Risk Factors for Mortality from COVID-19 in Hospitalized Patients: A Logistic Regression Model
Introduction: The population is susceptible to COVID-19 and knowing the most predominant characteristics and comorbidities of those affected is essential to diminish its effects. Objective: This study analyzed the biological, social and clinical risk factors for mortality in hospitalized patients wi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | Spanish |
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Universidad Ricardo Palma
2021-01-01
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| Series: | Revista de la Facultad de Medicina Humana |
| Subjects: | |
| Online Access: | http://revistas.urp.edu.pe/index.php/RFMH/article/view/3264 |
| Summary: | Introduction: The population is susceptible to COVID-19 and knowing the most predominant characteristics and comorbidities of those affected is essential to diminish its effects. Objective: This study analyzed the biological, social and clinical risk factors for mortality in hospitalized patients with COVID-19 in the district of Trujillo, Peru. Methods: A descriptive type of study was made, with a quantitative approach and a correlational, retrospective, cross-sectional design. Data was obtained from the Ministry of Health’s database, with a sample of 64 patients from March to May 2020. Results: 85,71% of the total deceased are male, the most predominant occupation is Retired with an 28,57% incidence, and an average age of 64,67 years. When it came to symptoms of deceased patients, respiratory distress represents the highest percentage of incidence with 90,48%, then fever with 80,95%, followed by malaise in general with 57,14% and cough with 52,38%. The signs that indicated the highest percentage in deaths were dyspnea and abnormal pulmonary auscultation with 47,62%, in Comorbidities patients with cardiovascular disease were found in 42,86% and 14,29% with diabetes. The logistic regression model to predict mortality in hospitalized patients allowed the selection of risk factors such as age, sex, cough, shortness of breath and diabetes. Conclusion: The model is adequate to establish these factors, since they show that a fairly considerable percentage of explained variation would correctly classify 90,6% of the cases. |
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| ISSN: | 1814-5469 2308-0531 |