Prognostic Factors for Mortality in Adults Hospitalized With COVID-19 Infection in the Americas.

Abstract

Many reports on COVID-19have highlighted an association between patient characteristics upon admission for COVID-19 and how they affect variable progressions of the disease severity and, ultimately mortality. In this cohort, we analyzed data from patients in the Americas who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, and were hospitalized. We retrieved data from identified studies selected from full-text screening to identify relevant patient demographics: age, gender, race, duration of hospital stay, and comorbidities. Data were assessed for consistency, missing values, presence of outliers, and implausible values were documented. Our analysis was performed by calculating the case fatality rates for data reported by the original authors within the studies. The primary outcome was mortality. Participants in the review were COVID-19 inpatients diagnosed through reverse-transcriptase polymerase chain reaction tests (RT-PCR) and above the age of 45 years in the North and South American continents. The study included 12,895 patients who were hospitalized with the COVID-19 infection, and an overall 14.4% in-hospital case-fatality rate was demonstrated. The prognostic factor with the most significant association for increased risk of in-hospital mortality in the elderly was having a high viral load of the COVID-19 virus with a case-fatality rate of 41.4%. In addition, being of Caucasian ethnicity and having the comorbidity of chronic obstructive pulmonary disease (COPD) were also associated with higher rates of mortality among elderly hospitalized patients with case-fatality rates of 40.8% and 35.7% respectively. Prognostic factors for COVID-19 mortality were determined by having a high viral load of the infection, being of Caucasian ethnicity, and having underlying pulmonary comorbidities such as COPD. In the future research should be undertaken to determine the underlying mechanisms behind these effects.