Mortality prediction model using data from the Hospital Information System

• Autor(a) principal: Gomes, Andréa Silveira
• Data de Publicação: 2010
• Outros Autores: Klück, Mariza Machado, Riboldi, João, Fachel, Jandyra Maria Guimarães
• Tipo de documento: Artigo
• Idioma: por; eng
• Título da fonte: Revista de Saúde Pública
• Texto Completo: https://www.revistas.usp.br/rsp/article/view/32848
• topic: Mortalidade Hospitalar; Sistemas de Informação Hospitalar^i1^sutiliza; Modelos Logísticos; Avaliação de Resultados^i1^sCuidados de Sa; Estudos Transversais; Mortalidad Hospitalaria; Sistemas de Información en Hospital^i3^sutilizac; Modelos Logísticos; Evaluación de Resultado^i3^sAtención de Sa; Estudios Transversales; Hospital Mortality; Hospital Information Systems^i2^sutilizat; Logistic Models; Outcome Assessment^i2^sHealth C; Cross-Sectional Studies

OBJECTIVE: To develop a hospital mortality prediction model based on data from the Hospital Information System of the Brazilian National Health System. METHODS: This was a cross-sectional study using data from 453,515 authorizations for hospital admission relating to 332 hospitals in Rio Grande do Sul, Southern Brazil in the year 2005. From the ratio between observed and expected deaths, the hospitals were ranked in an adjusted manner, and this was compared with the crude ranking of the mortality rate. Logistic regression was used to develop a predictive model for the likelihood of hospital mortality according to sex, age, diagnosis and use of an intensive care unit. Confidence intervals (95%) were obtained for the 206 hospitals with more than 365 hospital admissions per year. RESULTS: An index for the risk of hospital mortality was obtained. Ranking the hospitals using only the crude mortality rate differed from the ranking when it was adjusted according to the predictive likelihood model. Among the 206 hospitals analyzed, 40 of them presented observed mortality that was significantly greater than what was expected, while 58 hospitals presented mortality that was significantly lower than expected. Use of an intensive care unit presented the greatest weight in making up the risk index, followed by age and diagnosis. When the hospitals attended patients with widely differing profiles, the risk adjustment did not result in a definitive indication regarding which provider was best. Among this group of hospitals, those of large size presented greater numbers of deaths than would be expected from the characteristics of the hospital admissions. CONCLUSIONS: The hospital mortality risk index was shown to be an appropriate predictor for calculating the expected death rate, and it can be applied to evaluate hospital performance. It is recommended that, in comparing hospitals, the adjustment using the predictive likelihood model for the risk should be used, with stratification according to hospital size.