Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates.
| Autor: | Baez AA; Emergency Medicine/Public Health/Epidemiology, Augusta University/Medical College of Georgia, Augusta, USA.; Postgraduate Studies, Universidad Nacional Pedro Henríquez Ureña, Santo Domingo, DOM., Lopez OJ; Internal Medicine Residency, University of Texas Rio Grande Valley, Rio Grande, USA., Martinez M; Surgery, University of Washington Medical Center, Washington, USA., White C; Health Sciences, University of South Florida, Tampa, USA., Ramirez-Slaibe P; Health Sciences, Centro de Investigación y Estudios en Gerencia de Salud, Santo Domingo, DOM., Martinez L; Actuarial, Superintendencia de Salud, Santo Domingo, DOM., Castellanos PL; Epidemiology and Public Health, Superintendent of Health and Labor Risks (SISALRIL), Santo Domingo, DOM. |
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| Jazyk: | angličtina |
| Zdroj: | Cureus [Cureus] 2022 Jul 12; Vol. 14 (7), pp. e26781. Date of Electronic Publication: 2022 Jul 12 (Print Publication: 2022). |
| DOI: | 10.7759/cureus.26781 |
| Abstrakt: | Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19. Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences. Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 > 2 led to a "number needed to (NNT) diagnose" that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model. Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making. Competing Interests: The authors have declared that no competing interests exist. (Copyright © 2022, Baez et al.) |
| Databáze: | MEDLINE |
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