ABSTRACT
OBJECTIVE: COVID-19 has tried out global health causing high mortality. There are some risk factors that associate greater severity and mortality from COVID-19;but their individual impact is unknown yet. There are also no fixed criteria for hospital admission. For this reason, this study aimed to analyze the factors associated with the severity of COVID-19 and create predictive models for the risk of hospitalization and death due to COVID-19. METHODS: A descriptive retrospective cohort study was made in Talavera de la Reina (Toledo, Spain). Data were collected through computerized records of Primary Care, Emergencies and Hospitalization. The sample consisted of 275 patients over eighteen years old diagnosed with COVID-19 in a centralized laboratory from March 1st to May 31st, 2020. Analysis was carried on using SPSS, creating two predictive models for the risk of hospitalization and death using linear regression. RESULTS: The probability of hospitalization increased independently with polypharmacy (OR 1.086;CI95% 1.009-1.169), the Charlson index (OR 1.613;CI95% 1.158-2.247), the history of acute myocardial infarction (AMI) (OR 4.358;95% CI 1.114-17.051) and the presence of COVID symptoms (OR 7.001;95% CI 2.805-17.475). The probability of death was independently associated with age, increasing 8.1% (OD 1.081;95% CI 1.054- 1.110) for each year of the patient. CONCLUSIONS: Comorbidity, polypharmacy, history of AMI and the presence of COVID-19 symptoms predict the risk of hospitalization. The age of individuals predicts the risk of death. Detecting patients at high risk of hospitalization and death allows us to define the target population and define measures to implement.