OBJECTIVES: To develop and validate a model for predicting the risk of hospital admission within 1 year in the HIV population under antiretroviral treatment. METHODS: We conducted a retrospective observational study. Patients receiving antiretroviral treatment for at least 1 year who were followed by the pharmacy service in a Spanish-speaking hospital between January 2008 and December 2012 were included. Demographics, and clinical and pharmacotherapy variables, were included in the model design. To find prognostic factors for hospital admission a multivariate logistic regression model was created after performing a univariate analysis. Model validity was determined by the shrinkage method and the model discrimination by Harrell's C-index. RESULTS: 442 patients were included in the study. The variables 'CD4 count <200 (cells/µL)', 'drug/alcohol use', 'detectable viral load (>50 copies/mL)', 'number of previous admissions', and 'number of drugs different from antiretroviral treatment' were the independent predictors of risk of hospital admission. Probabilities predicted by the model showed an R2=0.98 for the development sample and an R2=0.86 for the validation sample. The Harrell's C index for the development and validation data were 0.82 (95% CI 0.77 to 0.87) and 0.80 (95% CI 0.73 to 0.88), respectively. CONCLUSIONS: The model developed in this study may be useful in daily practice for identifying HIV patients at high risk of 1-year hospital admission.
OBJECTIVES: To develop and validate a model for predicting the risk of hospital admission within 1 year in the HIV population under antiretroviral treatment. METHODS: We conducted a retrospective observational study. Patients receiving antiretroviral treatment for at least 1 year who were followed by the pharmacy service in a Spanish-speaking hospital between January 2008 and December 2012 were included. Demographics, and clinical and pharmacotherapy variables, were included in the model design. To find prognostic factors for hospital admission a multivariate logistic regression model was created after performing a univariate analysis. Model validity was determined by the shrinkage method and the model discrimination by Harrell's C-index. RESULTS: 442 patients were included in the study. The variables 'CD4 count <200 (cells/µL)', 'drug/alcohol use', 'detectable viral load (>50 copies/mL)', 'number of previous admissions', and 'number of drugs different from antiretroviral treatment' were the independent predictors of risk of hospital admission. Probabilities predicted by the model showed an R2=0.98 for the development sample and an R2=0.86 for the validation sample. The Harrell's C index for the development and validation data were 0.82 (95% CI 0.77 to 0.87) and 0.80 (95% CI 0.73 to 0.88), respectively. CONCLUSIONS: The model developed in this study may be useful in daily practice for identifying HIV patients at high risk of 1-year hospital admission.
Entities:
Keywords:
HIV; antiretroviral treatment; hospital admission; pharmacy; predictive model
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