ArticleResearch in nursing & health2010
Classifying subgroups of patients with symptoms of acute coronary syndromes: A cluster analysis.
Article in Research in nursing & health, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Symptom clusters and unplanned hospital readmission in Chinese patients with acute myocardial infarction on admission.Frontiers in cardiovascular medicine · 2024Article
- Characteristic differences of chest pain in male and female patients with acute coronary syndrome: A pilot study.Journal of public health research · 2021Article
- Chest pain symptoms differences between diabetes mellitus and non-diabetes mellitus patients with acute coronary syndrome: A pilot study.Journal of public health research · 2021Article
- Prevalence and Predictors of Delay in Seeking Emergency Care in Patients Who Call 9-1-1 for Chest Pain.The Journal of emergency medicine · 2019Observational
- Protein Cytokines, Cytokine Gene Polymorphisms, and Potential Acute Coronary Syndrome Symptoms.Biological research for nursing · 2019Article
- The Quality of Symptoms in Women and Men Presenting to the Emergency Department With Suspected Acute Coronary Syndrome.Journal of emergency nursing · 2019Article
- Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods.Western journal of nursing research · 2019Article
- Patient-reported symptoms improve prediction of acute coronary syndrome in the emergency department.Research in nursing & health · 2018Article
- Symptom clusters and treatment time delay in Korean patients with ST-elevation myocardial infarction on admission.Medicine · 2018Observational
- Impact of comorbidities by age on symptom presentation for suspected acute coronary syndromes in the emergency department.European journal of cardiovascular nursing · 2017Article
- Gender Differences in Factors Related to Prehospital Delay in Patients with ST-Segment Elevation Myocardial Infarction.Yonsei medical journal · 2017Article
- Reliability of the McSweeney Acute and Prodromal Myocardial Infarction Symptom Survey among black and white women.European journal of cardiovascular nursing · 2013Article
- Multivariate modeling to identify patterns in clinical data: the example of chest pain.BMC medical research methodology · 2011Article
- Latent variable mixture modeling: a flexible statistical approach for identifying and classifying heterogeneity.Nursing researchArticle
- Symptom Trajectories After an Emergency Department Visit for Potential Acute Coronary Syndrome.Nursing researchObservational
- Symptom clusters in patients presenting to the emergency department with possible acute coronary syndrome differ by sex, age, and discharge diagnosis.Heart & lung : the journal of critical careArticle
- Disparities in patients presenting to the emergency department with potential acute coronary syndrome: it matters if you are Black or White.Heart & lung : the journal of critical careArticle
- Symptoms of Acute Myocardial Infarction as Described in Calls to Tele-Nurses and in Questionnaires: A Mixed-Methods Study.The Journal of cardiovascular nursingArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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