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).

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 pe

SUBMITTER: Hofstetter L 

PROVIDER: S-EPMC12320300 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature