Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods.
Catherine J Ryan, Karen M Vuckovic, Lorna Finnegan and 6 others
PMID 30667327WHAT IT FOUND
Emergency department patients with acute coronary syndrome grouped into different symptom clusters depending on how researchers sorted them.
Chest, gastrointestinal, and heavy symptom patterns appeared, but no single cluster list was settled.
Key findings
01One symptom-grouping method found four types: chest symptoms, exertion-like symptoms, non-chest pain symptoms, and gastrointestinal symptoms.
02When patients were grouped by their own symptom patterns, most methods found three clusters, but the clusters were not identical: chest symptoms appeared in all, while low symptom burden, heavy symptom burden, and classic symptoms appeared in different methods.
03When age, race, and sex were added, the patient-grouping method found four clusters: chest symptoms in 40% of patients, heavy symptom burden in 25%, classic symptoms in 19%, and low symptom burden in 15%.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
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What it does not show
The analysis used existing emergency department symptom data and was designed to compare statistical methods, not to test a clinical decision rule or outcome. Different methods gave different cluster numbers and labels, so no single symptom cluster solution was established. Symptoms were recorded only once, within 15 minutes of emergency department arrival, and only as present or absent, so severity, timing, and symptom change were not captured. The sample included 474 patients from five US emergency departments and was mostly White, non-Hispanic males aged 50 to 60 years, so results may not apply to other populations. Choosing the number of clusters involved statistical programs and researcher judgment, and adding age, race, and sex changed the latent class result.
Declared interests
The authors declared no potential conflicts of interest. The publication type lists NIH extramural research support, but the text does not describe the funding role.
The easy way to misread this
Do not read these clusters as a validated way to triage or diagnose acute coronary syndrome. The paper compared statistical grouping methods on existing emergency department data and did not test whether any cluster predicted outcomes or improved patient care.