Factors affecting the unplanned hospital readmission of elderly patients with cardiovascular disease - A predictive model

Objective: This study focused on the effects of changes to patient medicati on regimens, adverse drug reactions, abnormal laboratory results during hos pital stay, and discharge diagnosis on unplanned readmission rates of elder ly patients with cardiovascular disease. The objective of the study was to identify which variables predict non-elective hospital readmission. Design: A retrospective cohort design was used with random review of medica l charts of patients admitted to the study-site hospital over a 6-month per iod. Setting: Antrim Area Hospital, a 385-bed hospital located in a rural settin g. Patients: 100 elderly cardiology patients (47 male, 53 female, average age 75 +/- 6 years) with a non-elective admission to the study-site hospital. Main Outcome Measures: Non-elective readmission rates to the study-site hos pital during the 12 months after discharge. Results: An unplanned readmission rate of 32% 1 year post hospital discharg e was noted, with mean length of time to first readmission of 40.7 days. Ch i-squared analysis revealed statistically significant relationships between patients being readmitted one or more times within a 12-month period and t he following variables: acute coronary insufficiency as a primary diagnosis (p = 0.009, odds ratio = 0.32), patients discharged on a calcium-channel b locker (p = 0.023, odds ratio = 3.18), one or more changes in strength/dose of drugs during hospital stay (p = 0.028, odds ratio = 0.38), and patient discharged on a potassium-channel activator (p = 0.034, odds ratio = 2.72). Multivariate logistic regression analysis produced a five-variable model p redictive of hospital readmissions with a specificity of 84.1%, sensitivity of 61.3% and accuracy of 77.0%. Conclusion: The present research highlights a number of risk factors that a re associated with multiple hospital admissions of elderly patients with ca rdiovascular disease. This knowledge should be of use to medical staff when identifying patients in need of intensive discharge planning.