Predicting patient outcomes and risk for revision surgery after hip and knee replacement surgery: study protocol for a comparison of modelling approaches using the Swiss National Joint Registry (SIRIS) - Léonie Hofstetter; Nathalie Schweyckart; Christof Seiler; Christian Brand; Laura C. Rosella; Mazda Farshad; Milo A. Puhan; Cesar A. Hincapié (Diagnostic and Prognostic Research)
Abstract Background Prediction of postoperative patient-reported outcomes and risk for revision surgery after total hip arthroplasty (THA) or total knee arthroplasty (TKA) can inform clinical decision-making, health resource allocation, and care planning. Machine learning (ML) algorithms are increasingly used as an alternative to traditional logistic regression (LR) prediction, but there is uncertainty about their superiority in overall model performance. The aim of this study is to compare the predictive performance of LR with different ML approaches for predicting patient outcomes and risk for revision surgery after THA and TKA. Methods A population-based historical cohort study will be developed using routinely collected data from all primary and revision THA and TKA procedures performed in Switzerland and registered in the Swiss National Joint Registry (SIRIS). Patients of age ≥ 18 years with surgery for primary osteoarthritis from 01 January 2015 up to 31 December 2023 will be included. Outcomes of interest will be (1) 12-month postoperative poor pain outcome (defined as < 50% improvement of pain or < 3 absolute reduction in pain on a 11-point (0 to 10) numeric rating scale) and poor satisfaction outcome, and (2) early revision within 5 years after primary surgery. Prespecified predictor variables will include demographic characteristics, comorbidity score, patient-reported health status measures, and surgical variables. Measures of overall predictive accuracy, discrimination, and calibration will be used to compare predictive performance, and decision curve analysis performed to evaluate the clinical usefulness of models. The models will be internally validated using cross-validation and externally validated using geographical validation. Development of the models will be informed by the updated Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD + AI) statement. Discussion This study will develop, validate, and compare prediction models for postoperative patient-reported outcomes and risk for revision surgery after THA and TKA using SIRIS data.