Early prediction and identification for severe patients during the pandemic of Covid-19

A severe Covid-19 risk model constructed by multivariate logistic regression analysis

Bibliographic Data

ID19550413
AuthorsHaifeng Hu (0000-0002-4884-323X, Air Force Medical University), Hong Du (0009-0007-8703-6055, Xi'an Medical University), Jing Li (0000-0001-7792-4322, Air Force Medical University), Yage Wang (0009-0008-0651-3313, Air Force Medical University), Xiaoqing Wu (0009-0008-9958-8655, Air Force Medical University), Chunfu Wang (0000-0002-7843-3549, Xi'an Medical University), Ye Zhang (0009-0002-2442-4804, Air Force Medical University), Gufen Zhang (Air Force Medical University), Yanyan Zhao (0009-0005-8467-2801, Xi'an Medical University), Wen Kang (0000-0001-6095-9879, Xi'an Medical University), Jianqi Lian (0000-0002-5549-7590, Xi'an Medical University, corresponding author)
Year2020
Volume10
Issue2
Pages020510-020510
Publication date2020-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Global Health (JOURNAL)
Journal identifiersISSN: 2047-2978 • E-ISSN: 2047-2986
PublisherInternational Society of Global Health (PUBLISHER • GB)
DOI10.7189/jogh.10.020510
PMID33110593
OpenAlexW3092258613
LanguageEN
References cited16

BACKGROUND: As an emergent and fulminant infectious disease, Corona Virus Disease 2019 (COVID-19) has caused a worldwide pandemic. The early identification and timely treatment of severe patients are crucial to reducing the mortality of COVID-19. This study aimed to investigate the clinical characteristics and early predictors for severe COVID-19, and to establish a prediction model for the identification and triage of severe patients. METHODS: All confirmed patients with COVID-19 admitted by the Second Affiliated Hospital of Air Force Medical University were enrolled in this retrospective non-interventional study. The patients were divided into a mild group and a severe group, and the clinical data were compared between the two groups. Univariate and multivariate analysis were used to identify the independent early predictors for severe COVID-19, and the prediction model was constructed by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the prediction model and each early predictor. RESULTS: < 0.05). Univariate and multivariate analysis showed that venerable age, hypertension, lymphopenia, hypoalbuminemia and elevated neutrophil lymphocyte ratio (NLR) were the independent high-risk factors for severe COVID-19. ROC curves demonstrated significant predictive value of age, lymphocyte count, albumin and NLR for severe COVID-19. The sensitivity and specificity of the newly constructed prediction model for predicting severe COVID-19 was 90.5% and 84.2%, respectively, and whose positive predictive value, negative predictive value and crude agreement were all over 85%. CONCLUSIONS: The severe COVID-19 risk model might help clinicians quickly identify severe patients at an early stage and timely take optimal therapeutic schedule for them

2019-20 coronavirus outbreak · Biology · Disease · Logistic regression · Multivariate analysis · Multivariate statistics · Outbreak · Pandemic · Statistics · COVID-19 Clinical Research Studies · COVID-19 Impact on Reproduction · Mathematics · Medicine · Sepsis Diagnosis and Treatment · Internal Medicine · Virology