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

• Published in: PloS one Vol. 14; no. 8; p. e0221434
• Main Authors: García-Olmos, Luis, Aguilar, Río, Lora, David, Carmona, Montse, Alberquilla, Angel, García-Caballero, Rebeca, Sánchez-Gómez, Luis
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16-08-2019; Public Library of Science (PLoS)
• Subjects: Aged; Analysis; Biology and Life Sciences; Cardiology; Care and treatment; Chronic illnesses; Chronic obstructive lung disease; Chronic obstructive pulmonary disease; Clinical trials; Comorbidity; Congestive heart failure; Diabetes; Diabetes mellitus; Emotions; Family medicine; Female; Health care; Health services; Heart; Heart diseases; Heart failure; Heart Failure - physiopathology; Heart Failure - therapy; Hospital admission and discharge; Hospitalization; Humans; Male; Medical research; Medicine and Health Sciences; Middle Aged; Models, Cardiovascular; Mortality; Older people; Patients; Physical Sciences; Population; Prediction models; Primary care; Primary Health Care; Prospective Studies; Questionnaires; Regression analysis; Research and Analysis Methods; Risk Factors; Social Sciences; Studies; Teaching hospitals; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0221434

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.
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.
Cohort study, prospective. Patients treated in family medicine clinics.
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.
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.
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.