Towards mitigating health inequity via machine learning: a nationwide cohort study to develop and validate ethnicity-specific models for prediction of cardiovascular disease risk in COVID-19 patients
Towards mitigating health inequity via machine learning: a nationwide cohort study to develop and validate ethnicity-specific models for prediction of cardiovascular disease risk in COVID-19 patients
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通过机器学习减轻健康不平等：一项全国性队列研究，旨在开发和验证用于预测 COVID-19 患者心血管疾病风险的特定种族模型
DOI：
10.1101/2023.09.13.23295489
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Allery F
中科院分区：
文献类型：
--
作者：
Allery F
医疗保健领域新兴的数据驱动技术，如风险预测模型，具有很大的前景，但也带来了潜在偏见和现有健康不平等加剧的挑战，这些不平等在心血管疾病（CVD）和COVID-19等疾病中已经观察到。这项研究解决了种族的影响，在风险预测模型的心血管事件后，SARS-CoV-2感染，并探讨了潜在的种族特定的模型，以减轻dispartials.MethodsThis回顾性队列研究利用六个链接的数据集访问通过国家卫生服务（NHS）英格兰的安全数据环境（SSL）服务英格兰，通过BHF数据科学中心的CVD-COVID-UK/COVID-IMPACT财团。确定了入选标准，并定义了人口统计学信息、风险因素和种族类别。采用了四种特征选择方法（LASSO，Random Forest，XGBoost，QRISK），并使用逻辑回归训练和测试了特定种族的预测模型。歧视（AUROC）和校准性能进行了评估，为不同的人群和ethnicitygroupsFindingsSeveral差异，观察到在整个研究队列与种族特定群体的模型训练。在特征选择阶段，针对族裔的模型产生了不同的选定特征。AUROC歧视措施显示，大多数种族群体的表现一致，基于QRisk的模型表现相对较差。校准性能在种族组和年龄组之间存在差异。种族特定的模型表现出的潜力，以提高校准性能为某些ethnic groups.InterpretationThis研究强调的重要性，考虑种族的风险预测建模，以确保公平的医疗结果。所选特征的差异和各种族之间的不对称校准强调了量身定制方法的必要性。针对族裔的模式为解决差异和提高模式绩效提供了途径。这项研究强调了数据驱动技术在缓解或加剧现有健康不平等方面的作用。研究之前的证据表明，SARS-CoV-2感染可能对预测以后的心血管疾病结局具有预后价值，这两种疾病都存在基于种族的健康不平等。现有的健康不平等有可能因风险预测模型等新兴数据驱动技术的偏见而加剧，目前没有建议的做法来缓解这一问题。模型性能通常不按种族分组，如果报告，本研究的附加价值本研究证明了在SARS患者心血管事件预测的风险预测模型中，包括对种族及其粒度的深入考虑的影响-CoV-2感染。这是专门为整个人口中的所有种族群体构建并代表所有种族群体的一组模型之一，评估不同的实践，以最好地减轻预测算法中基于种族的差异。此外，族裔数据历来没有得到很好的收集，多达三分之一的人在其健康记录中缺少族裔数据。通过数据链接，这项工作是第一次分析世界上最大的种族多样性常规收集之一的96%完整的种族记录。
BackgroundEmerging data-driven technologies in healthcare, such as risk prediction models, hold great promise but also pose challenges regarding potential bias and exacerbation of existing health inequalities, which have been observed across diseases such as cardiovascular disease (CVD) and COVID-19. This study addresses the impact of ethnicity in risk prediction modelling for cardiovascular events following SARS-CoV-2 infection and explores the potential of ethnicity-specific models to mitigate disparities.MethodsThis retrospective cohort study utilises six linked datasets accessed through National Health Service (NHS) England’s Secure Data Environment (SDE) service for England, via the BHF Data Science Centre’s CVD-COVID-UK/COVID-IMPACT Consortium. Inclusion criteria were established, and demographic information, risk factors, and ethnicity categories were defined. Four feature selection methods (LASSO, Random Forest, XGBoost, QRISK) were employed and ethnicity-specific prediction models were trained and tested using logistic regression. Discrimination (AUROC) and calibration performance were assessed for different populations and ethnicity groups.FindingsSeveral differences were observed in the models trained on the whole study cohort vs ethnicity-specific groups. At the feature selection stage, ethnicity-specific models yielded different selected features. AUROC discrimination measures showed consistent performance across most ethnicity groups, with QRISK-based models performing relatively poorly. Calibration performance exhibited variation across ethnicity groups and age categories. Ethnicity-specific models demonstrated the potential to enhance calibration performance for certain ethnic groups.InterpretationThis research highlights the importance of considering ethnicity in risk prediction modelling to ensure equitable healthcare outcomes. Differences in selected features and asymmetric calibration across ethnicities underscore the necessity of tailored approaches. Ethnicity-specific models offer a pathway to addressing disparities and improving model performance. The study emphasises the role of data-driven technologies in either alleviating or exacerbating existing health inequalities.Evidence before this studyResearch has suggested that SARS-CoV-2 infections may have prognostic value in predicting later cardiovascular disease outcomes, two diseases where ethnicity-based health inequalities have been observed. Existing health inequalities are at risk of being exacerbated by bias in emerging data-driven technologies such as risk prediction models, and there currently exists no recommended practice to mitigate this issue. Model performances are not typically stratified by ethnic groups and, if reported, ethnic groups are often only included in higher-level categories that have been criticised for simplicity of definition and for missing key ethnic heterogeneity.Added value of this studyThis study demonstrates the impact of including an in-depth consideration of ethnicity and its granularity in risk prediction modelling for cardiovascular event prediction in patients following a SARS-CoV-2 infection. This is one of, if not the first, set of models specifically built for and representative of all ethnic groups across an entire population, evaluating different practices to best mitigate ethnicity-based disparities in prediction algorithms. Moreover, ethnicity data has historically not been well captured, with as many as 1 in 3 individuals missing ethnicity data in their health records. With data linkage, this work is the first to analyse 96% complete ethnicity records in one of the world’s largest ethnically diverse routinely collected …
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发表时间：
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