Clinical diagnosis of severe COVID-19:A derivation and validation of a prediction rule  

作　　者：Ming Tang Xia-Xia Yu Jia Huang Jun-Ling Gao Fu-Lan Cen Qi Xiao Shou-Zhi Fu Yang Yang Bo Xiong Yong-Jun Pan Ying-Xia Liu Yong-Wen Feng Jin-Xiu Li Yong Liu 

机构地区：[1]Department of Critical Care Medicine,Shenzhen Third People’s Hospital,The Second Hospital Affiliated to Southern University of Science and Technology,Shenzhen 518114,Guangdong Province,China [2]School of Biomedical Engineering,Health Science Center,Shenzhen University,Shenzhen 518060,Guangdong Province,China [3]Buddhism and Science Research Laboratory,Centre of Buddhist Studies,The University of Hong Kong,Hong Kong 999077,China [4]Department of Critical Care Medicine,Wuhan Third Hospital,Wuhan 433304,Hubei Province,China [5]Shenzhen Key Laboratory of Pathogen and Immunity,National Clinical Research Center for Infectious Disease,State Key Discipline of Infectious Disease,Shenzhen Third People's Hospital,The Second Hospital Affiliated to Southern University of Science and Technology,Shenzhen 518114,Guangdong Province,China [6]Department of Pediatrics,Wuhan Asia General Hospital,Wuhan 430022,Hubei Province,China [7]Department of Critical Care Medicine,Southern University of Science and Technology Hospital,Shenzhen 518055,Guangdong Province,China [8]Department of Critical Care Medicine,The Second People's Hospital of Shenzhen,Shenzhen 518035,Guangdong Province,China [9]Department of Critical Care Medicine,The Second Xiangya Hospital,Central South University,Changsha 410011,Hunan Province,China [10]Shenzhen Hospital,Southern Medical University,Shenzhen 518000,Guangdong Province,China

出　　处：《World Journal of Clinical Cases》2021年第13期2994-3007,共14页世界临床病例杂志

摘　　要：BACKGROUND The widespread coronavirus disease 2019(COVID-19)has led to high morbidity and mortality.Therefore,early risk identification of critically ill patients remains crucial.AIM To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit(ICU)care.METHODS This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19,2020,and March 14,2020 in Shenzhen Third People’s Hospital.Multivariate logistic regression was applied to develop the predictive model.The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020,by area under the receiver operating curve(AUROC),goodness-of-fit and the performance matrix including the sensitivity,specificity,and precision.A nomogram was also used to visualize the model.RESULTS Among the patients in the derivation and validation datasets,38 and 9 participants(10.5%and 2.54%,respectively)developed severe COVID-19,respectively.In univariate analysis,21 parameters such as age,sex(male),smoker,body mass index(BMI),time from onset to admission(>5 d),asthenia,dry cough,expectoration,shortness of breath,asthenia,and Rox index<18(pulse oxygen saturation,SpO2)/(FiO2×respiratory rate,RR)showed positive correlations with severe COVID-19.In multivariate logistic regression analysis,only six parameters including BMI[odds ratio(OR)3.939;95%confidence interval(CI):1.409-11.015;P=0.009],time from onset to admission(≥5 d)(OR 7.107;95%CI:1.449-34.849;P=0.016),fever(OR 6.794;95%CI:1.401-32.951;P=0.017),Charlson index(OR 2.917;95%CI:1.279-6.654;P=0.011),PaO2/FiO2 ratio(OR 17.570;95%CI:1.117-276.383;P=0.041),and neutrophil/lymphocyte ratio(OR 3.574;95%CI:1.048-12.191;P=0.042)were found to be independent predictors of COVID-19.These factors were found to be significant risk factors for severe patients confirmed with C

关 键 词：COVID-19 Communicable diseases Clinical decision rules PROGNOSIS NOMOGRAMS 

分 类 号：R563.1[医药卫生—呼吸系统]