Predictive Nomogram for Severe COVID-19 and Identification of Mortality-Related Immune Features
Li Cai, Xi Zhou, Miao Wang, Heng Mei, Lisha Ai, Shidai Mu, Xiaoyan Zhao, Wei Chen, Yu Hu, Huafang Wang
Abstract
BackgroundPatients 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.ObjectiveTo build a nomogram for identifying patients with severe COVID-19 and explore the immunological features correlating with fatal outcomes.MethodsWe 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.ResultsThe 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).ConclusionThe 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. 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. To build a nomogram for identifying patients with severe COVID-19 and explore the immunological features correlating with fatal outcomes. 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. 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). 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.