Affiliation:
1. Department of Surgery The University of Melbourne Melbourne Victoria Australia
Abstract
AbstractBackgroundCurrent predictive tools for TKA focus on clinicians rather than patients as the intended user. The purpose of this study was to develop a patient‐focused model to predict health‐related quality of life outcomes at 1‐year post‐TKA.MethodsPatients who underwent primary TKA for osteoarthritis from a tertiary institutional registry after January 2006 were analysed. The primary outcome was improvement after TKA defined by the minimal clinically important difference in utility score at 1‐year post‐surgery. Potential predictors included demographic information, comorbidities, lifestyle factors, and patient‐reported outcome measures. Four models were developed, including both conventional statistics and machine learning (artificial intelligence) methods: logistic regression, classification tree, extreme gradient boosted trees, and random forest models. Models were evaluated using discrimination and calibration metrics.ResultsA total of 3755 patients were included in the study. The logistic regression model performed the best with respect to both discrimination (AUC = 0.712) and calibration (intercept = −0.083, slope = 1.123, Brier score = 0.202). Less than 2% (n = 52) of the data were missing and therefore removed for complete case analysis. The final model used age (categorical), sex, baseline utility score, and baseline Veterans‐RAND 12 responses as predictors.ConclusionThe logistic regression model performed better than machine learning algorithms with respect to AUC and calibration plot. The logistic regression model was well calibrated enough to stratify patients into risk deciles based on their likelihood of improvement after surgery. Further research is required to evaluate the performance of predictive tools through pragmatic clinical trials.Level of EvidenceLevel II, decision analysis.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献