Classifying subgroups of patients with symptoms of acute coronary syndromes: A cluster analysis.

The purpose of the study was to identify subgroups of patients presenting with acute coronary syndromes based on symptom clusters. Two hundred fifty-six patients completed a symptom assessment in their hospital rooms. Latent class cluster analysis and analysis of variance were used to classify subgroups of patients according to selected clinical characteristics. Four subgroups were identified and labeled as Heavy Symptom Burden, Chest Pain Only, Sweating and Weak, and Short of Breath and Weak (model fit χ(2) [130,891, n = 256] = 867.5, p = 1.00). The largest group of patients experienced classic symptoms of chest pain and shortness of breath but not sweating. Younger patients were more likely to cluster in the Heavy Symptom Burden group (F = 5.08, p = .002). Interpretation of the clinical significance of these groupings requires further study.

[1]  H. DeVon,et al.  The Symptoms of Unstable Angina: Do Women and Men Differ? , 2003, Nursing research.

[2]  B. Riegel,et al.  Acute coronary syndrome: what do patients know? , 2008, Archives of internal medicine.

[3]  K. Petrie,et al.  Patients' interpretation of symptoms as a cause of delay in reaching hospital during acute myocardial infarction , 2000, Heart.

[4]  B. Darney,et al.  Symptoms of acute coronary syndrome in women with diabetes: an integrative review of the literature. , 2008, Heart & lung : the journal of critical care.

[5]  S. Rankin,et al.  The Effects of a Collaborative Peer Advisor/Advanced Practice Nurse Intervention: Cardiac Rehabilitation Participation and Rehospitalization in Older Adults After a Cardiac Event , 2007, The Journal of cardiovascular nursing.

[6]  L. Tulman,et al.  Symptom Clusters: Concept Analysis and Clinical Implications for Cancer Nursing , 2005, Cancer nursing.

[7]  H. DeVon,et al.  Treatment Seeking for Acute Myocardial Infarction Symptoms: Differences in Delay Across Sex and Race , 2003, Nursing research.

[8]  D. Modai,et al.  Patient-dependent variables affecting treatment and prediction of acute coronary syndrome are age-related. A study performed in Israel. , 2007, International journal of cardiology.

[9]  Linda M. Collins,et al.  Latent class and latent transition analysis , 2009 .

[10]  D. Moser,et al.  Women's decision to seek care for symptoms of acute myocardial infarction. , 1995, Heart & lung : the journal of critical care.

[11]  S. Noureddine,et al.  Response to signs and symptoms of acute coronary syndrome: differences between Lebanese men and women. , 2008, American journal of critical care : an official publication, American Association of Critical-Care Nurses.

[12]  S. Reis,et al.  The Economic Burden of Angina in Women With Suspected Ischemic Heart Disease: Results From the National Institutes of Health–National Heart, Lung, and Blood Institute–Sponsored Women’s Ischemia Syndrome Evaluation , 2006, Circulation.

[13]  M. Lynn Determination and quantification of content validity. , 1986, Nursing research.

[14]  Kathryn A. Lee,et al.  Advancing the science of symptom management. , 2001, Journal of advanced nursing.

[15]  M. Dodd,et al.  Conceptual issues in symptom clusters research and their implications for quality-of-life assessment in patients with cancer. , 2007, Journal of the National Cancer Institute. Monographs.

[16]  D. Eterović,et al.  Symptom presentation of acute myocardial infarction: influence of sex, age, and risk factors. , 2002, American heart journal.

[17]  Á. Avezum,et al.  Practice variation and missed opportunities for reperfusion in ST-segment-elevation myocardial infarction: findings from the Global Registry of Acute Coronary Events (GRACE) , 2002, The Lancet.

[18]  S. Rankin,et al.  Comparing Interventions in Older Unpartnered Adults after Myocardial Infarction , 2006, European journal of cardiovascular nursing : journal of the Working Group on Cardiovascular Nursing of the European Society of Cardiology.

[19]  B. Riegel,et al.  Symptom recognition in elders with heart failure. , 2010, Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing.

[20]  D. Moser,et al.  Symptom Clusters in Acute Myocardial Infarction: A Secondary Data Analysis , 2007, Nursing research.

[21]  B. Riegel,et al.  A Nursing Intervention to Reduce Prehospital Delay in Acute Coronary Syndrome: A Randomized Clinical Trial , 2006, The Journal of cardiovascular nursing.

[22]  Brian Everitt,et al.  Cluster analysis , 1974 .

[23]  W. Gibler,et al.  Initial risk stratification and presenting characteristics of patients with evolving myocardial infarctions , 2008, Emergency Medicine Journal.

[24]  Adrienne Nishina,et al.  Subtypes, severity, and structural stability of peer victimization: what does latent class analysis say? , 2007, Child development.

[25]  J. Zerwic Symptoms of acute myocardial infarction: expectations of a community sample. , 1998, Heart & lung : the journal of critical care.

[26]  V. Vaccarino,et al.  Gender and age differences in chief complaints of acute myocardial infarction (Worcester Heart Attack Study). , 2004, The American journal of cardiology.

[27]  Catherine J Ryan,et al.  Symptoms across the continuum of acute coronary syndromes: differences between women and men. , 2008, American journal of critical care : an official publication, American Association of Critical-Care Nurses.

[28]  C. Yeh,et al.  Symptom clustering in older Taiwanese children with cancer. , 2008, Oncology nursing forum.

[29]  J. Zerwic,et al.  Perceptions of Symptoms of Myocardial Infarction Related to Health Care Seeking Behaviors in the Elderly , 2003, The Journal of cardiovascular nursing.

[30]  J. Hagenaars,et al.  Applied Latent Class Analysis , 2003 .

[31]  Christine Miaskowski,et al.  Occurrence of symptom clusters. , 2004, Journal of the National Cancer Institute. Monographs.

[32]  B. Muthén,et al.  Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study , 2007 .

[33]  J P Ornato,et al.  Prevalence, clinical characteristics, and mortality among patients with myocardial infarction presenting without chest pain. , 2000, JAMA.

[34]  Bradley E Aouizerat,et al.  Subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: a cluster analysis. , 2006, Oncology nursing forum.

[35]  C. Gwede,et al.  Exploring the differential experience of breast cancer treatment-related symptoms: a cluster analytic approach , 2008, Supportive Care in Cancer.

[36]  J. McSweeney,et al.  Challenging the rules: women's prodromal and acute symptoms of myocardial infarction. , 2000, Research in nursing & health.

[37]  Yoshimi Fukuoka,et al.  Cluster analysis: a useful technique to identify elderly cardiac patients at risk for poor quality of life , 2007, Quality of Life Research.

[38]  L. Collins,et al.  Latent Class Models for Stage-Sequential Dynamic Latent Variables , 1992 .

[39]  Diane Carroll,et al.  Cluster Analysis of Elderly Cardiac Patients' Prehospital Symptomatology , 2008, Nursing research.