Please use this identifier to cite or link to this item:http://hdl.handle.net/20.500.12105/8708
Development of a predictive model of hospitalization in primary care patients with heart failure
PLoS One. 2019 Aug 16;14(8):e0221434.
BACKGROUND: Heart failure (HF) is the leading cause of hospitalization in people over age 65. Predictive hospital admission models have been developed to help reduce the number of these patients. AIM: To develop and internally validate a model to predict hospital admission in one-year for any non-programmed cause in heart failure patients receiving primary care treatment. DESIGN AND SETTING: Cohort study, prospective. Patients treated in family medicine clinics. METHODS: Logistic regression analysis was used to estimate the association between the predictors and the outcome, i.e. unplanned hospitalization over a 12-month period. The predictive model was built in several steps. The initial examination included a set of 31 predictors. Bootstrapping was used for internal validation. RESULTS: The study included 251 patients, 64 (25.5%) of whom were admitted to hospital for some unplanned cause over the 12 months following their date of inclusion in the study. Four predictive variables of hospitalization were identified: NYHA class III-IV, OR (95% CI) 2.46 (1.23-4.91); diabetes OR (95% CI) 1.94 (1.05-3.58); COPD OR (95% CI) 3.17 (1.45-6.94); MLHFQ Emotional OR (95% CI) 1.07 (1.02-1.12). AUC 0.723; R2N 0.17; Hosmer-Lemeshow 0.815. Internal validation AUC 0.706.; R2N 0.134. CONCLUSION: This is a simple model to predict hospitalization over a 12-month period based on four variables: NYHA functional class, diabetes, COPD and the emotional dimension of the MLHFQ scale. It has an acceptable discriminative capacity enabling the identification of patients at risk of hospitalization.
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