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一种用于利用电子健康记录数据进行COVID-19回顾性研究的序贯严重程度量表。

An ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data.

作者信息

Khodaverdi Maryam, Price Bradley S, Porterfield J Zachary, Bunnell H Timothy, Vest Michael T, Anzalone Alfred Jerrod, Harper Jeremy, Kimble Wes D, Moradi Hamidreza, Hendricks Brian, Santangelo Susan L, Hodder Sally L

机构信息

West Virginia Clinical and Translational Sciences Institute, Morgantown, West Virginia, USA.

Department of Medicine, University of Kentucky, Lexington, Kentucky, USA.

出版信息

JAMIA Open. 2022 Jul 9;5(3):ooac066. doi: 10.1093/jamiaopen/ooac066. eCollection 2022 Oct.

Abstract

OBJECTIVES

Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data.

MATERIALS AND METHODS

An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis.

RESULTS

The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period.

DISCUSSION

An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time.

CONCLUSIONS

The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.

摘要

目的

虽然世界卫生组织(WHO)的COVID-19临床进展量表在前瞻性临床试验中很有用,但它不能有效地用于回顾性电子健康记录(EHR)数据集。通过修改现有的WHO临床进展量表,我们开发了一种序数严重程度量表(OS),并评估了其在使用回顾性EHR数据分析COVID-19患者结局方面的有用性。

材料和方法

在国家COVID队列协作组(N3C)数据专区内,利用观察性医疗结局合作组织的通用数据模型开发了一种OS,用于确定COVID-19疾病的严重程度。然后,我们使用2020年1月至2021年10月期间美国63个医疗机构提交给N3C的异质性EHR数据,评估所开发的OS的有用性。采用主成分分析(PCA)来描述COVID-19诊断后28天内患者疾病严重程度的变化。

结果

本分析中使用的数据集包括2880456名患者。对28天期间OS水平的每日变化进行PCA分析,揭示了前14天和后14天疾病严重程度变化的对比模式,并说明了在整个28天期间进行评估的重要性。

讨论

基于离散的EHR数据元素、具有明确且稳健特征的OS,对于评估COVID-19患者结局很有用,可提供关于COVID-19疾病严重程度随时间进展的见解。

结论

OS提供了一个框架,有助于更好地理解急性COVID-19的病程,为临床决策和资源分配提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/25cc/9334687/00c02e652981/ooac066f1.jpg

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