Abstract：
Supervised machine learning techniques have been increasingly applied to predict patient outcomes after hip and knee arthroplasty procedures. The purpose of this study was to systematically review the applications of supervised machine learning techniques to predict patient outcomes after primary total hip and knee arthroplasty.
A comprehensive literature search using the electronic databases MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted in July of 2021. The inclusion criteria were studies that utilized supervised machine learning techniques to predict patient outcomes after primary total hip or knee arthroplasty.
Search criteria yielded n = 30 relevant studies. Topics of study included patient complications (n = 6), readmissions (n = 1), revision (n = 2), patient-reported outcome measures (n = 4), patient satisfaction (n = 4), inpatient status and length of stay (LOS) (n = 9), opioid usage (n = 3), and patient function (n = 1). Studies involved TKA (n = 12), THA (n = 11), or a combination (n = 7). Less than 35% of predictive outcomes had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range. Additionally, only 9 of the studies found improvement over logistic regression, and only 9 studies were externally validated.
Supervised machine learning algorithms are powerful tools that have been increasingly applied to predict patient outcomes after total hip and knee arthroplasty. However, these algorithms should be evaluated in the context of prognostic accuracy, comparison to traditional statistical techniques for outcome prediction, and application to populations outside the training set. While machine learning algorithms have been received with considerable interest, they should be critically assessed and validated prior to clinical adoption.
A comprehensive literature search using the electronic databases MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Systematic Reviews was conducted in July of 2021. The inclusion criteria were studies that utilized supervised machine learning techniques to predict patient outcomes after primary total hip or knee arthroplasty.
Search criteria yielded n = 30 relevant studies. Topics of study included patient complications (n = 6), readmissions (n = 1), revision (n = 2), patient-reported outcome measures (n = 4), patient satisfaction (n = 4), inpatient status and length of stay (LOS) (n = 9), opioid usage (n = 3), and patient function (n = 1). Studies involved TKA (n = 12), THA (n = 11), or a combination (n = 7). Less than 35% of predictive outcomes had an area under the receiver operating characteristic curve (AUC) in the excellent or outstanding range. Additionally, only 9 of the studies found improvement over logistic regression, and only 9 studies were externally validated.
Supervised machine learning algorithms are powerful tools that have been increasingly applied to predict patient outcomes after total hip and knee arthroplasty. However, these algorithms should be evaluated in the context of prognostic accuracy, comparison to traditional statistical techniques for outcome prediction, and application to populations outside the training set. While machine learning algorithms have been received with considerable interest, they should be critically assessed and validated prior to clinical adoption.
摘要：
背景：监督机器学习技术越来越多地用于预测髋关节和膝关节置换术后的患者预后。这项研究的目的是系统地回顾监督机器学习技术在预测初次全髋关节和膝关节置换术后患者预后中的应用。
方法：使用电子数据库MEDLINE进行全面的文献检索，EMBASE,Cochrane中央控制试验登记册,和Cochrane系统评价数据库于2021年7月进行。纳入标准是利用监督机器学习技术预测初次全髋关节或膝关节置换术后患者预后的研究。
结果：搜索标准产生了n=30项相关研究。研究主题包括患者并发症（n=6），再入院(n=1)，修订(n=2)，患者报告的结局指标（n=4），患者满意度(n=4)，住院状态和住院时间（LOS）（n=9），阿片类药物的使用（n=3），和患者功能（n=1）。研究涉及TKA（n=12），THA(n=11),或组合（n=7）。少于35％的预测结果的受试者工作特征曲线（AUC）下面积在出色或出色的范围内。此外,只有9项研究发现优于逻辑回归，只有9项研究得到了外部验证。
结论：有监督的机器学习算法是强大的工具，越来越多地应用于预测全髋关节和膝关节置换术后的患者预后。然而,这些算法应该在预后准确性的背景下进行评估，与传统的结果预测统计技术相比，并应用于训练集之外的人群。虽然机器学习算法受到了相当大的兴趣，在临床采用之前，应对它们进行严格评估和验证。
方法：使用电子数据库MEDLINE进行全面的文献检索，EMBASE,Cochrane中央控制试验登记册,和Cochrane系统评价数据库于2021年7月进行。纳入标准是利用监督机器学习技术预测初次全髋关节或膝关节置换术后患者预后的研究。
结果：搜索标准产生了n=30项相关研究。研究主题包括患者并发症（n=6），再入院(n=1)，修订(n=2)，患者报告的结局指标（n=4），患者满意度(n=4)，住院状态和住院时间（LOS）（n=9），阿片类药物的使用（n=3），和患者功能（n=1）。研究涉及TKA（n=12），THA(n=11),或组合（n=7）。少于35％的预测结果的受试者工作特征曲线（AUC）下面积在出色或出色的范围内。此外,只有9项研究发现优于逻辑回归，只有9项研究得到了外部验证。
结论：有监督的机器学习算法是强大的工具，越来越多地应用于预测全髋关节和膝关节置换术后的患者预后。然而,这些算法应该在预后准确性的背景下进行评估，与传统的结果预测统计技术相比，并应用于训练集之外的人群。虽然机器学习算法受到了相当大的兴趣，在临床采用之前，应对它们进行严格评估和验证。
