Evidence map›Paper›PMID 34098234›Full record

ArticleHeart & lung : the journal of critical care

Multimorbidity phenotypes in patients presenting to the emergency department with possible acute coronary syndrome.

Katherine M Breen, Lorna Finnegan, Karen M Vuckovic, Anne M Fink, Wayne Rosamond, Holli A DeVon

Open access · greenAbstract read
In one paragraph

Article in Heart & lung : the journal of critical care. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 36% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 5 institutions in 1 country.

Katherine M BreenNell Hodgson Woodruff School of Nursing, Emory University, 1520 Clifton Road, Atlanta, GA 30322, United States. Electronic address: Kbreen2@emory.edu.
Lorna FinneganLoyola University Chicago, Marcella Niehoff School of Nursing, Chicago, IL, United States.
Karen M VuckovicUniversity of Illinois at Chicago, College of Nursing, Department of Biobehavioral Health Science, Chicago, IL, United States.
Anne M FinkUniversity of Illinois at Chicago, College of Nursing, Department of Biobehavioral Health Science, Chicago, IL, United States.
Wayne RosamondUniversity of North Carolina, Gillings School of Global Public Health, Chapel Hill, NC, United States.
Holli A DeVonUniversity of California Los Angeles School of Nursing, Los Angeles, CA, United States.
University of Illinois Chicago · USEmory University · USLoyola University Chicago · USUniversity of California, Los Angeles · USUniversity of North Carolina at Chapel Hill · US

Funding

Training in Interventions to Improve Outcomes in Chronic ConditionsT32NR012715 · NINR · EMORY UNIVERSITY · PI GARY, REBECCA A, SONG, MI-KYUNG · 2012 to 2022
$3.2M
The Influence of Gender on Symptom Characteristics During Acute Coronary SyndromeR01NR012012 · NINR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI DEVON, HOLLI A · 2010 to 2013
$1.9M
NINR NIH HHS R01 NR012012NINR NIH HHS T32 NR012715
6 · The paper itself

Abstract

backgroundMultimorbidity (> 2 conditions) increases the risk of adverse outcomes and challenges health care systems for patients with acute coronary syndrome (ACS). These complications may be partially attributed to ACS clinical care which is driven by single-disease-based practice guidelines; current guidelines do not consider multimorbidity.

objectivesTo identify multimorbidity phenotypes (combinations of conditions) with suspected ACS. We hypothesized that: 1) subgroups of patients with similar multimorbidity phenotypes could be identified, 2) classes would differ according to diagnosis, and 3) class membership would differ by sex, age, functional status, family history, and discharge diagnosis.

methodsThis was a secondary analysis of data from a large multi-site clinical study of patients with suspected ACS. Conditions were determined by items on the Charlson Comorbidity Index and the ACS Patient Information Questionnaire. Latent class analysis was used to identify phenotypes.

resultsThe sample (n = 935) was predominantly male (68%) and middle-aged (mean= 59 years). Four multimorbidity phenotypes were identified: 1) high multimorbidity (Class 1) included hyperlipidemia, hypertension (HTN), obesity, diabetes, and respiratory disorders (COPD or asthma); 2) low multimorbidity (Class 2) included only obesity; 3) cardiovascular multimorbidity (Class 3) included HTN, hyperlipidemia, and coronary heart disease; and 4) cardio-oncology multimorbidity (Class 4) included HTN, hyperlipidemia, and cancer. Patients ruled-in for ACS primarily clustered in Classes 3 and 4 (OR 2.82, 95% CI 1.95-4.05, p = 0.001 and OR 1.76, 95% CI 1.13-2.74, p = 0.01).

conclusionIdentifying and understanding multimorbidity phenotypes may assist with risk-stratification and better triage of high-risk patients in the emergency department.

Indexed as

Acute Coronary SyndromeEmergency Service, HospitalHumansMaleMiddle AgedMultimorbidityPhenotypeTriageACSCardiovascular-oncologyLatent class analysisMultimorbidityMultimorbidity phenotypes

Identifiers

PMID34098234
PMCPMC8328942
OpenAlexW3170900503

What OpenQuestion holds

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Registered trials

None linked

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.