ArticleAging2022
Severe versus common COVID-19: an early warning nomogram model.
Article in Aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- A Nomogram for Predicting Survival in Patients with SARS-CoV-2 Omicron Variant Pneumonia Based on Admission Data.Infection and drug resistance · 2025Article
- Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence.JMIR public health and surveillance · 2024Article
- Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19.Heliyon · 2024Article
- A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.Frontiers in medicine · 2024Article
- Anti-SARS-CoV-2 spike immunoglobulin G and immunoglobulin M titers decline as interval from the second inactivated vaccine dose to the onset of illness is prolonged in breakthrough infection patients.The clinical respiratory journal · 2023Article
- Clinical Characteristics of Severe COVID-19 Patients During Omicron Epidemic and a Nomogram Model Integrating Cell-Free DNA for Predicting Mortality: A Retrospective Analysis.Infection and drug resistance · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The wide spread of coronavirus disease 2019 is currently the most rigorous health threat, and the clinical outcomes of severe patients are extremely poor. In this study, we establish an early warning nomogram model related to severe versus common COVID-19. A total of 1059 COVID-19 patients were analyzed in the primary cohort and divided into common and severe according to the guidelines on the Diagnosis and Treatment of COVID-19 by the National Health Commission of China (7th version). The clinical data were collected for logistic regression analysis to assess the risk factors for severe versus common type. Furthermore, 123 COVID-19 patients were reviewed as the validation cohort to assess the performance of this model. Multivariate logistic analysis revealed that age, dyspnea, lymphocyte count, C-reactive protein and interleukin-6 were independent factors for prewarning the severe type occurrence. Then, the early warning nomogram model including these risk factors for inferring the severe disease occurrence out of common type of COVID-19 was constructed. The C-index of this nomogram in the primary cohort was 0.863, 95% confidence interval (CI) (0.836-0.889). Meanwhile, in the validation cohort, the C-index of this nomogram was 0.889, 95% CI (0.828-0.950). In both the primary cohort and validation cohorts, the calibration curve showed good agreement between prediction and actual probability. The early warning model shows that data at the very beginning including age, dyspnea, lymphocyte count, CRP, and IL-6 may prewarn the severe disease occurrence to some extent, which could help clinicians early and timely treatment.
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