Mortality incidence, sociodemographic and clinical data in COVID-19 patients.

This data record contains a single file, <b>An. Dataset.xlsx</b>, in .<b>xlsx</b> file format.<br>The data file contains information on demographics, comorbidities, admission laboratory values, admission medications, admission supplemental oxygen orders, discharge and mortality. The data were derived from a healthcare surveillance software package (Clinical Looking Glass [CLG]; Streamline Health, Atlanta, Georgia) and review of the primary medical records.The data relate to COVID-19 patients admitted to a single healthcare system, over a specific period of time, and separated into the 1st 3 weeks of the pandemic and the 2nd 3 weeks of the pandemic.Some of the variables included in the dataset are: length of hospital stay (LOS), myocardial infraction (MI), peripheral vascular disease (PVD), congestive heart failure (CHF), cardiovascular disease (CVD), dementia (Dement), Chronic obstructive pulmonary disease (COPD), diabetes mellitus simple (DM simple), diabetes mellitus complicated (DM complicated), oxygen saturation (OsSats), mean arterial pressure, in mmHg (MAP), D-dimer, in mg/ml (Ddimer), platelets, in k per mm3 (Plts), international normalized ratio (INR), blood urea nitrogen, in mg/dL (BUN), alanine aminotransferase, in U/liter (AST), while blood cells, in per mm3 (WBC) and interleukin-6, in pg/ml (IL-6).<br><b>Study aims and methodology:</b>COVID-19 is commonly mild and self-limiting, but in a considerable portion of patients the disease is severe and fatal. Determining which patients are at high risk of severe illness or mortality is essential for appropriate clinical decision making. In this study, the authors propose a novel severity score specifically for COVID-19 to help predict disease severity and mortality.4,711 patients with confirmed SARS-CoV-2 infection were included in the study. The authors derived a risk model using the first half of the cohort (n=2,355 patients) by logistic regression and bootstrapping methods. The discriminative power of the risk model was assessed by calculating the area under the receiver operating characteristic curves (AUC). The severity score was validated in a second half of 2,356 patients. This study was approved by the Montefiore Medical Center/Albert Einstein College of Medicine Institutional Review Board. The Institutional Review Board approved waiver of patient informed consent due to the retrospective design of the studyFor more details on the methodology, please read the related article.<br>

本数据集仅包含一个文件，即<b>An. Dataset.xlsx</b>，文件格式为.xlsx。
该数据文件涵盖人口统计学特征、合并症、入院实验室检测指标、入院用药方案、入院吸氧医嘱、出院结局及死亡率相关信息。数据来源于医疗监测软件包Clinical Looking Glass（CLG；Streamline Health，佐治亚州亚特兰大市）以及原始病历的回顾性审查。
本数据集关联某特定时间段内收治于单一医疗系统的新冠（COVID-19）患者，按疫情进程分为疫情前3周与后3周两个亚组。
数据集包含的部分变量如下：住院时长（length of stay, LOS）、心肌梗死（myocardial infarction, MI）、外周血管疾病（peripheral vascular disease, PVD）、充血性心力衰竭（congestive heart failure, CHF）、心血管疾病（cardiovascular disease, CVD）、痴呆（dementia, Dement）、慢性阻塞性肺疾病（chronic obstructive pulmonary disease, COPD）、单纯型糖尿病（diabetes mellitus simple, DM simple）、复杂型糖尿病（diabetes mellitus complicated, DM complicated）、血氧饱和度（oxygen saturation, OsSats）、平均动脉压（单位：mmHg，mean arterial pressure, MAP）、D-二聚体（单位：mg/ml，D-dimer）、血小板计数（单位：每立方毫米千个，k per mm3, Plts）、国际标准化比值（international normalized ratio, INR）、血尿素氮（单位：mg/dL，blood urea nitrogen, BUN）、丙氨酸转氨酶（alanine aminotransferase, AST）、白细胞计数（单位：每立方毫米，white blood cells, WBC）以及白细胞介素-6（单位：pg/ml，interleukin-6, IL-6）。
<b>研究目的与方法：</b>
新型冠状病毒肺炎（COVID-19）多呈轻症自限性，但在相当比例的患者中可进展为重症甚至致死。明确哪些患者存在重症或死亡高风险，对于制定合理的临床决策至关重要。本研究中，作者提出一种专为新冠疫情设计的新型重症评分工具，以辅助预测疾病严重程度与死亡风险。
本研究共纳入4711例经实验室确认的SARS-CoV-2感染患者。研究者利用队列的前半部分（n=2355例患者），通过logistic回归与Bootstrap法构建风险预测模型。采用受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUC）评估该模型的判别效能，并在队列后半部分的2356例患者中对该重症评分进行验证。
本研究经蒙特菲奥雷医学中心/阿尔伯特·爱因斯坦医学院伦理审查委员会批准。鉴于本研究为回顾性研究设计，伦理审查委员会豁免了患者知情同意要求。
如需了解方法学细节，请参阅相关研究论文。