Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models Extension: Development of a Critical Appraisal Tool Extension to Assess Racial and Ethnic Equity-Related Risk of Bias for Clinical Prediction Models.

IF 2.6 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Health Equity Pub Date : 2023-11-30 eCollection Date: 2023-01-01 DOI:10.1089/heq.2023.0035
Shazia M Siddique, Corinne V Evans, Michael Harhay, Eric S Johnson, Jaya Aysola, Gary E Weissman, Nikhil K Mull, Emilia Flores, Harald Schmidt, Kelley Tipton, Brian Leas, Jennifer S Lin
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引用次数: 0

Abstract

Introduction: Despite mounting evidence that the inclusion of race and ethnicity in clinical prediction models may contribute to health disparities, existing critical appraisal tools do not directly address such equity considerations.

Objective: This study developed a critical appraisal tool extension to assess algorithmic bias in clinical prediction models.

Methods: A modified e-Delphi approach was utilized to develop and obtain expert consensus on a set of racial and ethnic equity-based signaling questions for appraisal of risk of bias in clinical prediction models. Through a series of virtual meetings, initial pilot application, and an online survey, individuals with expertise in clinical prediction model development, systematic review methodology, and health equity developed and refined this tool.

Results: Consensus was reached for ten equity-based signaling questions, which led to the development of the Critical Appraisal for Racial and Ethnic Equity in Clinical Prediction Models (CARE-CPM) extension. This extension is intended for use along with existing critical appraisal tools for clinical prediction models.

Conclusion: CARE-CPM provides a valuable risk-of-bias assessment tool extension for clinical prediction models to identify potential algorithmic bias and health equity concerns. Further research is needed to test usability, interrater reliability, and application to decision-makers.

临床预测模型中种族和民族平等关键评估扩展:开发关键评估工具扩展版,以评估临床预测模型中与种族和民族平等相关的偏差风险。
导言:尽管越来越多的证据表明,将种族和民族纳入临床预测模型可能会导致健康差异,但现有的关键评估工具并没有直接解决此类公平问题:本研究开发了一种关键评估工具扩展版,用于评估临床预测模型中的算法偏差:方法:采用修改后的 e-Delphi 方法来开发一套基于种族和民族平等的信号问题,用于评估临床预测模型中的偏差风险,并获得专家共识。通过一系列虚拟会议、初步试点应用和在线调查,在临床预测模型开发、系统综述方法学和健康公平方面具有专长的人士开发并完善了这一工具:结果:就十个基于公平的信号问题达成了共识,并由此开发出了临床预测模型中种族和民族平等批判性评估(CARE-CPM)扩展工具。该扩展工具旨在与现有的临床预测模型关键评估工具一起使用:结论:CARE-CPM 为临床预测模型提供了一个有价值的偏倚风险评估工具扩展,可用于识别潜在的算法偏倚和健康公平问题。需要进一步开展研究,以测试可用性、研究者之间的可靠性以及对决策者的应用。
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
Health Equity
Health Equity Social Sciences-Health (social science)
CiteScore
3.80
自引率
3.70%
发文量
97
审稿时长
24 weeks
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