Date of Conferral

2022

Degree

Ph.D.

School

Public Health

Advisor

Eboni Green

Abstract

The development and validation of 30-day readmission risk prediction models, such as the LACE index, have been of interest to researchers and healthcare organizations, especially since the Centers for Medicare and Medicaid Services began to impose monetary penalties on hospitals with higher than expected 30-day readmission rates. However, there is a lack of consensus concerning the efficacy of the LACE index in the heart failure population. The purpose of the study was to examine the discriminative accuracy of the LACE index to predict all-cause 30-day readmission for heart failure patients and to build a modified 30-day readmission risk prediction model based on variables known to influence the risk of readmission. Andersen’s behavioral model of health services was used to understand how the patient level variables of interest contributed to readmission risk as predisposing, enabling, and need factors. Using a correlational study design, quantitative data were retrospectively collected from the electronic medical records (n=655) of heart failure patients. The Receiver Operating Characteristic curve and simple binary logistic regression were used to answer the research questions. The results of the analyses revealed that both the LACE index and the modified risk prediction model had poor discriminatory accuracy for predicting the risk of 30-day readmission for the sample population. The results of the simple binary logistic regression indicated several of the independent variables were statistically significantly associated with all-cause 30-day readmission. Healthcare facilities operate with limited resources, and the identification of patients at a higher risk for 30-day readmission would allow healthcare professionals to initiate preventive measures before and after discharge.

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