Jacobien H F Oosterhoff, Anne A H de Hond, Rinne M Peters, Liza N van Steenbergen, Juliette C Sorel, Wierd P Zijlstra, Rudolf W Poolman, David Ring, Paul C Jutte, Gino M M J Kerkhoffs, Hein Putter, Ewout W Steyerberg, Job N Doornberg
BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty...
March 12, 2024: Clinical Orthopaedics and related Research