心血管代谢疾病严重程度、健康的社会决定因素（SDoH）与 COVID-19 不良结局之间的关联。
Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes.
机构信息
Division of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA.
出版信息
Obesity (Silver Spring). 2022 Jul;30(7):1483-1494. doi: 10.1002/oby.23440. Epub 2022 May 25.
OBJECTIVE
This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes.
METHODS
Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared.
RESULTS
A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83).
CONCLUSIONS
Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.
目的
本研究旨在确定回顾性心脏代谢疾病分期（CMDS）和健康社会决定因素（SDoH）预测 COVID-19 结局的能力。
方法
从电子病历中提取了个体和社区 SDoH 和 CMDS 临床参数（BMI、血糖、血压、高密度脂蛋白、甘油三酯），这些参数是在 COVID-19 检测呈阳性前 3 年内收集的。使用贝叶斯逻辑回归对 CMDS 和 SDoH 进行建模，以预测随后的住院、重症监护病房（ICU）入院和死亡率，并研究是否将 SDoH 添加到 CMDS 模型中可以改善预测。对模型进行交叉验证，并比较曲线下面积（AUC）。
结果
共确定了 2873 名患者（平均年龄：58 岁[SD 13.2]，59%为女性，45%为黑人）。CMDS、保险状况、男性和更高的血糖值与所有结局的发生几率增加有关；地区社会脆弱性与住院（比值比：1.84，95%置信区间：1.38-2.45）和 ICU 入院（比值比 1.98，95%置信区间：1.45-2.85）的几率增加有关。当将 SDoH 添加到 CMDS 时，AUC 有所提高（p<0.001）：住院（AUC 0.78 与 0.82）、ICU 入院（AUC 0.77 与 0.81）和死亡率（AUC 0.77 与 0.83）。
结论
心脏代谢疾病的回顾性临床标志物和 SDoH 独立预测了该人群 COVID-19 的结局。
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