A Nomogram-Based Prediction for Severe Pneumonia in Patients with Coronavirus Disease 2019 (COVID-19).

Background: The outbreak of a novel coronavirus disease 2019 (COVID-19) is currently ongoing worldwide. A proportion of COVID-19 patients progress rapidly to acute respiratory failure.

Objective: We aimed to build a model to predict the risk of developing severe pneumonia in patients with COVID-19 in the early stage.

Methods: Data from patients who were confirmed to have COVID-19 and were admitted within 7 days from the onset of respiratory symptoms were retrospectively collected. The patients were classified into severe and non-severe groups according to the presence or absence of severe pneumonia during 1-2 weeks of follow-up. The clinical characteristics and laboratory indicators were screened by cross-validation based on LASSO regression to build a prediction model presented by a nomogram. The discrimination and stability, as well as the prediction performance of the model, were analysed.

Results: The neutrophil-lymphocyte ratio, monocyte counts, eosinophil percentage, serum lactate dehydrogenase level and history of diabetes mellitus were collected for the model. Bootstrap resampling showed the apparent C-statistics, and the brier scores were 0.929 and 0.098. The optimism of the C-statistics and brier score was 0.0172 and -0.019, respectively. The adjusted C-statistics and brier score were 0.9108 and 0.1169, respectively. The optimal cut-off value of the total nomogram score was determined to be 119 according to the maximal Youden index. The sensitivity, specificity, positive predictive value, and negative predictive value for differentiating the presence and absence of severe pneumonia were 83%, 89%, 74%, and 94%, respectively.

Conclusion: In our study, the neutrophil-lymphocyte ratio, monocyte counts, eosinophil percentage, serum lactate dehydrogenase level and history of diabetes mellitus showed great discrimination and stability for the prediction of the presence of severe pneumonia and were selected for the model.