ArticleFrontiers in public health2023
Development and validation of a prediction model based on comorbidities to estimate the risk of in-hospital death in patients with COVID-19.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- A machine learning model for predicting six-month rehospitalization in heart failure patients with multiple comorbidities.Frontiers in cardiovascular medicine · 2026Article
- Plasma Proteomic Profiling Yields a High-Performance Biomarker Panel for Predicting a Poor Prognosis in Patients with COVID-19.Journal of proteome research · 2025Article
- Admission prioritization of heart failure patients with multiple comorbidities.Frontiers in digital health · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Most existing prognostic models of COVID-19 require imaging manifestations and laboratory results as predictors, which are only available in the post-hospitalization period. Therefore, we aimed to develop and validate a prognostic model to assess the in-hospital death risk in COVID-19 patients using routinely available predictors at hospital admission. Methods: We conducted a retrospective cohort study of patients with COVID-19 using the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Patients hospitalized in Eastern United States (Florida, Michigan, Kentucky, and Maryland) were included in the training set, and those hospitalized in Western United States (Nevada) were included in the validation set. Discrimination, calibration, and clinical utility were evaluated to assess the model's performance. Results: A total of 17 954 in-hospital deaths occurred in the training set ( Conclusion: An easy-to-use prognostic model based on predictors readily available at hospital admission was developed and validated for the early identification of COVID-19 patients with a high risk of in-hospital death. This model can be a clinical decision-support tool to triage patients and optimize resource allocation.
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