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Cluster dyads of risk factors and symptoms are associated with major adverse cardiac events in patients with acute myocardial infarction

Authors

  • Seon Young Hwang RN PhD,

    Associate Professor
    1. College of Nursing, Hanyang University, Seoul, South Korea
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  • JinShil Kim RN PhD

    Associate Professor, Corresponding author
    1. College of Nursing, Gachon University, Incheon, South Korea
    • Correspondence: JinShil Kim, PhD, RN, Associate Professor, Gachon University, College of Nursing, 191 Hambakmoero, Yeonsu-gu, Incheon, South Korea; telephone (82) 032-820-4229; fax (82) 032-820-4201; Email: blushil68@gmail.com

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Abstract

The purpose of this study was to examine the cluster dyads of risk factors and symptoms and their impact on the incidence of 12 month major adverse cardiac events (MACEs) among patients with first-time myocardial infarction (MI). In a descriptive study, a total of 522 patients completed semi-structured interviews for data on risk factors and symptoms. Patients were followed for 12 months to determine MACEs. Latent class cluster analysis was performed to identify risk factor clusters and symptom clusters. Logistic regression analysis was performed to determine the impact of cluster dyads on 12 month MACEs. There were 436 event-free survivors and 86 patients with MACEs for 12 months. Ten risk factors and 14 symptoms were clustered into two (dyslipidemia/smoking, hypertension/diabetes dominant) and three (typical, multiple, atypical) memberships, respectively. Six cluster dyads which were generated based on the association between risk factors and symptom clusters were a significant predictor of 12 month MACEs, with the incidence occurring three times higher in a dyad of hypertension/diabetes-and-atypical symptoms than a dyad of dyslipidemia/smoking-and-typical symptoms (odds ratio = 3.10, P = 0.01), after adjustment for age, gender and a type of MI diagnosis. The information on cluster dyads suggests that health-care providers need to consider both risk factors and symptoms at hospital presentation for risk stratification to prevent adverse outcomes.

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