Dataset Information

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

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.

SUBMITTER: Garcia-Olmos L

PROVIDER: S-EPMC6697326 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

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

García-Olmos Luis L Aguilar Río R Lora David D Carmona Montse M Alberquilla Angel A García-Caballero Rebeca R Sánchez-Gómez Luis L

PloS one 20190816 8

<h4>Background</h4>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.<h4>Aim</h4>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.<h4>Design and setting</h4>Cohort study, prospective. Patients treated in family medicine clinics.<h4>Methods</h4 ...[more]

PMID: 31419267

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Project description:BackgroundScalable and safe approaches for heart failure guideline-directed medical therapy (GDMT) optimization are needed.ObjectivesThe authors assessed the safety and effectiveness of a virtual care team guided strategy on GDMT optimization in hospitalized patients with heart failure with reduced ejection fraction (HFrEF).MethodsIn a multicenter implementation trial, we allocated 252 hospital encounters in patients with left ventricular ejection fraction ≤40% to a virtual care team guided strategy (107 encounters among 83 patients) or usual care (145 encounters among 115 patients) across 3 centers in an integrated health system. In the virtual care team group, clinicians received up to 1 daily GDMT optimization suggestion from a physician-pharmacist team. The primary effectiveness outcome was in-hospital change in GDMT optimization score (+2 initiations, +1 dose up-titrations, -1 dose down-titrations, -2 discontinuations summed across classes). In-hospital safety outcomes were adjudicated by an independent clinical events committee.ResultsAmong 252 encounters, the mean age was 69 ± 14 years, 85 (34%) were women, 35 (14%) were Black, and 43 (17%) were Hispanic. The virtual care team strategy significantly improved GDMT optimization scores vs usual care (adjusted difference: +1.2; 95% CI: 0.7-1.8; P < 0.001). New initiations (44% vs 23%; absolute difference: +21%; P = 0.001) and net intensifications (44% vs 24%; absolute difference: +20%; P = 0.002) during hospitalization were higher in the virtual care team group, translating to a number needed to intervene of 5 encounters. Overall, 23 (21%) in the virtual care team group and 40 (28%) in usual care experienced 1 or more adverse events (P = 0.30). Acute kidney injury, bradycardia, hypotension, hyperkalemia, and hospital length of stay were similar between groups.ConclusionsAmong patients hospitalized with HFrEF, a virtual care team guided strategy for GDMT optimization was safe and improved GDMT across multiple hospitals in an integrated health system. Virtual teams represent a centralized and scalable approach to optimize GDMT.

Project description:BackgroundHospitalization for heart failure (HF) is associated with increased risk of death among patients with chronic HF. The degree to which hospitalization for HF is a distinct biologic entity with independent prognostic value versus a marker of higher risk chronic HF patients is unclear.MethodsAfter excluding patients with new-onset HF, the ASCEND-HF trial (Acute Study of Clinical Effectiveness of Nesiritide in Decompensated Heart Failure) included 4205 patients hospitalized for worsening chronic HF with reduced or preserved ejection fraction. The present analysis compared patients by presence or absence of prior HF hospitalization within 12 months and by timing of prior HF hospitalization relative to index hospitalization. Associations with 180-day all-cause mortality were assessed, including adjustment for 27 prespecified clinical factors.ResultsOverall, 2241 (53.3%) patients had a HF hospitalization within the prior 12 months and 1964 (46.7%) did not. Mortality rates at 180 days were 15.5% and 11.9%, respectively. In unadjusted analyses, prior HF hospitalization was associated with increased risk of 180-day mortality (HR, 1.35 [95% CI, 1.14-1.59]; P<0.01). After adjustment, the point estimate was attenuated and the association not statistically significant (HR, 1.18 [95% CI, 0.99-1.40]; P=0.064). Similarly, after adjustment, compared with patients without prior hospitalization, prior HF hospitalization was not associated with mortality, irrespective of timing (0-4 months: HR, 1.10 [95% CI, 0.87-1.39], P=0.41; 4-8 months: HR, 0.95 [95% CI, 0.70-1.27]; P=0.72; 8-12 months: HR, 1.06 [95% CI, 0.74-1.51], P=0.77; >12 months: HR, 0.81 [95% CI, 0.63-1.06], P=0.12).ConclusionsIn this cohort of patients hospitalized for worsening HF, prior HF hospitalization was not associated with 180-day mortality after comprehensively accounting for patient characteristics measured during the index patient visit. Clinical confounders measured at the point-of-care may explain previously observed associations between prior HF hospitalization and mortality, and these clinical factors may be a more direct means of predicting patient survival. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00475852.

Dataset Information

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

Background

Aim

Design and setting

Methods

Results

Conclusion

Publications

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

Similar Datasets

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