Establishing Clinically Distinct Patient Treatment Subgroups Following Anterior Cruciate Ligament Reconstruction: A Machine Learning Clustering Analysis

Background: Treatment decisions in patients with anterior cruciate ligament (ACL) injuries are influenced by multiple factors, such as the desire to return to sports or symptomatic instability. Identifying the differential treatment effect of ACL reconstruction (ACLR) compared with nonoperative management on a patient-specific level can inform surgical decision-making. Hypothesis: Unsupervised machine learning can identify distinct patient subgroups based on outcome achievement after ACL injury, and ACLR will exert a protective effect on the development of posttraumatic osteoarthritis (PTOA) over nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: A longitudinal populational registry identified patients with ACL injuries from 1990 to 2016 with a minimum 7.5-year follow-up. An unsupervised random forest algorithm was utilized to develop and validate patient subgroups. Treatment effects of ACLR on outcomes were analyzed using a machine learning causal inference estimator. Patient subgroup membership was incorporated into a multivariable logistic regression to identify factors predictive of optimal outcomes. Results: A total of 923 patients (785 primary ACLR, 138 nonoperative) were included. The random forest algorithm arrived at an optimal partition of 2 subgroups, with 653 patients in the optimal outcome subgroup (368 male [56.4%]; mean age, 26.0 ± 10.2 years; mean body mass index [BMI], 26.5 ± 4.30) and 270 patients in the suboptimal outcome subgroup (152 male [56.3%]; mean age, 35.0 ± 10.1 years; mean BMI, 30.5 ± 5.54). The latter group demonstrated significantly increased rates of secondary meniscal injury, development of symptomatic PTOA, and progression to total knee arthroplasty (TKA) at the final follow-up (all P < .01). In the optimal outcome subgroup, ACLR had significantly protective treatment effects on the risk of secondary meniscal injury (average treatment effect [ATE], 61%), contralateral ACL injury (ATE, 8%), symptomatic PTOA (ATE, 16%), and progression to TKA (ATE, 6%) (all P < .01). Conversely, in the suboptimal outcome subgroup, ACLR only protected against symptomatic PTOA (ATE, 11%) and progression to TKA (ATE, 8%) (both P < .01). Conclusion: Two clinically meaningful subgroups were identified from retrospectively collected data and found to experience differential treatment responses after ACL injuries. ACLR decreased the rate of development of PTOA and TKA in both subgroups but was not as effective in preventing secondary meniscal injuries or contralateral ACL injuries in patients who were older, heavier, or had concomitant medial meniscus injuries.