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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.
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10.1016/j.jaip.2020.10.043
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J_Allergy_Clin_Immunol_Pract
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document_parses/pdf_json/cca2c32b050499d1275b7b145429fb423db2e66e.json
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Predictive nomogram for severe COVID-19 and identification of mortality-related immune features
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