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描述 COVID-19 的临床表型以及相关合并症和并发症特征。

Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles.

机构信息

Department of Surgery, University of Minnesota, Minneapolis, MN, United States of America.

Department of Medicine, Division of Pulmonary and Critical Care, University of Minnesota, Minneapolis, MN, United States of America.

出版信息

PLoS One. 2021 Mar 31;16(3):e0248956. doi: 10.1371/journal.pone.0248956. eCollection 2021.

Abstract

PURPOSE

Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.

METHODS

This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes.

RESULTS

The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III.

CONCLUSION

We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.

摘要

目的

新冠肺炎(COVID-19)住院患者的结局存在异质性。识别临床表型可能有助于制定个体化治疗方案并改善结局。本研究旨在确定 COVID-19 患者的特定临床表型,并比较入院特征和结局。

方法

这是对 2020 年 3 月 7 日至 2020 年 8 月 25 日期间美国 14 家医院的 COVID-19 患者进行的回顾性分析。对入院后 72 小时内采集的 33 个变量进行了集合聚类。采用主成分分析可视化变量对聚类的贡献。拟合多项回归模型比较表型间患者合并症。拟合多变量模型估计表型与院内并发症和临床结局之间的关联。

结果

数据库包括 1022 例 COVID-19 住院患者。确定了三种临床表型(I、II、III),I 表型患者 236 例(23.1%),II 表型患者 613 例(60.0%),III 表型患者 173 例(16.9%)。有呼吸系统合并症的患者最常见的是表型 III(p=0.002),而有血液系统、肾脏和心脏合并症的患者最常见的是表型 I(均 p<0.001)。表型 I 患者发生呼吸系统、肾脏、肝脏、代谢(均 p<0.001)和血液学(p=0.02)并发症的调整优势比最高。表型 I 和 II 与表型 III 相比,死亡风险分别增加了 7.30 倍(HR:7.30,95%CI:(3.11-17.17),p<0.001)和 2.57 倍(HR:2.57,95%CI:(1.10-6.00),p=0.03)。

结论

我们确定了三种 COVID-19 临床表型,反映了具有不同合并症、并发症和临床结局的患者人群。需要进一步研究以确定这些表型在临床实践和试验设计中的效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f28/8011766/125ebdadb9fe/pone.0248956.g001.jpg

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