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对六批连续入住重症监护病房的新冠肺炎患者的分析:来自葡萄牙人群的主要发现与见解

Analysis of six consecutive waves of ICU-admitted COVID-19 patients: key findings and insights from a Portuguese population.

作者信息

Von Rekowski Cristiana P, Pinto Iola, Fonseca Tiago A H, Araújo Rúben, Calado Cecília R C, Bento Luís

机构信息

NMS - NOVA Medical School, FCM - Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Campo Mártires da Pátria 130, 1169-056, Lisbon, Portugal.

ISEL - Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, Rua Conselheiro Emídio Navarro 1, 1959-007, Lisbon, Portugal.

出版信息

Geroscience. 2025 Apr;47(2):2399-2422. doi: 10.1007/s11357-024-01410-x. Epub 2024 Nov 14.

Abstract

Identifying high-risk patients, particularly in intensive care units (ICUs), enhances treatment and reduces severe outcomes. Since the pandemic, numerous studies have examined COVID-19 patient profiles and factors linked to increased mortality. Despite six pandemic waves, to the best of our knowledge, there is no extensive comparative analysis of patients' characteristics across these waves in Portugal. Thus, we aimed to analyze the demographic and clinical features of 1041 COVID-19 patients admitted to an ICU and their relationship with the different SARS-Cov-2 variants in Portugal. Additionally, we conducted an in-depth examination of factors contributing to early and late mortality by analyzing clinical data and laboratory results from the first 72 h of ICU admission. Our findings revealed a notable decline in ICU admissions due to COVID-19, with the highest mortality rates observed during the second and third waves. Furthermore, immunization could have significantly contributed to the reduction in the median age of ICU-admitted patients and the severity of their conditions. The factors contributing to early and late mortality differed. Age, wave number, D-dimers, and procalcitonin were independently associated with the risk of early death. As a measure of discriminative power for the derived multivariable model, an AUC of 0.825 (p < 0.001; 95% CI, 0.719-0.931) was obtained. For late mortality, a model incorporating age, wave number, hematologic cancer, C-reactive protein, lactate dehydrogenase, and platelet counts resulted in an AUC of 0.795 (p < 0.001; 95% CI, 0.759-0.831). These findings underscore the importance of conducting comprehensive analyses across pandemic waves to better understand the dynamics of COVID-19.

摘要

识别高危患者,尤其是在重症监护病房(ICU)中的患者,可改善治疗效果并降低严重后果。自疫情大流行以来,众多研究调查了新冠病毒病(COVID-19)患者的特征以及与死亡率增加相关的因素。尽管经历了六次疫情浪潮,但据我们所知,葡萄牙尚未对这些浪潮中患者的特征进行广泛的比较分析。因此,我们旨在分析1041名入住ICU的COVID-19患者的人口统计学和临床特征,以及他们与葡萄牙不同严重急性呼吸综合征冠状病毒2(SARS-CoV-2)变体的关系。此外,我们通过分析ICU入院后头72小时的临床数据和实验室结果,对导致早期和晚期死亡的因素进行了深入研究。我们的研究结果显示,因COVID-19入住ICU的人数显著下降,第二波和第三波期间的死亡率最高。此外,免疫接种可能对降低入住ICU患者的中位年龄及其病情严重程度有显著贡献。导致早期和晚期死亡的因素有所不同。年龄、疫情波次、D-二聚体和降钙素原与早期死亡风险独立相关。作为对推导的多变量模型判别力的一种衡量,曲线下面积(AUC)为0.825(p < 0.001;95%置信区间,0.719 - 0.931)。对于晚期死亡,一个纳入年龄、疫情波次、血液系统癌症、C反应蛋白、乳酸脱氢酶和血小板计数的模型,其AUC为0.795(p < 0.001;95%置信区间,0.759 - 0.831)。这些发现强调了对各疫情浪潮进行全面分析以更好地了解COVID-19动态的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a773/11979077/2f2602a28a24/11357_2024_1410_Fig1_HTML.jpg

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