Machine learning can accurately predict risk factors for all‐cause reoperation after ACLR: creating a clinical tool to improve patient counseling and outcomes

Abstract

Purpose Identifying predictive factors for all-cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all-cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression. Methods A longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all-cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature inluence on the predictions, we utilized a game-theorybased method through SHapley Additive exPlanations. Results A total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow-up of 9 years. Two-hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identiied the following risk factors as predictive for all-cause reoperation: diagnosis of systemic inlammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial ixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best-performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard. Conclusions All-cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identiied diagnosis of systemic inlammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial ixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction. Level of evidence III.

Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research

“…The use of large data registries has gained much attention for developing and validating predictive models using AI [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. These analyses have leveraged the statistical power of large data sets to better understand the propensity for adverse events, cost of episodes of care and resource utilizationphenomenon either not readily available or too rare to be studied using institutional data sets.…”