ArticleScientific reports2021
Finding of the factors affecting the severity of COVID-19 based on mathematical models.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
6 citing papers in PubMed.
- A Qualitative Framework for Ranking the Relative Likelihood of SARS-CoV-2 Transmission Between People and Felids at Zoological Interfaces.Animals : an open access journal from MDPI · 2026Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Mutations in SARS-CoV-2: Insights on structure, variants, vaccines, and biomedical interventions.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2023Review
- An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples.Sensors (Basel, Switzerland) · 2022Article
- Are lipid ratios and triglyceride-glucose index associated with critical care outcomes in COVID-19 patients?PloS one · 2022Article
- Remdesivir Reduces Mortality in Hemato-Oncology Patients with COVID-19.Journal of inflammation research · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Since 2019, a large number of people worldwide have been infected with severe acute respiratory syndrome coronavirus 2. Among those infected, a limited number develop severe coronavirus disease 2019 (COVID-19), which generally has an acute onset. The treatment of patients with severe COVID-19 is challenging. To optimize disease prognosis and effectively utilize medical resources, proactive measures must be adopted for patients at risk of developing severe COVID-19. We analyzed the data of COVID-19 patients from seven medical institutions in Tokyo and used mathematical modeling of patient blood test results to quantify and compare the predictive ability of multiple prognostic indicators for the development of severe COVID-19. A machine learning logistic regression model was used to analyze the blood test results of 300 patients. Due to the limited data set, the size of the training group was constantly adjusted to ensure that the results of machine learning were effective (e.g., recognition rate of disease severity > 80%). Lymphocyte count, hemoglobin, and ferritin levels were the best prognostic indicators of severe COVID-19. The mathematical model developed in this study enables prediction and classification of COVID-19 severity.
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Registered trials
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