Development and validation of the HNC-LL score for predicting the severity of coronavirus disease 2019 - Xiao, Lu-shan; Zhang, Wen-Feng; Gong, Meng-chun; Zhang, Yan-pei; Chen, Li-ya; Zhu, Hong-bo; Hu, Chen-yi; Kang, Pei; Liu, Li; Zhu, Hong (EBioMedicine, 2020)
Information regarding risk factors associated with severe coronavirus disease (COVID-19) is limited. This study aimed to develop a model for predicting COVID-19 severity. Overall, 690 patients with confirmed COVID-19 were recruited between 1 January and 18 March 2020 from hospitals in Honghu and Nanchang; finally, 442 patients were assessed. Data were categorised into the training and test sets to develop and validate the model, respectively. A predictive HNC-LL (Hypertension, Neutrophil count, C-reactive protein, Lymphocyte count, Lactate dehydrogenase) score was established using multivariate logistic regression analysis. The HNC-LL score accurately predicted disease severity in the Honghu training cohort (area under the curve [AUC]=0.861, 95% confidence interval [CI]: 0.800–0.922; P<0.001); Honghu internal validation cohort (AUC=0.871, 95% CI: 0.769–0.972; P<0.001); and Nanchang external validation cohort (AUC=0.826, 95% CI: 0.746–0.907; P<0.001) and outperformed other models, including CURB-65 (confusion, uraemia, respiratory rate, BP, age ≥65 years) score model, MuLBSTA (multilobular infiltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hypertension, and age) score model, and neutrophil-to-lymphocyte ratio model. The clinical significance of HNC-LL in accurately predicting the risk of future development of severe COVID-19 was confirmed. We developed an accurate tool for predicting disease severity among COVID-19 patients. This model can potentially be used to identify patients at risks of developing severe disease in the early stage and therefore guide treatment decisions. This work was supported by the National Nature Science Foundation of China (grant no. 81972897) and Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015).