Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

doi:10.1038/s41746-021-00433-4

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong
	Zhou, Jiandong 1; Lee, Sharen 2; Wang, Xiansong 3; Li, Yi4 ; Wu, William Ka Kei 3; Liu, Tong 5; Cao, Zhidong6 ; Zeng, Daniel Dajun6 ; Leung, Keith Sai Kit 7; Wai, Abraham Ka Chung 7; Wong, Ian Chi Kei 8,9; Cheung, Bernard Man Yung 10; Zhang, Qingpeng 1; Tse, Gary 4
发表期刊	NPJ DIGITAL MEDICINE
ISSN	2398-6352
	2021-04-08
卷号	4 期号:1 页码:9
通讯作者	Zhang, Qingpeng(qingpeng.zhang@cityu.edu.hk) ; Tse, Gary(garytse@tmu.edu.cn)
摘要	Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong's public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82-0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85-0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results.
DOI	10.1038/s41746-021-00433-4
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[71972164] ; National Natural Science Foundation of China (NSFC)[72042018] ; Health and Medical Research Fund of the Food and Health Bureau of Hong Kong[16171991] ; Innovation and Technology Fund of Innovation and Technology Commission of Hong Kong[MHP/081/19] ; National Key Research and Development Program of China, Ministry of Science and Technology of China[2019YFE0198600] ; Collaborative Research Fund (CRF) of Research Grants Council of Hong Kong[C7154-20G]
项目资助者	National Natural Science Foundation of China (NSFC) ; Health and Medical Research Fund of the Food and Health Bureau of Hong Kong ; Innovation and Technology Fund of Innovation and Technology Commission of Hong Kong ; National Key Research and Development Program of China, Ministry of Science and Technology of China ; Collaborative Research Fund (CRF) of Research Grants Council of Hong Kong
WOS研究方向	Health Care Sciences & Services ; Medical Informatics
WOS类目	Health Care Sciences & Services ; Medical Informatics
WOS记录号	WOS:000638117000001
出版者	NATURE RESEARCH
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44273
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Zhang, Qingpeng; Tse, Gary
作者单位	1.City Univ Hong Kong, Sch Data Sci, Hong Kong, Peoples R China 2.Cardiovasc Analyt Grp, Lab Cardiovasc Physiol, Hong Kong, Peoples R China 3.Li Ka Shing Inst Hlth Sci, Hong Kong, Peoples R China 4.Wuhan Univ Sci & Technol, Wuhan Asia Heart Hosp, Dept Cardiothorac Surg, Wuhan, Hubei, Peoples R China 5.Tianjin Med Univ, Tianjin Inst Cardiol, Tianjin Key Lab Ion Mol Funct Cardiovasc Dis, Hosp 2, Tianjin, Peoples R China 6.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 7.Univ Hong Kong, LKS Fac Med, Emergency Med Unit, Pokfulam, Hong Kong, Peoples R China 8.Univ Hong Kong, Dept Pharmacol & Pharm, Pokfulam, Hong Kong, Peoples R China 9.UCL Sch Pharm, Med Optimisat Res & Educ CMORE, London, England 10.Univ Hong Kong, Dept Med, Pokfulam, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Jiandong,Lee, Sharen,Wang, Xiansong,et al. Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong[J]. NPJ DIGITAL MEDICINE,2021,4(1):9.
APA	Zhou, Jiandong.,Lee, Sharen.,Wang, Xiansong.,Li, Yi.,Wu, William Ka Kei.,...&Tse, Gary.(2021).Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong.NPJ DIGITAL MEDICINE,4(1),9.
MLA	Zhou, Jiandong,et al."Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong".NPJ DIGITAL MEDICINE 4.1(2021):9.