一项观察性研究,开发一种细菌性肺炎诊断的预测模型,用于重症COVID-19患者-C19-PNEUMOSCORE
Eloisa Sofia Tanzarella1, Joel Vargas2, Marco Menghini3
1Dipartimento di Scienze Dell'emergenza, Anestesiologiche e della Rianimazione, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy.
Journal of clinical medicine
|July 29, 2023
概括
一种新的评分模型,C19-PNEUMOSCORE,有助于识别入住ICU的COVID-19患者的细菌性肺炎. 这种工具有助于优化抗生素的使用,减少过度使用在重症监护机构.
科学领域:
- 临界护理医学 临界护理医学
- 传染性疾病 传染性疾病
- 肺部病理学 肺部病理学
背景情况:
- 过度使用抗生素是管理COVID-19患者的一个重要问题.
- 需要一个可靠的工具来准确地诊断细菌性肺炎在重症监护室 (ICU) 入院时.
- 细菌共同感染使COVID-19复杂化,需要明智的抗生素管理.
研究的目的:
- 在ICU入院时开发和验证COVID-19患者细菌性肺炎的预测评分模型.
- 在这个患者队列中确定肺部共感染的关键临床和实验室预测因素.
- 创建一个易于实施的床头工具,用于指导抗生素治疗.
主要方法:
- 一项涉及331名需要侵入性机械通风的COVID-19患者的多中心观察研究.
- 开发一个多变量后勤回归模型,以确定细菌性肺炎的预测因素.
- 创建基于已识别的预测因素及其系数的C19-PNEUMOSCORE.
主要成果:
- 确定了七个预测因素:疫苗接种状况,慢性病,ICU前住院时间≥5天,中性粒细胞≥9.41 × 10^9/L,前素≥0.2 ng/mL,C-反应蛋白≥107.6 mg/L和Brixia胸部X射线得分≥9.
- 从0到9的C19-PNEUMOSCORE显示了84.9%的灵敏度和55.9%的特异性,截止值≥3.3.
- 该模型在识别细菌性肺炎方面实现了71.6%的整体准确性.
结论:
- C19-PNEUMOSCORE是一种实用的床边工具,用于在严重的COVID-19患者中早期识别细菌性肺炎.
- 这一分数的实施可以支持抗生素管理工作,并优化治疗决策.
- 这种评分系统有助于在ICU环境中区分细菌共感染和病毒性肺炎.
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