Preoperatively predicting failure to achieve the minimum clinically important difference and substantial clinical benefit for total knee arthroplasty patients using machine learning.

IF 4.4 Q2 Medicine
Jaeyoung Park, Emilie N Miley, Xiang Zhong, Chancellor F Gray
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Abstract

Background: A clear understanding of minimal clinically important difference (MCID) and substantial clinical benefit (SCB) is essential for effectively implementing patient-reported outcome measurements (PROMs) as a performance measure for total knee arthroplasty (TKA). Since not achieving MCID and SCB may reflect suboptimal surgical benefit, the primary aim of this study was to use machine learning to predict patients who may not achieve the threshold-based outcomes (i.e., MCID and SCB) on the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) following TKA.

Methods: Data from 1064 patients who underwent TKA at a single academic medical center between 2016 and 2022 contained 81 preoperative variables, including routinely collected measures and PROMs (KOOS JR and Patient-Reported Outcomes Measurement Information Systems [PROMIS-10]). Several machine-learning models were developed, which include penalized logistic regression as a linear model, support vector machine with polynomial and radial kernels as nonlinear models, and random forest and extreme gradient boosting as nonparametric models. These models predicted both distribution- and anchor-based MCIDs and SCB. In addition, logistic regression models were used to identify relevant risk factors for failing to meet these thresholds.

Results: The random forest models and the penalized logistic regression models achieved acceptable area under the receiver operating characteristic curve (AUC) close to or above 0.7 for all the outcomes. Furthermore, the logistic regression models identified shared risk factors for the three outcomes: preoperative PROMs (i.e., KOOS JR score, PROMIS-10 global physical T-score, and PROMIS-10 general mental health), antidepressant medication history, age, and Kellgren-Lawrence grade.

Conclusions: Machine-learning models were able to identify patients at risk of failure to achieve the threshold-based metrics and relevant preoperative factors. As such, these models may be used to both improve shared decision-making and help create risk-stratification tools to improve quality assessment of surgical outcomes.

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Abstract Image

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术前使用机器学习预测全膝关节置换术患者实现最小临床重要差异和实质性临床获益的失败。
背景:明确了解最小临床重要差异(MCID)和实质性临床获益(SCB)对于有效实施患者报告的结果测量(PROMs)作为全膝关节置换术(TKA)的绩效衡量标准至关重要。由于未达到MCID和SCB可能反映了手术效果不佳,因此本研究的主要目的是使用机器学习来预测TKA后膝关节损伤和骨关节炎结局评分(oos JR)中可能未达到阈值结果(即MCID和SCB)的患者。方法:来自2016年至2022年在单一学术医疗中心接受TKA的1064例患者的数据包含81个术前变量,包括常规收集的测量和PROMs (oos JR和患者报告的结果测量信息系统[允诺-10])。开发了几种机器学习模型,其中包括惩罚逻辑回归作为线性模型,具有多项式和径向核的支持向量机作为非线性模型,以及随机森林和极端梯度增强作为非参数模型。这些模型预测了基于分布和锚点的MCIDs和SCB。此外,使用逻辑回归模型来确定未能达到这些阈值的相关危险因素。结果:随机森林模型和惩罚logistic回归模型在受试者工作特征曲线(AUC)下的可接受区域均接近或大于0.7。此外,logistic回归模型确定了三个结局的共同危险因素:术前PROMs(即oos JR评分、promise -10整体身体t -评分和promise -10一般心理健康)、抗抑郁药物史、年龄和kelglen - lawrence评分。结论:机器学习模型能够识别有失败风险的患者,以达到阈值为基础的指标和相关的术前因素。因此,这些模型可用于改善共同决策,并有助于创建风险分层工具,以提高手术结果的质量评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.50
自引率
0.00%
发文量
42
审稿时长
19 weeks
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