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. 2019 Jan-Feb;13(1):1557988318814295.
doi: 10.1177/1557988318814295. Epub 2018 Nov 28.

Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure

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Impact of a Pharmacist-Led Intervention on 30-Day Readmission and Assessment of Factors Predictive of Readmission in African American Men With Heart Failure

DeAngelo McKinley et al. Am J Mens Health. 2019 Jan-Feb.

Abstract

Heart failure (HF) is responsible for more 30-day readmissions than any other condition. Minorities, particularly African American males (AAM), are at much higher risk for readmission than the general population. In this study, demographic, social, and clinical data were collected from the electronic medical records of 132 AAM patients (control and intervention) admitted with a primary or secondary admission diagnosis of HF. Both groups received guideline-directed therapy for HF. Additionally the intervention group received a pharmacist-led intervention. Data collected from these patients were used to develop and validate a predictive model to evaluate the impact of the pharmacist-led intervention, and identify predictors of readmission in this population. After propensity score matching, the intervention was determined to have a significant impact on readmission, as a significantly smaller proportion of patients in the intervention group were readmitted as compared to the control group (11.5% vs. 42.9%; p = .03). A predictive model for 30-day readmission was developed using K-nearest neighbor (KNN) classification algorithm. The model was able to correctly classify about 71% patients with an AUROC of 0.70. Additionally, the model provided a set of key patient attributes predictive of readmission status. Among these predictive attributes was whether or not a patient received the intervention. A relative risk analysis identified that patients who received the intervention are less likely to be readmitted within 30 days. This study demonstrated the benefit of a pharmacist-led intervention for AAM with HF. Such interventions have the potential to improve quality of life for this patient population.

Keywords: heart failure; machine learning; pharmacist intervention; predictive modeling; readmission.

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Conflict of interest statement

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Distribution of propensity scores before (left panel) and after (right panel) trimming the dataset. Light gray region represents overlapping area of propensity scores.
Figure 2.
Figure 2.
Percent readmission in the control and the intervention group for the unadjusted and adjusted analysis. *Significance at the alpha = .05 level.
Figure 3.
Figure 3.
PSM-adjusted readmission risk by attribute. *Statistical significance at the α = .05 level.

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