医院获得性 SARS-CoV-2 感染的拟议新定义:确证因子分析结果。

Nicolás Reinoso Schiller, Claas Baier, Isabella Dresselhaus, Ulrike Loderstädt, Dirk Schlüter, Tim Eckmanns, Simone Scheithauer
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引用次数: 0

摘要

目的:本研究旨在制定和讨论医院获得性严重急性呼吸系统综合征冠状病毒 2(SARS-CoV-2)感染(HA-SIs)的扩展定义,该定义超出了仅使用时间参数的范围:设计:采用确证因素分析法检验 HA-SI 的合适定义:在德国下萨克森州的两家三级公立医院开展了一项双中心队列研究。研究对象为 2020 年 3 月至 2023 年 8 月期间入院的 366 名经实验室确诊感染 SARS-CoV-2 的住院患者:结果:所提出的模型显示出足够的拟合指数(CFI.scaled = 0.959,RMSEA = 0.049)。与现有分类的描述性比较显示,我们的模型具有很强的特点,特别是其对特定地区疫情的适应性:结论:使用地区发病率作为替代变量来更好地界定 HA-SI 病例是一种实用而新颖的方法。该模型与文献中的最新科学成果非常吻合。这项工作成功地将近期文献中描述的对 HA-SI 发病具有重要意义的变量统一到一个模型中。对模型及其应用的进一步改进和调整,如分类过程自动化(医院感染)或采用可比模型进行医院感染流感分类,都是有待讨论的主题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proposed new definition for hospital-acquired SARS-CoV-2 infections: results of a confirmatory factor analysis.

Objective: The present study aims to develop and discuss an extension of hospital-acquired severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections (HA-SIs) definition which goes beyond the use of time parameters alone.

Design: A confirmatory factor analysis was carried out to test a suitable definition for HA-SI.

Setting and patients: A two-center cohort study was carried out at two tertiary public hospitals in the German state of lower Saxony. The study involved a population of 366 laboratory-confirmed SARS-CoV-2-infected inpatients enrolled between March 2020 and August 2023.

Results: The proposed model shows adequate fit indices (CFI.scaled = 0.959, RMSEA = 0.049). A descriptive comparison with existing classifications revealed strong features of our model, particularly its adaptability to specific regional outbreaks.

Conclusion: The use of the regional incidence as a proxy variable to better define HA-SI cases represents a pragmatic and novel approach. The model aligns well with the latest scientific results in the literature. This work successfully unifies, within a single model, variables which the recent literature described as significant for the onset of HA-SI. Further potential improvements and adaptations of the model and its applications, such as automating the categorization process (in terms of hospital acquisition) or employing a comparable model for hospital-acquired influenza classification, are subjects open for discussion.

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