Clinical characteristics of Coronavirus Disease 2019 and development of a prediction model for prolonged hospital length of stay

Abstract

Background: 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).

Methods: 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.

Results: 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% vs. 28%; P=0.002). Patients in the ProLOS group showed lower neutrophil counts [median (IQR): 2.50 (1.77-3.23) ×10⁹/L vs. 2.90 (2.21-4.19) ×10⁹/L; P=0.048], higher partial thrombin time (PT) (13.42±0.63 vs. 13.10±0.48 s; P=0.029), lower D-Dimer [0.26 (0.22-0.46) vs. 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%).

Conclusions: 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.

Keywords: Novel coronavirus; clinical characteristics; cost; length of stay (LOS); prediction.

Figure 1

Discrimination of the prediction model. The area under curve (AUC) was 84.8% (95% CI: 75.3% to 94.4%).

Figure 2

Nomogram demonstrating the use of the prediction model. Each patient can have a point in each of the items and then the points are summed together to get a total point. In the above example, the patient has a total point of 280, which corresponds to the probability of 87.5% to prolonged hospital stay. *, significance with P<0.05; **, significance with P<0.01. PCTAbnorm, abnormal procalcitonin; LymphLess1, lymphocyte count less than ×10⁹/L; HR, heart rate; EpidemiHis, epidemiological history; LOS, length of stay in hospital.