Hospitalized COVID-19 Patients with Severe Acute Respiratory Syndrome: A Population-Based Registry Analysis to Assess Clinical Findings, Pharmacological Treatment and Survival.
Eduardo Gutierrez-AbejónFrancisco Herrera-GómezM Aránzazu Pedrosa-NaudínEduardo TamayoFrancisco Javier ÁlvarezPublished in: Medicina (Kaunas, Lithuania) (2022)
Background and Objectives: One of the most serious clinical outcomes in hospitalized patients with COVID-19 is severe acute respiratory syndrome (SARS). The aim is to analyze pharmacological treatment, survival and the main mortality predictors. Materials and Methods: A real-world data study from COVID-19-hospitalized patients with SARS from 1 March to 31 May 2020 has been carried out. Variables such as hospital length of stay, ventilation type and clinical outcomes have been taken into account. Results: In Castile and Leon, 14.03% of the 7307 in-hospital COVID-19 patients developed SARS, with a mortality rate of 42.53%. SARS prevalence was doubled in males compared to females, and 78.54% had an age of 65 years or more. The most commonly used medicines were antibiotics (89.27%), antimalarials (68.1%) and corticosteroids (55.9%). Survival of patients developing SARS was lower compared to patients without this complication (12 vs. 13 days). The main death predictors were disseminated intravascular coagulation (DIC) (OR: 13.87) and age (>65 years) (OR: 7.35). Conclusions: Patients older than 65 years who develop DIC have a higher probability of hospital death. Tocilizumab and steroids have been linked to a lower incidence of hospital death, being the main treatment for COVID-19 hospitalized patients with SARS.
Keyphrases
- end stage renal disease
- sars cov
- ejection fraction
- newly diagnosed
- chronic kidney disease
- coronavirus disease
- healthcare
- prognostic factors
- emergency department
- type diabetes
- rheumatoid arthritis
- intensive care unit
- cardiovascular events
- physical activity
- adverse drug
- electronic health record
- acute care
- extracorporeal membrane oxygenation
- respiratory failure
- respiratory syndrome coronavirus
- machine learning