A risk score based on baseline risk factors for predicting mortality in COVID-19 patients. (3rd June 2021)

• Record Type: Journal Article
• Authors: Chen, Ze; Chen, Jing; Zhou, Jianghua; Lei, Fang; Zhou, Feng; Qin, Juan-Juan; Zhang, Xiao-Jing; Zhu, Lihua; Liu, Ye-Mao; Wang, Haitao; Chen, Ming-Ming; Zhao, Yan-Ci; Xie, Jing; Shen, Lijun; Song, Xiaohui; Zhang, Xingyuan; Yang, Chengzhang; Liu, Weifang; Zhang, Xiao; Guo, Deliang; Yan, Youqin; Liu, Mingyu; Mao, Weiming; Liu, Liming; Ye, Ping; Xiao, Bing; Luo, Pengcheng; Zhang, Zixiong; Lu, Zhigang; Wang, Junhai; Lu, Haofeng; Xia, Xigang; Wang, Daihong; Liao, Xiaofeng; Peng, Gang; Liang, Liang; Yang, Jun; Chen, Guohua; Azzolini, Elena; Aghemo, Alessio; Ciccarelli, Michele; Condorelli, Gianluigi; Stefanini, Giulio G.; Wei, Xiang; Zhang, Bing-Hong; Huang, Xiaodong; Xia, Jiahong; Yuan, Yufeng; She, Zhi-Gang; Guo, Jiao; Wang, Yibin; Zhang, Peng; Li, Hongliang;
• Is Part Of: Current medical research and opinion. Volume 37:Number 6(2021)
• Page Start: 917
• Page End: 927
• Publication Date: 2021-06-03
• Subjects: COVID-19 -- risk score -- modeling -- mortality -- in-hospital; Clinical medicine -- Periodicals; Therapeutics -- Periodicals; 615.5
• DOI: 10.1080/03007995.2021.1904862
• Languages: English
• ISSNs: 0300-7995

Abstract

Abstract: Background: 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. Methods: 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. Results: Eight factors, namely, O xygen saturation, blood U rea nitrogen, R espiratory rate, admission before the date the national M aximum number of daily new cases was reached, A ge, P rocalcitonin, C -reactive protein (CRP), and absolute N 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 >11 compared with those with scores ≤ 11 was 18.18 (95% CI 13.93–23.71; p < .0001). The predictive performance, specificity, and sensitivity of the score were validated in three independent cohorts. Conclusions: 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.