Documentation Committee; SRPAS, successful recovery of patient acceptable symptom state; YBT, Y-balance test.

Figure 2.

(A) Feature permutation importance of gradient boosting model in the training set for predicting SRPAS of the IKDC. (B) Shapley additive explanation analyses of gradient boosting model in the training set for predicting SRPAS of the IKDC. (C) Receiver operating characteristic curves of 5 machine learning algorithms in the training set for predicting SRPAS of the IKDC. AP, average power; AUC, area under the curve; BMI, body mass index; FP, false positive; IKDC, International Knee Documentation Committee; PT, peak torque; SRPAS, successful recovery of patient acceptable symptom state; TP, true positive; YBT, Y-balance test.

Figure 3.

(A) Feature permutation importance of gradient boosting model in the training set for predicting SRPAS of the ACL-RSI. (B) Shapley additive explanation analyses of gradient boosting model in the training set for predicting SRPAS of the ACL-RSI. (C) Receiver operating characteristic curves of 5 machine learning algorithms in the training set for predicting SRPAS of the ACL-RSI. ACL-RSI, Anterior Cruciate Ligament–Return to Sport after Injury; AP, average power; AUC, area under the curve; BMI, body mass index; FP, false positive; PT, peak torque; SRPAS, successful recovery of patient acceptable symptom state; TP, true positive; YBT, Y-balance test.