Tools
Tools
Liu, Songqiao, Luo, Huanyuan, Lei, Zhengqing, Xu, Hao, Hao, Tong, Chen, Chuang, Wang, Yuancheng, Xie, Jianfeng, Liu, Ling, Ju, Shenghong, Qiu, Haibo, Wang, Duolao 
ORCID: https://orcid.org/0000-0003-2788-2464 and Yang, Yi
(2021)
'A nomogram predicting severe COVID-19 based on a large study cohort from China'. The American Journal of Emergency Medicine, Vol 50, pp. 218-223.
|
Text
A nomogram predicting severe COVID-19 based on a large study cohort from China.pdf - Accepted Version Download (511kB) | Preview |
Abstract
Background
The use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19.
Methods
This study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests.
Results
Three predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90–0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76–0.93 in the validation cohort) and satisfactory agreement.
Conclusions
The nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.
Statistics
Actions (login required)
 |
Edit Item |