A risk score based on baseline risk factors for predicting mortality in COVID-19 patients
Creators: name: Chen, Ze
name: Chen, Jing
name: Zhou, Jianghua
name: Lei, Fang
name: Zhou, Feng
name: Qin, Juan-Juan
name: Zhang, Xiao-Jing
name: Zhu, Lihua
name: Liu, Ye-Mao
name: Wang, Haitao
name: Chen, Ming-Ming
name: Zhao, Yan-Ci
name: Xie, Jing
name: Shen, Lijun
name: Song, Xiaohui
name: Zhang, Xingyuan
name: Yang, Chengzhang
name: Liu, Weifang
name: Zhang, Xiao
name: Guo, Deliang
name: Yan, Youqin
name: Liu, Mingyu
name: Mao, Weiming
name: Liu, Liming
name: Ye, Ping
name: Xiao, Bing
name: Luo, Pengcheng
name: Zhang, Zixiong
name: Lu, Zhigang
name: Wang, Junhai
name: Lu, Haofeng
name: Xia, Xigang
name: Wang, Daihong
name: Liao, Xiaofeng
name: Peng, Gang
name: Liang, Liang
name: Yang, Jun
name: Chen, Guohua
name: Azzolini, Elena
name: Aghemo, Alessio
name: Ciccarelli, Michele
name: Condorelli, Gianluigi
name: Stefanini, Giulio G.
name: Wei, Xiang
name: Zhang, Bing-Hong
name: Huang, Xiaodong
name: Xia, Jiahong
name: Yuan, Yufeng
name: She, Zhi-Gang
name: Guo, Jiao
name: Wang, Yibin
name: Zhang, Peng
name: Li, Hongliang
Publisher: name: Taylor & Francis
Year: 2021
Subjects: subject: Medicine
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Science Policy
subject: Hematology

description: To develop a sensitive and clinically applicable risk assessment tool identifying coronavirus disease 2019 (COVID-19) patients with a high risk of mortality at hospital admission. This model would assist frontline clinicians in optimizing medical treatment with limited resources. 6415 patients from seven hospitals in Wuhan city were assigned to the training and testing cohorts. A total of 6351 patients from another three hospitals in Wuhan, 2169 patients from outside of Wuhan, and 553 patients from Milan, Italy were assigned to three independent validation cohorts. A total of 64 candidate clinical variables at hospital admission were analyzed by random forest and least absolute shrinkage and selection operator (LASSO) analyses. Eight factors, namely, <b>O</b>xygen saturation, blood <b>U</b>rea nitrogen, <b>R</b>espiratory rate, admission before the date the national <b>M</b>aximum number of daily new cases was reached, <b>A</b>ge, <b>P</b>rocalcitonin, <b>C</b>-reactive protein (CRP), and absolute <b>N</b>eutrophil counts, were identified as having significant associations with mortality in COVID-19 patients. A composite score based on these eight risk factors, termed the OURMAPCN-score, predicted the risk of mortality among the COVID-19 patients, with a C-statistic of 0.92 (95% confidence interval [CI] 0.90–0.93). The hazard ratio for all-cause mortality between patients with OURMAPCN-score &gt;11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93–23.71; <i>p</i> &lt; .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. The OURMAPCN score is a risk assessment tool to determine the mortality rate in COVID-19 patients based on a limited number of baseline parameters. This tool can assist physicians in optimizing the clinical management of COVID-19 patients with limited hospital resources.