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Published on: April 30, 2014
Ensemble Algorithm for Risk Prediction of Clinical Failure After Anterior Cruciate Ligament Reconstruction
Tianlun Zhang1, Zipeng Ye1, Jiangyu Cai1
1Department of Sports Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background:
Patient-specific risk profiles of clinical failure after anterior cruciate ligament reconstruction (ACLR) are meaningful for preoperative surgical planning and postoperative rehabilitation guidance.
Purpose:
To create an ensemble algorithm machine learning (ML) model and ML-based web-based tool that can predict the patient-specific risk of clinical failure after ACLR.
Study Design:
Cohort study; Level of evidence, 3.
Methods:
Included were 432 patients (mean age, 26.8 ± 8.4 years; 74.1% male) who underwent anatomic double-bundle ACLR with hamstring tendon autograft between January 2010 and February 2019. The primary outcome was the probability of clinical failure at a minimum 2-year follow-up. The authors included 24 independent variables for feature selection and model development. The data set was split randomly into training sets (75%) and test sets (25%). Models were built using 4 ML algorithms: extreme gradient boosting, random forest, light gradient boosting machine, and adaptive boosting. In addition, a weighted-average voting (WAV) ensemble model was constructed using the ensemble-voting technique to predict clinical failure after ACLR. Concordance (area under the receiver operating characteristic curve [AUC]), calibration, and decision curve analysis were used to evaluate predictive performances of the 5 models.
Results:
Clinical failure occurred in 73 of the 432 patients (16.9%). The 8 most important predictors for clinical failure were follow-up period, high-grade preoperative knee laxity, time from injury to ACLR, participation in competitive sports, posterior tibial slope, graft diameter, age at surgery, and medial meniscus resection. The WAV ensemble algorithm achieved the best predictive performance based on concordance (AUC, 0.9139), calibration (calibration intercept, -0.1806; calibration slope, 1.2794; Brier score, 0.0888), and decision curve analysis (greatest net benefits) and was used to develop an web-based application to predict a patient's clinical failure risk of ACLR.
Conclusion:
The WAV ensemble algorithm was able to accurately predict patient-specific risk of clinical failure after ACLR. Clinicians and patients can use the web-based application during preoperative consultation to understand individual prediction outcomes.
