Predictive Nomogram for Severe COVID-19 and Identification of Mortality-Related Immune Features

BACKGROUND: Patients with severe 2019 novel coronavirus disease (COVID-19) have a high mortality rate. The early identification of severe COVID-19 is of critical concern. In addition, the correlation between the immunological features and clinical outcomes in severe cases needs to be explored. OBJECTIVE: To build a nomogram for identifying patients with severe COVID-19 and explore the immunological features correlating with fatal outcomes. METHODS: We retrospectively enrolled 85 and 41 patients with COVID-19 in primary and validation cohorts, respectively. A predictive nomogram based on risk factors for severe COVID-19 was constructed using the primary cohort and evaluated internally and externally. In addition, in the validation cohort, immunological features in patients with severe COVID-19 were analyzed and correlated with disease outcomes. RESULTS: The risk prediction nomogram incorporating age, C-reactive protein, and D-dimer for early identification of patients with severe COVID-19 showed favorable discrimination in both the primary (area under the curve [AUC] 0.807) and validation cohorts (AUC 0.902) and was well calibrated. Patients who died from COVID-19 showed lower abundance of peripheral CD45RO(+)CD3(+) T cells and natural killer cells, but higher neutrophil counts than that in the patients who recovered (P = .001, P = .009, and P = .009, respectively). Moreover, the abundance of CD45RO(+)CD3(+) T cells, neutrophil-to-lymphocyte ratio, and neutrophil-to-natural killer cell ratio were strong indicators of death in patients with severe COVID-19 (AUC 0.933 for all 3). CONCLUSION: The novel nomogram aided the early identification of severe COVID-19 cases. In addition, the abundance of CD45RO(+)CD3(+) T cells and neutrophil-to-lymphocyte and neutrophil-to-natural killer cell ratios may serve as useful prognostic predictors in severe patients.