EBioMedicine . Development and Validation of the HNC-LL Score for Predicting the Severity of Coronavirus Disease 2019

[tetano]

EBioMedicine


. 2020 Jul 6;57:102880.
doi: 10.1016/j.ebiom.2020.102880. Online ahead of print.
Development and Validation of the HNC-LL Score for Predicting the Severity of Coronavirus Disease 2019


Lu-Shan Xiao ¹ , Wen-Feng Zhang ² , Meng-Chun Gong ³ , Yan-Pei Zhang ⁴ , Li-Ya Chen ⁵ , Hong-Bo Zhu ⁶ , Chen-Yi Hu ⁴ , Pei Kang ⁵ , Li Liu ⁷ , Hong Zhu ⁸



Affiliations

• PMID: 32645614
• DOI: 10.1016/j.ebiom.2020.102880

Abstract

Background: 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.
Methods: 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.
Findings: 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.
Interpretation: 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.
Funding: 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).

Keywords: COVID-19; HNC-LL; Prediction; SARS-COV-2; Severity.