ORIGINAL RESEARCH article
Front. Med.
Sec. Infectious Diseases: Pathogenesis and Therapy
Development of a Laboratory-Based Nomogram for Predicting Clinical Outcomes in Patients with Severe COVID-19 Undergoing Glucocorticoid Therapy
Provisionally accepted
- The First Hospital of Jilin University, Changchun, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: While glucocorticoids remain cornerstone therapy for severe COVID-19, substantial heterogeneity persists in clinical outcomes. This single-center retrospective study sought to establish a predictive model integrating inflammatory biomarkers to guide risk stratification and personalized management. Methods: We analysed 151 adults with WHO-defined severe COVID-19 receiving glucocorticoid therapy (December 2022-August 2023). Treatment non-response was defined as mortality during hospitalization, mechanical ventilation escalation, or persistent organ dysfunction. LASSO and logistic regression analyses identified predictors, with optimal biomarker thresholds determined using ROC curves. A nomogram was constructed and validated via split-sample testing (7:3 ratio) and 10-fold cross-validation. Results: Ferritin >970.7 ng/mL and IL-10 >4.79 pg/mL predicted glucocorticoid resistance (AUC: training set 0.779, test set 0.780). The nomogram incorporated diabetes, ferritin, and IL-10, demonstrating robust calibration (Hosmer-Lemeshow P=0.84; Brier score=0.182) and discrimination (sensitivity=71.4%, specificity=70.0%). Diabetic patients exhibited heightened inflammatory responses and poorer outcomes, exacerbated by glucocorticoid-induced hyperglycaemia. Conclusion: This nomogram shows promising predictive performance and provides a potentially implementable framework for risk stratification and personalized management, which warrants prospective validation in larger, multi-center cohorts.
Keywords: Severe COVID-19, glucocorticoid therapy, Nomogram model, Cytokine storm, predictivebiomarkers, ferritin, Interleukin-10
Received: 26 May 2025; Accepted: 24 Nov 2025.
Copyright: © 2025 Xu, Jie, Jia, Song and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dan Li
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.