Development of a predictive model of hospitalization in primary care patients with heart failure

Luis García-Olmos, Río Aguilar, David Lora, Montse Carmona, Angel Alberquilla, Rebeca García-Caballero, Luis Sánchez-Gómez and the CHIC Group

PLOS ONE, 2019, vol. 14, issue 8, 1-12

Abstract: 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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0221434

DOI: 10.1371/journal.pone.0221434

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