Symptom Trajectories After Emergency Department Visits for Potential Acute Coronary Syndrome
AuthorKnight, Elizabeth Pickering
growth mixture modeling
acute coronary syndrome
AdvisorShea, Kimberly D.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractBackground: Many patients evaluated for acute coronary syndrome (ACS) in emergency departments (EDs) experience ongoing or recurrent symptoms after discharge, regardless of their ultimate medical diagnosis. A comprehensive understanding of post-ED symptom trajectories is lacking. Aims: Aim 1 was to determine trajectories of severity of common symptoms (chest pressure, chest discomfort, unusual fatigue, chest pain, shortness of breath, lightheadedness, upper back pain and shoulder pain) in the six months following an ED visit for potential ACS. Aim 2 was to identify relationships between symptom trajectories and baseline physiologic factors (age, gender, diabetes status, diagnosis, comorbidities, functional status) and situational factors (marital status, insurance status, education level). Aim 3 was to identify relationships between symptom trajectories and health service use (outpatient visits and calls, ED visits, 911 calls, hospitalization) in the six months after the ED visit. Methods: This was a secondary data analysis from a study conducted in five U.S. EDs. Patients (n=1002) who had abnormal electrocardiogram or biomarker testing and were identified by the triage nurse as potentially having ACS were enrolled. Symptom severity was assessed in the hospital and 30 days and six months post-discharge using the 13-item ACS Symptom Checklist. Symptom severity was modeled across the three study time points using growth mixture modeling. Model selection was based on interpretability, theoretical justification, and statistical fit indices. Patient characteristics were used to predict trajectories using logistic regression and differences in health service use were tested using chi-square analysis. Results: Between two and four distinct trajectory classes were identified for each symptom. Identified trajectories were labeled "tapering off," "mild/persistent," "moderate/persistent," "moderate/worsening," "moderate/improving," "late onset," and "severe/improving." Age, sex, diabetes, BMI, functional status, insurance status, and diagnosis significantly predicted symptom trajectories. Clinic visits and phone calls, 911 calls, ED visits, and probability of hospitalization varied significantly among trajectories. Conclusions: Research on the individual nature of symptom trajectories can support patient-centered care. Patients at risk for ongoing symptoms and increased health service use can be targeted for education and follow-up based on clinically observable characteristics. Further research is needed to verify the existence of multiple symptoms trajectories in diverse populations.
Degree ProgramGraduate College