• Songqiao Liu, Huanyuan Luo, Zhengqing Lei, Hao Xu, Tong Hao, Chuang Chen, Yuancheng Wang, Jianfeng Xie, Ling Liu, Shenghong Ju, Haibo Qiu, Duolao Wang, and Yi Yang.
• Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing 210009, China.
• Am J Emerg Med. 2021 Dec 1; 50: 218223218-223.

BackgroundThe 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.MethodsThis 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.ResultsThree 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.ConclusionsThe 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.Copyright © 2021 Elsevier Inc. All rights reserved.

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