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西班牙 PRECOVID 研究:实验室确诊 COVID-19 病例的基线慢性合并症与死亡率。

Baseline Chronic Comorbidity and Mortality in Laboratory-Confirmed COVID-19 Cases: Results from the PRECOVID Study in Spain.

机构信息

EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain.

Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, 28222 Madrid, Spain.

出版信息

Int J Environ Res Public Health. 2020 Jul 17;17(14):5171. doi: 10.3390/ijerph17145171.

Abstract

We aimed to analyze baseline socio-demographic and clinical factors associated with an increased likelihood of mortality in men and women with coronavirus disease (COVID-19). We conducted a retrospective cohort study (PRECOVID Study) on all 4412 individuals with laboratory-confirmed COVID-19 in Aragon, Spain, and followed them for at least 30 days from cohort entry. We described the socio-demographic and clinical characteristics of all patients of the cohort. Age-adjusted logistic regressions models were performed to analyze the likelihood of mortality based on demographic and clinical variables. All analyses were stratified by sex. Old age, specific diseases such as diabetes, acute myocardial infarction, or congestive heart failure, and dispensation of drugs like vasodilators, antipsychotics, and potassium-sparing agents were associated with an increased likelihood of mortality. Our findings suggest that specific comorbidities, mainly of cardiovascular nature, and medications at the time of infection could explain around one quarter of the mortality in COVID-19 disease, and that women and men probably share similar but not identical risk factors. Nonetheless, the great part of mortality seems to be explained by other patient- and/or health-system-related factors. More research is needed in this field to provide the necessary evidence for the development of early identification strategies for patients at higher risk of adverse outcomes.

摘要

我们旨在分析与男性和女性冠状病毒病(COVID-19)患者死亡率增加相关的基线社会人口学和临床因素。我们对西班牙阿拉贡的所有 4412 名经实验室确诊的 COVID-19 患者进行了回顾性队列研究(PRECOVID 研究),并从队列进入之日起至少随访 30 天。我们描述了队列中所有患者的社会人口学和临床特征。基于人口统计学和临床变量,进行了年龄调整的逻辑回归模型分析死亡率的可能性。所有分析均按性别分层。高龄、特定疾病(如糖尿病、急性心肌梗死或充血性心力衰竭)以及血管扩张剂、抗精神病药和保钾利尿剂等药物的使用与死亡率增加相关。我们的研究结果表明,特定的合并症(主要是心血管性质)和感染时的药物可能解释了 COVID-19 疾病约四分之一的死亡率,并且女性和男性可能具有相似但不完全相同的危险因素。尽管如此,大部分死亡率似乎可以用其他与患者和/或医疗系统相关的因素来解释。需要在这一领域开展更多研究,为开发针对高风险不良结局患者的早期识别策略提供必要的证据。

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