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一种基于基线风险因素的风险评分,用于预测新冠肺炎患者的死亡率。

A risk score based on baseline risk factors for predicting mortality in COVID-19 patients.

作者信息

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

机构信息

Department of Cardiology, Renmin Hospital, School of Basic Medical Science, Wuhan University, Wuhan, China.

Institute of Model Animal, Wuhan University, Wuhan, China.

出版信息

Curr Med Res Opin. 2021 Jun;37(6):917-927. doi: 10.1080/03007995.2021.1904862. Epub 2021 Apr 10.

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, xygen saturation, blood rea nitrogen, espiratory rate, admission before the date the national aximum number of daily new cases was reached, ge, rocalcitonin, -reactive protein (CRP), and absolute 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;  < .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.

摘要

背景

开发一种敏感且适用于临床的风险评估工具,以识别在入院时具有高死亡风险的2019冠状病毒病(COVID-19)患者。该模型将帮助一线临床医生在资源有限的情况下优化医疗治疗。

方法

将来自武汉市七家医院的6415名患者分配到训练和测试队列中。将来自武汉市另外三家医院的6351名患者、来自武汉以外地区的2169名患者以及来自意大利米兰的553名患者分配到三个独立的验证队列中。通过随机森林和最小绝对收缩和选择算子(LASSO)分析对入院时总共64个候选临床变量进行了分析。

结果

八个因素,即血氧饱和度、血尿素氮、呼吸频率、在全国每日新增病例数达到最大值日期之前入院、年龄、降钙素原(PCT)、C反应蛋白(CRP)和绝对中性粒细胞计数,被确定与COVID-19患者的死亡率有显著关联。基于这八个风险因素的综合评分,称为OURMAPCN评分,可预测COVID-19患者的死亡风险,C统计量为0.92(95%置信区间[CI]0.90-0.93)。OURMAPCN评分>11的患者与评分≤11的患者相比,全因死亡率的风险比为18.18(95%CI 13.93-23.71;P<0.0001)。该评分的预测性能、特异性和敏感性在三个独立队列中得到了验证。

结论

OURMAPCN评分是一种基于有限数量的基线参数来确定COVID-19患者死亡率的风险评估工具。该工具可帮助医生在医院资源有限的情况下优化COVID-19患者的临床管理。

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