Klemt Christian, Uzosike Akachimere Cosmas, Esposito John G, Harvey Michael Joseph, Yeo Ingwon, Subih Murad, Kwon Young-Min
Bioengineering Laboratory, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
Arch Orthop Trauma Surg. 2023 Apr;143(4):2235-2245. doi: 10.1007/s00402-022-04526-x. Epub 2022 Jun 29.
Patient-reported outcome measures (PROMs) are increasingly used as quality benchmark in total hip and knee arthroplasty (THA; TKA) due to bundled payment systems that aim to provide a patient-centered, value-based treatment approach. However, there is a paucity of predictive tools for postoperative PROMs. Therefore, this study aimed to develop and validate machine learning models for the prediction of numerous patient-reported outcome measures following primary hip and knee total joint arthroplasty.
A total of 4526 consecutive patients (2137 THA; 2389 TKA) who underwent primary hip and knee total joint arthroplasty and completed both pre- and postoperative PROM scores was evaluated in this study. The following PROM scores were included for analysis: HOOS-PS, KOOS-PS, Physical Function SF10A, PROMIS SF Physical and PROMIS SF Mental. Patient charts were manually reviewed to identify patient demographics and surgical variables associated with postoperative PROM scores. Four machine learning algorithms were developed to predict postoperative PROMs following hip and knee total joint arthroplasty. Model assessment was performed through discrimination, calibration and decision curve analysis.
The factors most significantly associated with the prediction of postoperative PROMs include preoperative PROM scores, Charlson Comorbidity Index, American Society of Anaesthesiology score, insurance status, age, length of hospital stay, body mass index and ethnicity. The four machine learning models all achieved excellent performance across discrimination (AUC > 0.83), calibration and decision curve analysis.
This study developed machine learning models for the prediction of patient-reported outcome measures at 1-year following primary hip and knee total joint arthroplasty. The study findings show excellent performance on discrimination, calibration and decision curve analysis for all four machine learning models, highlighting the potential of these models in clinical practice to inform patients prior to surgery regarding their expectations of postoperative functional outcomes following primary hip and knee total joint arthroplasty.
Level III, case control retrospective analysis.
由于旨在提供以患者为中心、基于价值的治疗方法的捆绑支付系统，患者报告结局测量指标（PROMs）在全髋关节和膝关节置换术（THA；TKA）中越来越多地被用作质量基准。然而，术后PROMs的预测工具匮乏。因此，本研究旨在开发并验证机器学习模型，以预测初次髋膝关节全关节置换术后多种患者报告结局测量指标。
本研究评估了4526例连续接受初次髋膝关节全关节置换术并完成术前和术后PROM评分的患者（2137例THA；2389例TKA）。纳入分析的PROM评分如下：HOOS-PS、KOOS-PS、身体功能SF10A、PROMIS SF身体和PROMIS SF心理。人工查阅患者病历，以确定与术后PROM评分相关的患者人口统计学和手术变量。开发了四种机器学习算法，以预测髋膝关节全关节置换术后的PROMs。通过区分度、校准和决策曲线分析进行模型评估。
与术后PROMs预测最显著相关的因素包括术前PROM评分、Charlson合并症指数、美国麻醉医师协会评分、保险状况、年龄、住院时间、体重指数和种族。这四种机器学习模型在区分度（AUC＞0.83）、校准和决策曲线分析方面均表现出色。
本研究开发了机器学习模型，用于预测初次髋膝关节全关节置换术后1年的患者报告结局测量指标。研究结果显示，所有四种机器学习模型在区分度、校准和决策曲线分析方面均表现出色，突出了这些模型在临床实践中的潜力，可为患者在手术前提供有关初次髋膝关节全关节置换术后功能结局预期的信息。
III级，病例对照回顾性分析。
