Clinical characteristics of Coronavirus Disease 2019 outside Wuhan and development of early risk stratification tool

Yucai Hong, Jianping Huang, Dong Chen, Yinghai Ye, Feifei Su, Jianyi Dai, Jichan Shi, Chaochao Shao, Zhongheng Zhang

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Received Date: 6th March 20

Clinical characteristics of coronavirus disease 2019 (COVID-19) outside Wuhan are not well described, and there is no risk stratification tool for the prediction of COVID-19 outcome.
To describe the clinical characteristics of COVID-19 outside Wuhan and to develop a risk stratification tool for early risk stratification.
Design, Setting, and Participants
Single center, retrospective observational study conducted at Wenzhou, Zhejiang province (January 2020-February 2020) that included 140 patients with confirmed COVID-19. Clinical characteristics were described. A multivariate regression model was built to predict the risk of length of stay in hospital > 20 days (ProLOS). The last patient visit was on February 23, 2020.
Main Outcomes and Measures 
The primary outcome was ProLOS. Other outcomes included conversion to negative nucleic acid test, date of hospital discharge, vital status at discharge.
A total of 140 patients were included during the study period. Lower lymphocyte count (1.0 [0.7, 1.3] vs. 1.3 [0.9, 1.72] *109/L; p = 0.008), lower ionized sodium (136 [134.6, 137.83] vs. 138 [135.28, 140.03] mmol/L; p < 0.001) and higher PaCO2 (40.82 ± 3.96 vs. 38.48 ± 5.48 mmHg; p = 0.007) were associated with higher risk of ProLOS. The median time from hospital admission to the first negative nucleic acid test was 13 days (7.25 to 17 days). There were 4 (3%) critically and 5 (4%) severely ill patients. A multivariate model included predictors of age, time from contact to confirmation, DBP, lymphocyte count, AST, ionized sodium, PaCO2 showed good calibration and discrimination with an AUC of 81.6% (95% CI: 74.4% to 88.8%). Conclusions and Relevance 
The study described clinical characteristics of COVID-19 outside Wuhan. The major difference of COVID-19 in other cities included low comorbidity burden, low prevalence of severe or critical cases and low mortality rate. The risk stratification tool can be used for medical decision making and resource allocation.

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This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.

Scientific Reports

Nature Research, Springer Nature