Clinical Management Strategies in Mild COVID-19 cases in Latin-America: A Decision Model
DOI:
https://doi.org/10.1590/SciELOPreprints.681Keywords:
Coronavirus infections, Theoretical models, Resource allocationAbstract
COVID-19 pandemic is challenging Latin American health systems, which could benefit from information to make appropriate decisions in contexts of constrained health resources.
Objective: to evaluate, in adult patients with suspected mild clinical forms of COVID-19, the clinical effectiveness (life expectancy) and resource consumption (days of hospitalization) of different management strategies.
Methods: stochastic decision tree comparing the conventional strategy recommended by WHO - PAHO (diagnostic test for COVID-19 and hospitalization of patients testing positive) versus two alternative strategies (immediate addition of a prognostic test with hospitalization according to its result, or follow-up with hospitalization only in case of clinical deterioration).
Results: the alternative management strategies showed expected clinical utility similar to the conventional strategy in 80 years-old base cases, and slightly lower in 60 years- and 40 years-old base cases, with lower consumption of hospitalization days. In sensitivity analysis, alternative strategies comparatively improved their expected clinical utility given a lower sensitivity of the diagnostic test or a higher ability of the follow-up to detect clinical worsening.
Conclusions: in cases of suspected COVID-19 without pneumonic infiltrate or signs of severity, alternative strategies can be considered to avoid hospitalization for the majority of patients, especially if efficient follow-up modalities can be implemented. This information is valuable for health decision-makers, to carefully weight clinical and epidemiological elements and design locally feasible strategies.
Downloads
Submitted
Posted
How to Cite
Section
Copyright (c) 2020 Carlos Boissonnet, Mariano Giorgi, Lucila Carosella, Carola Brescacín, Jerónimo Pissinis, Javier Guetta

This work is licensed under a Creative Commons Attribution 4.0 International License.


