Atypical Symptom Cluster Predicts a Higher Mortality in Patients With First-Time Acute Myocardial Infarction.
- Author:
Seon Young HWANG
1
;
Young Geun AHN
;
Myung Ho JEONG
Author Information
- Publication Type:Original Article
- Keywords: Acute myocardial infarction; Acute coronary syndrome; Symptom; Cluster analysis
- MeSH: Acute Coronary Syndrome; Biomarkers; Chest Pain; Cluster Analysis; Fatigue; Humans; Myocardial Infarction; Regression Analysis; Thorax; Surveys and Questionnaires
- From:Korean Circulation Journal 2012;42(1):16-22
- CountryRepublic of Korea
- Language:English
- Abstract: BACKGROUND AND OBJECTIVES: Identifying symptom clusters of acute myocardial infarction (AMI) and their clinical significance may be useful in guiding treatment seeking behaviors and in planning treatment strategy. The aim of this study was to identify clusters of acute symptoms and their associated factors that manifested in patients with first-time AMI, and to compare clinical outcomes among cluster groups within 1-year of follow-up. SUBJECTS AND METHODS: A total of 391 AMI patients were interviewed individually using a structured questionnaire for acute and associated symptoms between March 2008 and June 2009 in Korea. RESULTS: Among 14 acute symptoms, three distinct clusters were identified by Latent Class Cluster Analysis: typical chest symptom (57.0%), multiple symptom (27.9%), and atypical symptom (15.1%) clusters. The cluster with atypical symptoms was characterized by the least chest pain (3.4%) and moderate frequencies (31-61%) of gastrointestinal symptoms, weakness or fatigue, and shortness of breath; they were more likely to be older, diabetic and to have worse clinical markers at hospital presentation compared with those with other clusters. Cox proportional hazards regression analysis showed that, when age and gender were adjusted for, the atypical symptom cluster significantly predicted a higher risk of 1-year mortality compared to the typical chest pain cluster (hazard ratio 3.288, 95% confidence interval 1.087-9.943, p=0.035). CONCLUSION: Clusters of symptoms can be utilized in guiding a rapid identification of symptom patterns and in detecting higher risk patients. Intensive treatment should be considered for older and diabetic patients with atypical presentation.
