To establish a nomogram model for predicting the mortality risk of COVID-19 patients, in order to early screen those who are in higher risk.

All the clinical data of COVID-19 patients were collected from Eastern Campus of Renmin Hospital of Wuhan University during 2020 January to April and North Campus of Shanghai Ninth People Hospital during 2022 April to May. Patients (n=166) from the Renmin Hospital of Wuhan Universiy were considered as training set, while those(n=52) from Shanghai Ninth Peoplef Wuhan Universiy were considered as training sets of Renmin Hospital of Wuhan University during 2020 January to April and North Campuste logistic regression analysis and the R Programming Language was used to conduct the nomogram model. The prediction accuracy and judgment ability of nomogram model were evaluated by receiver operating characteristic curve (ROC), C index and calibration curve, and the clinical application value was evaluated by decision curve analysis. The model was externally validated by the validation set.

A total of 218 patients with severe/critical COVID-19 were included in this study, among whom 67 of them died (30.73%). Multivariate logistic regression analysis showed that more than three kinds of underlying diseases, APACHEⅡ score, neutrophile granulocyte/lymphocyte, and lactate were all independent risk factors. ROC analysis showed that the area under curve (AUC) of training set was 0.869 (95% CI: 0.811-0.927), while the AUC of validation set was 0.797 (95% CI: 0.671-0.924). The calibration curves between the training set and the validation set were tested by Hosmer lemeshow test (P=0.473, P=0.421). Decision curve analysis showed that the nomogram prediction model had high clinical application value.

The nomogram model presents significantly predictive value for mortality risk of COVID-19, which is individualized, visualized and graphically predicted. Whatpredict, it is benefit for physician to make appropriate clinical decisions and treatment at early stage.