ArticleVirology journal2024
Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.
Article in Virology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Factors affecting the impact of COVID-19 vaccination on post COVID-19 conditions among adults: A systematic literature review.Human vaccines & immunotherapeutics · 2025Pooled it
- Associated factors and predictive nomogram of long COVID: a cross-sectional study in China.BMC pulmonary medicine · 2026Article
- Dysregulated Resolution of Inflammation After Respiratory Viral Infections: Molecular Pathways Linking Neuroinflammation to Post-Viral Neuropathic Pain-A Narrative Review.International journal of molecular sciences · 2025Review
- A Predictive Model for the Development of Long COVID in Children.International journal of environmental research and public health · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundLong coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model.
methodsA total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
resultsA total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort.
conclusionsWe established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.
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