A Novel Machine Learning Model to Predict Revision ACL Reconstruction Failure in the MARS Cohort
Authors:   MARS Group, Vasavada Kinjal , Vasavada Vrinda , Moran Jay , Devana Sai , Lee Changhee , Hame Sharon L. , Jazrawi Laith M. , Sherman Orrin H. , Huston Laura J. , Haas Amanda K. , Allen Christina R. , Cooper Daniel E. , DeBerardino Thomas M. , Spindler Kurt P. , Stuart Michael J. , (Ned) Amendola Annunziato , Annunziata Christopher C. , Arciero Robert A. , Bach Bernard R. , Baker Champ L. , Bartolozzi Arthur R. , Baumgarten Keith M. , Berg Jeffrey H. , Bernas Geoffrey A. , Brockmeier Stephen F. , Brophy Robert H. , Bush-Joseph Charles A. , Butler V J. Brad , Carey James L. , Carpenter James E. , Cole Brian J. , Cooper Jonathan M. , Cox Charles L. , Creighton R. Alexander , David Tal S. , Dunn Warren R. , Flanigan David C. , Frederick Robert W. , Ganley Theodore J. , Gatt Charles J. , Gecha Steven R. , Giffin James Robert , Hannafin Jo A. , Lindsay Harris Norman , Hechtman Keith S. , Hershman Elliott B. , Hoellrich Rudolf G. , Johnson David C. , Johnson Timothy S. , Jones Morgan H. , Kaeding Christopher C. , Kamath Ganesh V. , Klootwyk Thomas E. , Levy Bruce A. , Ma C. Benjamin , Maiers G. Peter , Marx Robert G. , Matava Matthew J. , Mathien Gregory M. , McAllister David R. , McCarty Eric C. , McCormack Robert G. , Miller Bruce S. , Nissen Carl W. , O’Neill Daniel F. , Owens Brett D. , Parker Richard D. , Purnell Mark L. , Ramappa Arun J. , Rauh Michael A. , Rettig Arthur C. , Sekiya Jon K. , Shea Kevin G. , Slauterbeck James R. , Smith Matthew V. , Spang Jeffrey T. , Svoboda Steven J. , Taft Timothy N. , Tenuta Joachim J. , Tingstad Edwin M. , Vidal Armando F. , Viskontas Darius G. , White Richard A. , Williams James S. , Wolcott Michelle L. , Wolf Brian R. , Wright Rick W. , York James J. 
Published: November 2024

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).