Date Available

4-22-2014

Year of Publication

2014

Degree Name

Doctor of Nursing Practice

Advisor

Dr. Melanie Hardin-Pierce

Committee Member

Dr. Debra Moser

Co-Director of Graduate Studies

Dr. Lacey Buckler

Abstract

Abstract

Objectives:

  1. Determine demographic, physiologic, and laboratory characteristics at time of admission of the heart failure (HF) population in a regional acute care facility in Central Kentucky through review of patient electronic medical records.
  2. Determine which HF population characteristics are significantly associated with readmissions to the hospital.
  3. Provide identification of the statistically significant common characteristics of the HF population to this facility so that they may work towards development of an electronic risk for readmission predictive instrument.

Design: Retrospective chart review.

Setting: Regional acute care facility in Central Kentucky.

Participants: All patients (n = 175) with a diagnosis or history of HF (to include diagnosis related group (DRG) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.1, 428.41, 428.23, 428.43, 428.31, 428.33, 428.1, 428.20, 428.22, 428.30, 428.32, 428.40, 428.40, 428.42, 428.0, and 428.9; The Joint Commission, 2013) admitted to the acute care setting of a regional hospital in the Central Kentucky area between the dates of January 1, 2013 and July 31, 2013. Eligible participants were identified via an electronic discharge report listing all patients discharged during the study time period with a HF code.

Main Outcome Measure: A chart review was performed to define the HF population within the regional acute care facility. Abstracted information was collected on data instruments (Appendices A,B, and C) and analyzed to define the overall HF population (n = 175). The data was then analyzed to determine significance between patient characteristics (demographic, physiologic, and laboratory) and 30 day readmissions. The data was examined both on the individual patient level and independent of patient level looking at each admission independently.

Results: An in depth description of the HF patient population in this facility was obtained. Several patient characteristics including a history of anemia, COPD, ischemic heart disease, diabetes, and the laboratory values creatinine and BNP outside of the reference range were found to have a significant association with 30 day readmissions. Discharge to a skilled nursing facility (SNF) was also found to be a significant predictor of 30 day readmissions. Some social variables such as marital status were not found to have a significant relationship to 30 day readmissions.

Conclusion: This investigation is a stepping stone to creating an electronic tool designed to reflect the characteristics of HF population admitted to a single facility and predict risk of HF readmissions within 30 days at the time of admission. Implementation of a plan of care designed to meet the needs of this HF population as well as identify those patients at high risk for will allow for provision of a comprehensive and timely individualized plan of care to reduce the incidence of 30 day readmissions.

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