Comorbidity adjustment index for the international classification of diseases, 10

• Autor(a) principal: Ramiarina, Robert Antonio
• Data de Publicação: 2008
• Outros Autores: Ramiarina, Beatriz Luiza, Almeida, Renan Moritz V R, Pereira, Wagner Coelho de Albuquerque
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista de Saúde Pública
• Texto Completo: https://www.revistas.usp.br/rsp/article/view/32472
• topic: Comorbidade; Classificação Internacional de Doenças; Mortalidade Hospitalar; Registros Médicos; Modelos Estatísticos; Tábuas de Vida; Modelos Epidemiológicos; Modelos Matemáticos; Comorbilidad; Clasificación Internacional de Enfermedades; Mortalidad Hospitalaria; Historia Clínica del Paciente; Modelos Estadísticos; Tabla de Vida; Modelos Epidemiológicos; Modelos Matemáticos; Comorbidity; International Classification of Diseases; Hospital Mortality; Medical Records; Models; Statistic; Life Tables; Epidemiological Models; Mathematic Models

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >;1, and their mortality rate was 4.5% (66.0% of them had scores >;1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.