Amendola, Annunziato

Background: As machine learning becomes increasingly utilized in orthopaedic clinical research, the application of machine learning methodology to cohort data from the Multicenter ACL Revision Study (MARS) presents a valuable opportunity to translate data into patient-specific insights. Purpose: To apply novel machine learning methodology to MARS cohort data to determine a predictive model of revision anterior cruciate ligament reconstruction (rACLR) graft failure and features most predictive of failure. Study Design: Cohort study; Level of evidence, 3. Methods: The authors prospectively recruited patients undergoing rACLR from the MARS cohort and obtained preoperative radiographs, surgeon-reported intraoperative findings, and 2- and 6-year follow-up data on patient-reported outcomes, additional surgeries, and graft failure. Machine learning models including logistic regression (LR), XGBoost, gradient boosting (GB), random forest (RF), and a validated ensemble algorithm (AutoPrognosis) were built to predict graft failure by 6 years postoperatively. Validated performance metrics and feature importance measures were used to evaluate model performance. Results: The cohort included 960 patients who completed 6-year follow-up, with 5.7% (n = 55) experiencing graft failure. AutoPrognosis demonstrated the highest discriminative power (model area under the receiver operating characteristic curve: AutoPrognosis, 0.703; RF, 0.618; GB, 0.660; XGBoost, 0.680; LR, 0.592), with well-calibrated scores (model Brier score: AutoPrognosis, 0.053; RF, 0.054; GB, 0.057; XGBoost, 0.058; LR, 0.111). The most important features for AutoPrognosis model performance were prior compromised femoral and tibial tunnels (placement and size) and allograft graft type used in current rACLR. Conclusion: The present study demonstrated the ability of the novel AutoPrognosis machine learning model to best predict the risk of graft failure in patients undergoing rACLR at 6 years postoperatively with moderate predictive ability. Femoral and tibial tunnel size and position in prior ACLR and allograft use in current rACLR were all risk factors for rACLR failure in the context of the AutoPrognosis model. This study describes a unique model that can be externally validated with larger data sets and contribute toward the creation of a robust rACLR bedside risk calculator in future studies. Registration: NCT00625885 (ClinicalTrials.gov identifier).

AbstractInfection is a rare occurrence after revision anterior cruciate ligament reconstruction (rACLR). Because of the low rates of infection, it has been difficult to identify risk factors for infection in this patient population. The purpose of this study was to report the rate of infection following rACLR and assess whether infection is associated with patient‐ and surgeon‐dependent risk factors. We reviewed two large prospective cohorts to identify patients with postoperative infections following rACLR. Age, sex, body mass index (BMI), smoking status, history of diabetes, and graft choice were recorded for each patient. The association of these factors with postoperative infection following rACLR was assessed. There were 1423 rACLR cases in the combined cohort, with 9 (0.6%) reporting postoperative infections. Allografts had a higher risk of infection than autografts (odds ratio, 6.8; 95% CI, 0.9–54.5; p = .045). Diabetes (odds ratio, 28.6; 95% CI, 5.5–149.9; p = .004) was a risk factor for infection. Patient age, sex, BMI, and smoking status were not associated with risk of infection after rACLR.