Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study.
Geospatial Distribution and Predictors of Mortality in Hospitalized Patients With COVID-19: A Cohort Study.
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DOI：
10.1093/ofid/ofaa436
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发表时间：
2020-10
影响因子：
4.2
通讯作者：
Stony Brook COVID-19 Research Consortium
中科院分区：
文献类型：
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作者：
Stony Brook COVID-19 Research Consortium
全球冠状病毒病2019(新冠肺炎)大流行为评估医院如何管理对不同人口统计和临床表现的住院患者的护理提供了机会。这项研究的目的是证明人口稠密的居民区对住院的影响，并确定在国际上受灾最严重的县之一住院的新冠肺炎患者的住院时间和死亡率的预测因素。这是一项单中心队列研究，研究对象是2020年3月2日至2020年5月11日期间在纽约连续住院的第1325名新冠肺炎患者。绘制了研究患者住所相对于该地区人口密度的地理空间分布，数据分析包括住院时间、有创机械通气(IMV)的需要和持续时间以及死亡率。建立Logistic回归模型来预测其余积极研究患者的出院倾向。研究队列的中位年龄(四分位数范围[IQR])为62岁(49-75岁)，超过一半的人是男性(57%)，有高血压(60%)、肥胖(41%)和糖尿病(42%)的病史。研究患者的地理居住地与人口密度较高的地区不成比例地相关(rS=0.235；P=0.004)，该地区有明显的“热点”。研究患者主要表现为高血压(MAP>；90 mmHg；670，51%)，表现为淋巴细胞减少(590，55%)、低钠血症(411，31%)和肾功能障碍(估计肾小球滤过率<；60mL/min/1.73m2；381，29%)。在有意向的患者(1188/第1325)中，15%(182/1188)的患者需要IMV，21%(250/1188)的患者发生急性肾损伤。在接受IMV治疗的患者中，存活患者的中位住院时间(22[16.5-29.5]天)显著长于死亡患者(15[10-23.75]天)，但这并不是因为使用呼吸机的时间延长。所有住院患者的总死亡率为15%，接受IMV的患者为48%，根据为预测其余使用呼吸机的患者的处置而建立的Logistic回归模型，预计这一数字将从48%微升至49%。住院期间急性肾损伤(优势比E，3.23)是需要IMV的患者死亡率的最强预测因子。这是第一项集体利用新冠肺炎患者的人口统计学、临床特征和医院病程来确定不良结果的预测因素的研究，这些因素可用于在未来的大流行浪潮中进行资源分配。
The global coronavirus disease 2019 (COVID-19) pandemic offers the opportunity to assess how hospitals manage the care of hospitalized patients with varying demographics and clinical presentations. The goal of this study was to demonstrate the impact of densely populated residential areas on hospitalization and to identify predictors of length of stay and mortality in hospitalized patients with COVID-19 in one of the hardest hit counties internationally. This was a single-center cohort study of 1325 sequentially hospitalized patients with COVID-19 in New York between March 2, 2020, to May 11, 2020. Geospatial distribution of study patients’ residences relative to population density in the region were mapped, and data analysis included hospital length of stay, need and duration of invasive mechanical ventilation (IMV), and mortality. Logistic regression models were constructed to predict discharge dispositions in the remaining active study patients. The median age of the study cohort (interquartile range [IQR]) was 62 (49–75) years, and more than half were male (57%) with history of hypertension (60%), obesity (41%), and diabetes (42%). Geographic residence of the study patients was disproportionately associated with areas of higher population density (rs = 0.235; P = .004), with noted “hot spots” in the region. Study patients were predominantly hypertensive (MAP > 90 mmHg; 670, 51%) on presentation with lymphopenia (590, 55%), hyponatremia (411, 31%), and kidney dysfunction (estimated glomerular filtration rate < 60 mL/min/1.73 m2; 381, 29%). Of the patients with a disposition (1188/1325), 15% (182/1188) required IMV and 21% (250/1188) developed acute kidney injury. In patients on IMV, the median (IQR) hospital length of stay in survivors (22 [16.5–29.5] days) was significantly longer than that of nonsurvivors (15 [10–23.75] days), but this was not due to prolonged time on the ventilator. The overall mortality in all hospitalized patients was 15%, and in patients receiving IMV it was 48%, which is predicted to minimally rise from 48% to 49% based on logistic regression models constructed to project disposition in the remaining patients on ventilators. Acute kidney injury during hospitalization (odds ratioE, 3.23) was the strongest predictor of mortality in patients requiring IMV. This is the first study to collectively utilize the demographics, clinical characteristics, and hospital course of COVID-19 patients to identify predictors of poor outcomes that can be used for resource allocation in future waves of the pandemic.
DOI：
10.1093/jamia/ocu023
发表时间：
2015-05-01
影响因子：
6.4
作者：
Voss, Erica A.;Makadia, Rupa;Ryan, Patrick B.
通讯作者：
Ryan, Patrick B.
影响因子：
39.2
作者：
Levey AS;Stevens LA;Schmid CH;Zhang YL;Castro AF 3rd;Feldman HI;Kusek JW;Eggers P;Van Lente F;Greene T;Coresh J;CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
通讯作者：
CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
影响因子：
120.7
作者：
Rosenberg, Eli S.;Dufort, Elizabeth M.;Zucker, Howard A.
通讯作者：
Zucker, Howard A.