title: Symptom clusters in acute myocardial infarction: a secondary data analysis.
abstract: BACKGROUND
  Early recognition of acute myocardial infarction (AMI) symptoms and reduced time to treatment may reduce morbidity and mortality. People having AMI experience a constellation of symptoms, but the common constellations or clusters of symptoms have yet to be identified.
  
  
  OBJECTIVE
  To identify clusters of symptoms that represent AMI.
  
  
  METHODS
  This was a secondary data analysis of nine descriptive, cross-sectional studies that included data from 1,073 people having AMI in the United States and England. Data were analyzed using latent class cluster analysis, an a theoretical method that uses only information contained in the data.
  
  
  RESULTS
  Five distinct clusters of symptoms were identified. Age, race, and sex were statistically significant in predicting cluster membership. None of the symptom clusters described in this analysis included all of the symptoms that are considered typical. In one cluster, subjects had only a moderate to low probability of experiencing any of the symptoms analyzed.
  
  
  CONCLUSIONS
  Symptoms of AMI occur in clusters, and these clusters vary among persons. None of the clusters identified in this study included all of the symptoms that are included typically as symptoms of AMI (chest discomfort, diaphoresis, shortness of breath, nausea, and lightheadedness). These AMI symptom clusters must be communicated clearly to the public in a way that will assist them in assessing their symptoms more efficiently and will guide their treatment-seeking behavior. Symptom clusters for AMI must also be communicated to the professional community in a way that will facilitate assessment and rapid intervention for AMI.
authors.affiliation: College of Nursing, University of Illinois at Chicago, Chicago, IL 60612, USA. cryan4@uic.edu
authors.forename: Catherine J
authors.initials: CJ
authors.lastname: Ryan
authors.forename: Holli A
authors.initials: HA
authors.lastname: DeVon
authors.forename: Rob
authors.initials: R
authors.lastname: Horne
authors.forename: Kathleen B
authors.initials: KB
authors.lastname: King
authors.forename: Kerry
authors.initials: K
authors.lastname: Milner
authors.forename: Debra K
authors.initials: DK
authors.lastname: Moser
authors.forename: Jill R
authors.initials: JR
authors.lastname: Quinn
authors.forename: Anne
authors.initials: A
authors.lastname: Rosenfeld
authors.forename: Seon Young
authors.initials: SY
authors.lastname: Hwang
authors.forename: Julie J
authors.initials: JJ
authors.lastname: Zerwic
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D004417:Dyspnea / Q000209:etiology; D042241:Early Diagnosis; D004739:England / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000150:complications / Q000175:diagnosis / Q000453:epidemiology; D009325:Nausea / Q000209:etiology; D010146:Pain / Q000209:etiology; D010342:Patient Acceptance of Health Care; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D017678:Sex Distribution; D013546:Sweating; D013997:Time Factors; D014481:United States / Q000453:epidemiology