RESUMO
A Bayesian analysis with the use of a rank-biserial correlation algorithm was applied to identify the impact of multiple comorbid conditions on fatal COVID-19 outcome in young adult cases (40-50 years). The demonstration was conducted for a publicly available database provided by the Mexican authority, in the absence of other alternative free-access repositories with information per patient. The methodology here proposed showed that even in the face of small sample sizes, it is possible to highlight deleterious synergistic comorbid conditions. Young adult cases with COVID-19 and co-existing diabetes, obesity, hypertension, CRF, or COPD were found more likely to have a fatal outcome compared with having no co-morbidities (X2-6 times). With the methodology proposed, we show that having diabetes or hypertension in addition to CRF increased risk for mortality more than what is expected from independent effect (adverse synergistic effect), whereas in patients with obesity, the additional presence of diabetes or hypertension do not increase markedly the death risk due to COVID-19. Quantitative analysis of having two comorbidities highlights the combinations of morbid conditions that are more likely to be associated with fatal outcomes in younger adults COVID-19 cases in a clinically applicable manner. The clinical implication of this method needs to be prospectively assessed.
