Predictive nomogram for severe COVID-19 and identification of mortality-related immune features

Cai, Li; Zhou, Xi; Wang, Miao; Mei, Heng; Ai, Lisha; Mu, Shidai; Zhao, Xiaoyan; Chen, Wei; Hu, Yu; Wang, Huafang

Background Severe COVID-19 patients have a high mortality rate. The early identification of severe COVID-19 is of critical concern. Additionally, the correlation between the immunological features and clinical outcomes in severe cases needs to be explored. Objective To build a nomogram for identifying severe COVID-19 patients 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. Additionally, 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 severe COVID-19 patients showed favorable discrimination in both the primary (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=0.001, P=0.009, and P=0.009, 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 severe COVID-19 patients (AUC 0.933 for all three). Conclusion The novel nomogram aided the early identification of severe COVID-19 cases. Additionally, 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.