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Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods.

ABSTRACT: Researchers have employed various methods to identify symptom clusters in cardiovascular conditions, without identifying rationale. Here, we test clustering techniques and outcomes using a data set from patients with acute coronary syndrome. A total of 474 patients who presented to emergency departments in five United States regions were enrolled. Symptoms were assessed within 15 min of presentation using the validated 13-item ACS Symptom Checklist. Three variable-centered approaches resulted in four-factor solutions. Two of three person-centered approaches resulted in three-cluster solutions. K-means cluster analysis revealed a six-cluster solution but was reduced to three clusters following cluster plot analysis. The number of symptoms and patient characteristics varied within clusters. Based on our findings, we recommend using (a) a variable-centered approach if the research is exploratory, (b) a confirmatory factor analysis if there is a hypothesis about symptom clusters, and (c) a person-centered approach if the aim is to cluster symptoms by individual groups.

SUBMITTER: Ryan CJ

PROVIDER: S-EPMC6570558 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods.

Ryan Catherine J CJ Vuckovic Karen M KM Finnegan Lorna L Park Chang G CG Zimmerman Lani L Pozehl Bunny B Schulz Paula P Barnason Susan S DeVon Holli A HA

Western journal of nursing research 20190122 7

Researchers have employed various methods to identify symptom clusters in cardiovascular conditions, without identifying rationale. Here, we test clustering techniques and outcomes using a data set from patients with acute coronary syndrome. A total of 474 patients who presented to emergency departments in five United States regions were enrolled. Symptoms were assessed within 15 min of presentation using the validated 13-item ACS Symptom Checklist. Three variable-centered approaches resulted in ...[more]

PMID: 30667327

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Project description:Many patients evaluated for acute coronary syndrome (ACS) in emergency departments (EDs) continue to experience troubling symptoms after discharge-regardless of their ultimate medical diagnosis. However, comprehensive understanding of common post-ED symptom trajectories is lacking.The aim of this study was to identify common trajectories of symptom severity in the 6 months after an ED visit for potential ACS.This was a secondary analysis of data from a larger observational, prospective study conducted in five U.S. EDs. Patients (N = 1005) who had electrocardiogram and biomarker testing ordered, and were identified by the triage nurse as potentially having ACS, were enrolled. Symptom severity was assessed in the hospital after initial stabilization and by telephone at 30 days and 6 months using the validated 13-item ACS Symptom Checklist. Growth mixture modeling was used for the secondary analysis. The eight most commonly reported symptoms (chest discomfort, chest pain, chest pressure, light-headedness, shortness of breath, shoulder pain, unusual fatigue, and upper back pain) were modeled across the three study time points. Models with increasing numbers of classes were compared, and final model selection was based on a combination of interpretability, theoretical justification, and statistical fit indices.The sample was 62.6% male with a mean age of 60.2 years (SD = 14.17 years), and 57.1% ruled out for ACS. Between two and four distinct trajectory classes were identified for each symptom. The seven different types of trajectories identified across the eight symptoms were labeled "tapering off," "mild/persistent," "moderate/persistent," "moderate/worsening," "moderate/improving," "late onset, "and "severe/improving." Trajectories differed on age, gender, and diagnosis.Research on the individual nature of symptom trajectories can contribute to patient-centered, rather than disease-centered, care. Further research is needed to verify the existence of multiple symptoms trajectories in diverse populations and to assess the antecedents and consequences of individual symptom trajectories.

Project description:BackgroundFollowing the 2013-2016 West African Ebola outbreak, distinct, persistent health complaints were recognized in Ebola virus disease (EVD) survivors. Here we provide an in-depth characterization of post-Ebola syndrome >2.5 years after resolution of disease. Additionally, we report subphenotypes of post-Ebola syndrome with overlapping symptom clusters in survivors from Eastern Sierra Leone.MethodsParticipants in Eastern Sierra Leone were identiﬁed by the Sierra Leone Association of Ebola survivors. Survivors and their contacts were administered a questionnaire assessing self-reported symptoms and a physical examination. Comparisons between survivors and contacts were conducted using conditional logistic regression. Symptom groupings were identified using hierarchical clustering approaches. Simplified presentation of incredibly complex evaluations (SPICE), correlation analysis, logistic regression, and principal component analysis (PCA) were performed to explore the relationships between symptom clusters.ResultsThree hundred seventy-five EVD survivors and 1040 contacts were enrolled into the study. At enrollment, EVD survivors reported signiﬁcantly more symptoms than their contacts in all categories (P < .001). Symptom clusters representing distinct organ systems were identified. Correlation and logistic regression analysis identified relationships between symptom clusters, including stronger relationships between clusters including musculoskeletal symptoms (r = 0.63, P < .001; and P < .001 for correlation and logistic regression, respectively). SPICE and PCA further highlighted subphenotypes with or without musculoskeletal symptoms.ConclusionsThis study presents an in-depth characterization of post-Ebola syndrome in Sierra Leonean survivors >2.5 years after disease. The interrelationship between symptom clusters indicates that post-Ebola syndrome is a heterogeneous disease. The distinct musculoskeletal and non-musculoskeletal phenotypes identified likely require targeted therapies to optimize long-term treatment for EVD survivors.

Dataset Information

Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods.

Publications

Acute Coronary Syndrome Symptom Clusters: Illustration of Results Using Multiple Statistical Methods.

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