Hofstetter Léonie, Schweyckart Nathalie, Seiler Christof, Brand Christian, Rosella Laura C, Farshad Mazda, Puhan Milo A, Hincapié Cesar A
Musculoskeletal Epidemiology Research Group, University of Zurich and Balgrist University Hospital, Forchstrasse 340, Zurich, 8008, Switzerland.
Epidemiology, Biostatistics and Prevention Institute (EBPI), University of Zurich, Zurich, Switzerland.
Diagn Progn Res. 2025 Aug 4;9(1):16. doi: 10.1186/s41512-025-00200-z.
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.
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.
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.
预测全髋关节置换术（THA）或全膝关节置换术（TKA）后患者报告的结局以及翻修手术风险，可为临床决策、卫生资源分配和护理规划提供依据。机器学习（ML）算法越来越多地被用作传统逻辑回归（LR）预测的替代方法，但它们在整体模型性能方面的优越性尚不确定。本研究的目的是比较LR与不同ML方法在预测THA和TKA后患者结局及翻修手术风险方面的预测性能。
将利用瑞士进行的所有初次和翻修THA及TKA手术的常规收集数据，并在瑞士国家关节登记处（SIRIS）登记，开展一项基于人群的历史性队列研究。纳入2015年1月1日至2023年12月31日期间因原发性骨关节炎接受手术的年龄≥18岁的患者。感兴趣的结局将包括：（1）术后12个月疼痛结局不佳（定义为在11分制（0至10）数字评分量表上疼痛改善不足50%或疼痛绝对减轻不足3分）和满意度结局不佳；（2）初次手术后5年内的早期翻修。预先设定的预测变量将包括人口统计学特征、合并症评分、患者报告健康状况指标和手术变量。将使用整体预测准确性、区分度和校准度的测量指标来比较预测性能，并进行决策曲线分析以评估模型的临床实用性。模型将通过交叉验证进行内部验证，并通过地理验证进行外部验证。模型的开发将参考多变量个体预后或诊断预测模型的更新透明报告（TRIPOD + AI）声明。
本研究将使用SIRIS数据开发、验证和比较THA和TKA后患者报告结局及翻修手术风险的预测模型。
