Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
J Eval Clin Pract. 2023 Mar;29(2):292-299. doi: 10.1111/jep.13765. Epub 2022 Sep 13.
Social determinants of health (SDOH) are being considered more frequently when providing orthopaedic care due to their impact on treatment outcomes. Simultaneously, prognostic machine learning (ML) models that facilitate clinical decision making have become popular tools in the field of orthopaedic surgery. When ML-driven tools are developed, it is important that the perpetuation of potential disparities is minimized. One approach is to consider SDOH during model development. To date, it remains unclear whether and how existing prognostic ML models for orthopaedic outcomes consider SDOH variables.
To investigate whether prognostic ML models for orthopaedic surgery outcomes account for SDOH, and to what extent SDOH variables are included in the final models.
A systematic search was conducted in PubMed, Embase and Cochrane for studies published up to 17 November 2020. Two reviewers independently extracted SDOH features using the PROGRESS+ framework (place of residence, race/ethnicity, Occupation, gender/sex, religion, education, social capital, socioeconomic status, 'Plus+' age, disability, and sexual orientation).
The search yielded 7138 studies, of which 59 met the inclusion criteria. Across all studies, 96% (57/59) considered at least one PROGRESS+ factor during development. The most common factors were age (95%; 56/59) and gender/sex (96%; 57/59). Differential effect analyses, such as subgroup analysis, covariate adjustment, and baseline comparison, were rarely reported (10%; 6/59). The majority of models included age (92%; 54/59) and gender/sex (69%; 41/59) as final input variables. However, factors such as insurance status (7%; 4/59), marital status (7%; 4/59) and income (3%; 2/59) were seldom included.
The current level of reporting and consideration of SDOH during the development of prognostic ML models for orthopaedic outcomes is limited. Healthcare providers should be critical of the models they consider using and knowledgeable regarding the quality of model development, such as adherence to recognized methodological standards. Future efforts should aim to avoid bias and disparities when developing ML-driven applications for orthopaedics.
由于社会决定因素（SDOH）对治疗结果有影响，在提供矫形护理时，越来越多地考虑这些因素。同时，有助于临床决策的预测性机器学习（ML）模型已成为矫形外科领域的流行工具。在开发 ML 驱动工具时，重要的是尽量减少潜在差异的延续。一种方法是在模型开发过程中考虑 SDOH。迄今为止，尚不清楚现有的用于矫形手术结果的预测性 ML 模型是否以及如何考虑 SDOH 变量。
调查用于矫形手术结果的预测性 ML 模型是否考虑了 SDOH，以及 SDOH 变量在最终模型中包含的程度。
对 PubMed、Embase 和 Cochrane 进行了系统检索，检索截至 2020 年 11 月 17 日发表的研究。两名评审员独立使用 PROGRESS+框架（居住地、种族/民族、职业、性别/性别、宗教、教育、社会资本、社会经济地位、“Plus+”年龄、残疾和性取向）提取 SDOH 特征。
检索结果产生了 7138 项研究，其中 59 项符合纳入标准。在所有研究中，96%（57/59）在开发过程中考虑了至少一个 PROGRESS+因素。最常见的因素是年龄（95%；56/59）和性别/性别（96%；57/59）。很少有研究报告差异影响分析，如亚组分析、协变量调整和基线比较（10%；6/59）。大多数模型都包含年龄（92%；54/59）和性别/性别（69%；41/59）作为最终输入变量。然而，保险状况（7%；4/59）、婚姻状况（7%；4/59）和收入（3%；2/59）等因素很少被包含。
目前在开发用于矫形手术结果的预测性 ML 模型时，对 SDOH 的报告和考虑程度有限。医疗保健提供者应仔细评估他们考虑使用的模型，并了解模型开发的质量，例如是否符合公认的方法学标准。未来的努力应旨在避免在开发用于矫形的 ML 驱动应用程序时出现偏差和差异。
