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多维预后指数能否改善对可能从机械通气中获益的老年 COVID-19 住院患者的识别?一项观察性、前瞻性、多中心研究。

Can the Multidimensional Prognostic Index Improve the Identification of Older Hospitalized Patients with COVID-19 Likely to Benefit from Mechanical Ventilation? An Observational, Prospective, Multicenter Study.

作者信息

Pilotto Alberto, Topinkova Eva, Michalkova Helena, Polidori Maria Cristina, Cella Alberto, Cruz-Jentoft Alfonso, von Arnim Christine A F, Azzini Margherita, Gruner Heidi, Castagna Alberto, Cenderello Giovanni, Custureri Romina, Custodero Carlo, Zieschang Tania, Padovani Alessandro, Sanchez-Garcia Elisabet, Veronese Nicola

机构信息

Department of Geriatric Care, Orthogeriatrics and Rehabilitation, Galliera Hospital, Genoa, Italy; Department of Interdisciplinary Medicine, "Aldo Moro" University of Bari, Bari, Italy.

Department of Geriatrics, First Faculty of Medicine, Charles University, Prague, Czech Republic; Faculty of Health and Social Sciences, University of South Bohemia, Ceske Budejovice, Czech Republic.

出版信息

J Am Med Dir Assoc. 2022 Sep;23(9):1608.e1-1608.e8. doi: 10.1016/j.jamda.2022.06.023. Epub 2022 Jul 1.

DOI:10.1016/j.jamda.2022.06.023
PMID:
35934019
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9247233/
Abstract

OBJECTIVE

Data on prognostic tools for indicating mechanical ventilation in older people with COVID-19 are still limited. The aim of this research was to evaluate if the Multidimensional Prognostic Index (MPI), based on the Comprehensive Geriatric Assessment (CGA), may help physicians in identifying older hospitalized patients affected by COVID-19 who might benefit from mechanical ventilation.

DESIGN

Longitudinal, multicenter study.

SETTINGS AND PARTICIPANTS

502 older people hospitalized for COVID-19 in 10 European hospitals.

METHODS

MPI was calculated using 8 different domains typical of the CGA. A propensity score, Cox's regression analysis was used for assessing the impact of mechanical ventilation on rehospitalization/mortality for 90 days' follow-up, stratified by MPI = 0.50. The accuracy of MPI in predicting negative outcomes (ie, rehospitalization/mortality) was assessed using the area under the curve (AUC), and the discrimination with several indexes like the Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI).

RESULTS

Among 502 older people hospitalized for COVID-19 (mean age: 80 years), 152 were treated with mechanical ventilation. In the propensity score analysis, during the 90-day follow-up period, there were 44 rehospitalizations and 95 deaths. Mechanical ventilation in patients with MPI values ≥ 0.50, indicating frailer participants, was associated with a higher risk of rehospitalization/mortality (hazard ratio 1.56, 95% CI 1.09-2.23), whereas in participants with MPI values < 0.50 this association was not significant. The accuracy of the model including age, sex, respiratory parameters, and MPI was good (AUC = 0.783) as confirmed by an NRI of 0.2756 (P < .001) and an IDI of 0.1858 (P < .001), suggesting a good discrimination of the model in predicting negative outcomes.

CONCLUSIONS AND IMPLICATIONS

MPI could be useful for better individualizing older people hospitalized by COVID-19 who could benefit from mechanical ventilation.

摘要

目的

关于用于指示新型冠状病毒肺炎(COVID-19)老年患者机械通气的预后工具的数据仍然有限。本研究的目的是评估基于综合老年评估(CGA)的多维预后指数(MPI)是否有助于医生识别可能从机械通气中获益的COVID-19老年住院患者。

设计

纵向多中心研究。

地点和参与者

欧洲10家医院中502名因COVID-19住院的老年人。

方法

使用CGA典型的8个不同领域计算MPI。采用倾向评分、Cox回归分析评估机械通气对90天随访期内再次住院/死亡的影响,并按MPI = 0.50进行分层。使用曲线下面积(AUC)评估MPI预测不良结局(即再次住院/死亡)的准确性,并使用净重新分类改善(NRI)和综合辨别改善(IDI)等多个指标进行辨别。

结果

在502名因COVID-19住院的老年人(平均年龄:80岁)中,152人接受了机械通气治疗。在倾向评分分析中,在90天随访期内,有44人再次住院,95人死亡。MPI值≥0.50的患者(表明参与者更虚弱)接受机械通气与再次住院/死亡风险较高相关(风险比1.56,95%可信区间1.09 - 2.23),而MPI值<0.50的参与者中这种关联不显著。包括年龄、性别、呼吸参数和MPI的模型准确性良好(AUC = 0.783),NRI为0.2756(P <.001)和IDI为0.1858(P <.)证实了这一点。001),表明该模型在预测不良结局方面具有良好的辨别能力。

结论和意义

MPI可能有助于更好地个体化那些可能从机械通气中获益的COVID-19老年住院患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bbf/9247233/50df15062088/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bbf/9247233/50df15062088/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4bbf/9247233/50df15062088/gr1_lrg.jpg

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