Department of Orthopedic Surgery, Hospital for Special Surgery, New York, New York, U.S.A.
Department of Sports Medicine, Department of Orthopedic Surgery, Rush University Medical Center, Chicago, Illinois, U.S.A..
Arthroscopy. 2022 Jun;38(6):2090-2105. doi: 10.1016/j.arthro.2021.12.030. Epub 2021 Dec 27.
To determine what subspecialties have applied machine learning (ML) to predict clinically significant outcomes (CSOs) within orthopaedic surgery and to determine whether the performance of these models was acceptable through assessing discrimination and other ML metrics where reported.
The PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases were queried for articles that used ML to predict achievement of the minimal clinically important difference (MCID), patient acceptable symptomatic state (PASS), or substantial clinical benefit (SCB) after orthopaedic surgical procedures. Data pertaining to demographic characteristics, subspecialty, specific ML algorithms, and algorithm performance were analyzed.
Eighteen articles met the inclusion criteria. Seventeen studies developed novel algorithms, whereas one study externally validated an established algorithm. All studies used ML to predict MCID achievement, whereas 3 (16.7%) predicted SCB achievement and none predicted PASS achievement. Of the studies, 7 (38.9%) concerned outcomes after spine surgery; 6 (33.3%), after sports medicine surgery; 3 (16.7%), after total joint arthroplasty (TJA); and 2 (11.1%), after shoulder arthroplasty. No studies were found regarding trauma, hand, elbow, pediatric, or foot and ankle surgery. In spine surgery, concordance statistics (C-statistics) ranged from 0.65 to 0.92; in hip arthroscopy, 0.51 to 0.94; in TJA, 0.63 to 0.89; and in shoulder arthroplasty, 0.70 to 0.95. Most studies reported C-statistics at the upper end of these ranges, although populations were heterogeneous.
Currently available ML algorithms can discriminate the propensity to achieve CSOs using the MCID after spine, TJA, sports medicine, and shoulder surgery with a fair to good performance as evidenced by C-statistics ranging from 0.6 to 0.95 in most analyses. Less evidence is available on the ability of ML to predict achievement of SCB, and no evidence is available for achievement of the PASS. Such algorithms may augment shared decision-making practices and allow clinicians to provide more appropriate patient expectations using individualized risk assessments. However, these studies remain limited by variable reporting of performance metrics, CSO quantification methods, and adherence to predictive modeling guidelines, as well as limited external validation.
Level III, systematic review of Level III studies.
确定哪些专业已经将机器学习（ML）应用于预测矫形外科中的临床显著结果（CSO），并通过评估报告的判别能力和其他 ML 指标来确定这些模型的性能是否可接受。
在 PubMed、EMBASE 和 Cochrane 对照试验中心注册数据库中查询了使用 ML 预测矫形手术后达到最小临床重要差异（MCID）、患者可接受的症状状态（PASS）或实质性临床获益（SCB）的文章。分析了与人口统计学特征、专业、特定 ML 算法和算法性能相关的数据。
符合纳入标准的文章有 18 篇。17 项研究开发了新的算法，而 1 项研究则对已建立的算法进行了外部验证。所有研究均使用 ML 预测 MCID 的达成情况，而 3 项（16.7%）预测了 SCB 的达成情况，没有研究预测 PASS 的达成情况。这些研究中，7 项（38.9%）涉及脊柱手术后的结果；6 项（33.3%）涉及运动医学手术后的结果；3 项（16.7%）涉及全关节置换术（TJA）后的结果；2 项（11.1%）涉及肩部置换术后的结果。没有研究涉及创伤、手部、肘部、儿科或足部和踝关节手术。脊柱手术中，一致性统计量（C 统计量）范围为 0.65 至 0.92；髋关节镜检查中，0.51 至 0.94；TJA 中，0.63 至 0.89；肩部置换术，0.70 至 0.95。大多数研究报告的 C 统计量都处于这些范围的较高端，尽管人群存在异质性。
目前可用的 ML 算法可以通过使用 MCID 来区分脊柱、TJA、运动医学和肩部手术后达到 CSO 的倾向，在大多数分析中，C 统计量在 0.6 至 0.95 之间，表现出公平到良好的性能。关于 ML 预测 SCB 达成的能力的证据较少，而关于达成 PASS 的证据则没有。这些算法可以增强共同决策实践，并允许临床医生使用个体化风险评估为患者提供更合适的预期。然而，这些研究仍然受到报告性能指标、CSO 量化方法以及预测建模指南的一致性以及外部验证的限制。
III 级，对 III 级研究的系统评价。
