Hong Yucai, Wu Xinhu, Qu Jijing, Gao Yuandi, Chen Hao, Zhang Zhongheng
Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.
Department of Emergency Medicine, Yueqing People's Hospital, Yueqing 325600, China.
Ann Transl Med. 2020 Apr;8(7):443. doi: 10.21037/atm.2020.03.147.
The epidemic of Coronavirus Disease 2019 (COVID-19) has become a global health emergency, but the clinical characteristics of COVID-19 are not fully described. We aimed to describe the clinical characteristics of COVID-19 outside of Wuhan city; and to develop a multivariate model to predict the risk of prolonged length of stay in hospital (ProLOS).
The study was conducted in a tertiary care hospital in Zhejiang province from January to February 20, 2020. Medical records of all confirmed cases of COVID-19 were retrospectively reviewed. Patients were categorized into the ProLOS and non-ProLOS groups by hospital length of stay greater and less than 14 days, respectively. Conventional descriptive statistics were applied. Multivariate regression model was built to predict the risk of ProLOS, with variables selected using stepwise approach.
A total of 75 patients with confirmed COVID-19 were included for quantitative analysis, including 25 (33%) patients in the ProLOS group. ProLOS patients were more likely to have history of traveling to Wuhan (68% 28%; P=0.002). Patients in the ProLOS group showed lower neutrophil counts [median (IQR): 2.50 (1.77-3.23) ×10/L 2.90 (2.21-4.19) ×10/L; P=0.048], higher partial thrombin time (PT) (13.42±0.63 13.10±0.48 s; P=0.029), lower D-Dimer [0.26 (0.22-0.46) 0.44 (0.32-0.84) mg/L; P=0.012]. There was no patient died and no severe case in our cohort. The overall LOS was 11 days (IQR, 5-15 days). The median cost for a hospital stay was 7,388.19 RMB (IQR, 5,085.39-11,145.44). The prediction model included five variables of procalcitonin, heart rate, epidemiological history, lymphocyte count and cough. The discrimination of the model was 84.8% (95% CI: 75.3% to 94.4%).
Our study described clinical characteristics of COVID-19 outside of Wuhan city and found that the illness was less severe than that in the core epidemic region. A multivariate model was developed to predict ProLOS, which showed good discrimination.
2019年冠状病毒病（COVID-19）疫情已成为全球卫生突发事件，但COVID-19的临床特征尚未得到充分描述。我们旨在描述武汉市以外地区COVID-19的临床特征，并建立一个多变量模型来预测住院时间延长（ProLOS）的风险。
本研究于2020年1月至2月20日在浙江省一家三级医院进行。对所有确诊的COVID-19病例的病历进行回顾性分析。根据住院时间是否大于和小于14天，将患者分别分为ProLOS组和非ProLOS组。应用常规描述性统计方法。采用逐步法选择变量，建立多变量回归模型来预测ProLOS的风险。
共纳入75例确诊的COVID-19患者进行定量分析，其中ProLOS组25例（33%）。ProLOS组患者更有可能有去过武汉的病史（68%对28%；P=0.002）。ProLOS组患者的中性粒细胞计数较低[中位数（IQR）：2.50（1.77 - 3.23）×10⁹/L对2.90（2.21 - 4.19）×10⁹/L；P=0.048]，活化部分凝血活酶时间（PT）较高（13.42±0.63秒对13.10±0.48秒；P=0.029），D - 二聚体较低[0.26（0.22 - 0.46）对0.44（0.32 - 0.84）mg/L；P=0.012]。我们的队列中无患者死亡，也无重症病例。总体住院时间为11天（IQR，5 - 15天）。住院费用中位数为7388.19元人民币（IQR，5085.39 - 11145.44元）。预测模型包括降钙素原、心率、流行病学史、淋巴细胞计数和咳嗽这五个变量。该模型的鉴别力为84.8%（95%CI：75.3%至94.4%）。
我们的研究描述了武汉市以外地区COVID-19的临床特征，发现病情比核心疫区轻。建立了一个预测ProLOS的多变量模型，该模型显示出良好的鉴别力。
