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卡纳塔克邦的 COVID-19 住院患者:生存和住院特征。

COVID-19-Hospitalized Patients in Karnataka: Survival and Stay Characteristics.

机构信息

MPH Scholar, Department of Epidemiology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

PhD Scholar, Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.

出版信息

Indian J Public Health. 2020 Jun;64(Supplement):S221-S224. doi: 10.4103/ijph.IJPH_486_20.

Abstract

The information on the clinical course of coronavirus disease 2019 (COVID-19) and its correlates which are essential to assess the hospital care needs of the population are currently limited. We investigated the factors associated with hospital stay and death for COVID-19 patients for the entire state of Karnataka, India. A retrospective-cohort analysis was conducted on 445 COVID-19 patients that were reported in the publicly available media-bulletin from March 9, 2020, to April 23, 2020, for the Karnataka state. This fixed cohort was followed till 14 days (May 8, 2020) for definitive outcomes (death/discharge). The median length of hospital stay was 17 days (interquartile range: 15-20) for COVID-19 patients. Having severe disease at the time of admission (adjusted-hazard-ratio: 9.3 (3.2-27.3);P < 0.001) and being aged ≥ 60 years (adjusted-hazard-ratio: 11.9 (3.5-40.6);P < 0.001) were the significant predictors of COVID-19 mortality. By moving beyond descriptive (which provide only crude information) to survival analyses, information on the local hospital-related characteristics will be crucial to model bed-occupancy demands for contingency planning during COVID-19 pandemic.

摘要

关于 2019 年冠状病毒病(COVID-19)及其相关信息的临床过程,对于评估人群的医院护理需求至关重要,但目前这些信息有限。我们调查了印度卡纳塔克邦 COVID-19 患者住院时间和死亡的相关因素。对 2020 年 3 月 9 日至 2020 年 4 月 23 日在公开媒体公告中报告的 445 例 COVID-19 患者进行了回顾性队列分析。该固定队列一直随访至 14 天(2020 年 5 月 8 日),以确定结局(死亡/出院)。COVID-19 患者的住院中位时间为 17 天(四分位间距:15-20)。入院时患有严重疾病(校正风险比:9.3(3.2-27.3);P<0.001)和年龄≥60 岁(校正风险比:11.9(3.5-40.6);P<0.001)是 COVID-19 死亡的显著预测因素。通过从描述性分析(仅提供粗略信息)转变为生存分析,可以获得有关当地医院相关特征的信息,这对于在 COVID-19 大流行期间进行应急规划模型床位占用需求至关重要。

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