Evidence map›Paper›PMID 29665065›Full record

ArticlePacing and clinical electrophysiology : PACE2018

Atrial fibrillation symptom profiles associated with healthcare utilization: A latent class regression analysis.

Megan M Streur, Sarah J Ratcliffe, David J Callans, M Benjamin Shoemaker, Barbara J Riegel

Abstract read
In one paragraph

Article in Pacing and clinical electrophysiology : PACE, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Trial
  2. Article
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  4. Article
  5. Article
  6. Atrial Fibrillation Symptom Perception.The journal for nurse practitioners : JNP · 2019
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Megan M StreurSchool of Nursing, University of Washington, Seattle, WA, USA.ORCID 0000-0002-7233-1293
Sarah J RatcliffeDepartment of Biostatistics & Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
David J CallansCardiology Division, Hospital of the University of Pennsylvania and the Presbyterian Medical Center of Philadelphia, Philadelphia, PA, USA.
M Benjamin ShoemakerDivision of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
Barbara J RiegelUniversity of Pennsylvania School of Nursing, Philadelphia, PA, USA.

Funding

The Vanderbilt Institute for Clinical and Translational Research (VICTR)UL1TR000445 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BERNARD, GORDON RAPHAEL · 2012 to 2016
$41.4M
Aging and Informatics Training ProgramT32NR014833 · NINR · UNIVERSITY OF WASHINGTON · PI THOMPSON, HILAIRE J · 2014 to 2018
$982k
Atrial Fibrillation Susceptibility Due to Genetic Variation at Chromosome 4q25K23HL127704 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI SHOEMAKER, MOORE BENJAMIN · 2015 to 2019
$869k
Atrial fibrillation symptom perceptionF31NR015687 · NINR · UNIVERSITY OF PENNSYLVANIA · PI STREUR, MEGAN · 2015 to 2015
$37k
NCATS NIH HHS CTSA award UL1TR000445NCATS NIH HHS UL1 TR000445NHLBI NIH HHS K23 HL127704NINR NIH HHS F31 NR015687NINR NIH HHS T32 NR014833
6 · The paper itself

Abstract

backgroundSymptoms drive healthcare use among adults with atrial fibrillation, but limited data are available regarding which symptoms are most problematic and which patients are most at-risk. The purpose of this study was to: (1) identify clusters of patients with similar symptom profiles, (2) characterize the individuals within each cluster, and (3) determine whether specific symptom profiles are associated with healthcare utilization.

methodsWe conducted a cross-sectional secondary data analysis of 1,501 adults from the Vanderbilt Atrial Fibrillation Registry. Participants were recruited from Vanderbilt cardiology clinics, emergency department, and in-patient services. Subjects included in our analysis had clinically verified atrial fibrillation and a completed symptom survey. Symptom and healthcare utilization data were collected with the University of Toronto Atrial Fibrillation Severity Scale. Latent class regression analysis was used to identify symptom clusters, with clinical and demographic variables included as covariates. We used Poisson regression to examine the association between latent class membership and healthcare utilization.

resultsParticipants were predominantly male (67%) with a mean age of 58.4 years (±11.9). Four latent classes were evident, including an Asymptomatic cluster (N = 487, 38%), Highly Symptomatic cluster (N = 142, 11%), With Activity cluster (N = 326, 25%), and Mild Diffuse cluster (N = 336, 26%). Highly Symptomatic membership was associated with the greatest rate of emergency department visits and hospitalizations (incident rate ratio 2.4, P < 0.001).

conclusionsClinically meaningful atrial fibrillation symptom profiles were identified that were associated with increased rates of emergency department visits and hospitalizations.

Indexed as

Latent Class AnalysisAtrial FibrillationCross-Sectional StudiesFemaleHumansMaleMiddle AgedPatient Acceptance of Health CareRegression AnalysisSymptom Assessmentatrial fibrillationemergency servicehospitalizationsymptom cluster

Identifiers

PMID29665065
PMCPMC6192872

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.