ArticleInternational journal of general medicine2021
Predictors of COVID-19 Hospital Treatment Outcome.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The trial behind it
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Who cites it
5 citing papers in PubMed.
- Association of inflammation and protein carbamylation in patients with COVID-19.Frontiers in medicine · 2025Article
- Calprotectin, a Promising Serological Biomarker for the Early Diagnosis of Superinfections with Multidrug-Resistant Bacteria in Patients with COVID-19.International journal of molecular sciences · 2024Article
- Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients.Children (Basel, Switzerland) · 2023Article
- Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case.Journal of personalized medicine · 2022Article
- Serum ACE2 Level is Associated With Severe SARS-CoV-2 Infection: A Cross-Sectional Observational Study.Biomarker insights · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere are more than 228,394,572 confirmed cases and 4,690,186 confirmed deaths caused by COVID-19 worldwide. The magnitude of the COOVID-19 pandemic has stimulated research on the treatment and diagnosis of COVID-19 patients.
objectiveIn this report, a battery of specific parameters was used to develop a model that allows prediction of the outcome of the COVID-19 treatment. These parameters are C-reactive protein, procalcitonin, fibrinogen, D-dimers, immature granulocytes, and interleukin-6.
methodsThe study was carried out on a sample of N = 49 survivors (22 men, 27 women) and 83 deceased patients (62 men, 21 women). The distribution of means and differences in means of the parameters studied between survivors and deceased patients were evaluated using the bootstrap method.
resultsA mathematical model that allows for the prediction of hospitalization outcome was obtained using the Naive Bayes model. The results demonstrated a statistically significant difference between survivors and deceased patients in all parameters studied. A mathematical model employing a battery of parameters provided a 97% precision in predicting the outcome of hospitalization.
conclusionThis study showed that the cross-correlation of survivability with absolute levels of C-reactive protein, procalcitonin, fibrinogen, D-dimers, immature granulocytes, and interleukin-6 could be used successfully in the hospital setting as a diagnostic tool.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.