| Revista: | Revista de saude publica |
| Base de datos: | |
| Número de sistema: | 000535638 |
| ISSN: | 0034-8910 |
| Autores: | Couto, Renato Camargos1 Pedrosa, Tania Moreira Grillo1 Seara, Luciana Moreira2 Couto, Carolina Seara3 Couto, Vitor Seara2 Giacomin, Karla4 Abreu, Ana Claudia Couto de2 |
| Instituciones: | 1Fundação Lucas Machado, Faculdade de Ciências Médicas de Minas Gerais, Belo Horizonte, Minas Gerais. Brasil 2Instituto de Acreditação e Gestão em Saúde, Departamento de Tecnologia da Informação, Belo Horizonte, Minas Gerais. Brasil 3Instituto de Assistência Médica ao Servidor Público Estadual, Hospital do Servidor Público Estadual, São Paulo. Brasil 4Centro Internacional de Longevidade, Belo Horizonte, Minas Gerais. Brasil |
| Año: | 2022 |
| Volumen: | 56 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | OBJECTIVE Defining priority vaccination groups is a critical factor to reduce mortality rates. METHODS We sought to identify priority population groups for covid-19 vaccination, based on in-hospital risk of death, by using Extreme Gradient Boosting Machine Learning (ML) algorithm. We performed a retrospective cohort study comprising 49,197 patients (18 years or older), with RT-PCR-confirmed for covid-19, who were hospitalized in any of the 336 Brazilian hospitals considered in this study, from March 19th, 2020, to March 22nd, 2021. Independent variables encompassed age, sex, and chronic health conditions grouped into 179 large categories. Primary outcome was hospital discharge or in-hospital death. Priority population groups for vaccination were formed based on the different levels of in-hospital risk of death due to covid-19, from the ML model developed by taking into consideration the independent variables. All analysis were carried out in Python programming language (version 3.7) and R programming language (version 4.05). RESULTS Patients’ mean age was of 60.5 ± 16.8 years (mean ± SD), mean in-hospital mortality rate was 17.9%, and the mean number of comorbidities per patient was 1.97 ± 1.85 (mean ± SD). The predictive model of in-hospital death presented area under the Receiver Operating Characteristic Curve (AUC - ROC) equal to 0.80. The investigated population was grouped into eleven (11) different risk categories, based on the variables chosen by the ML model developed in this study. CONCLUSIONS The use of ML for defining population priorities groups for vaccination, based on risk of in-hospital death, can be easily applied by health system managers |
| Disciplinas: | Medicina, Medicina |
| Palabras clave: | Inmunología, Salud pública |
| Keyword: | Immunology, Public health |
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