Evidence map›Paper›PMID 39854935›Full record

ArticleJournal of electrocardiology

Estimating very low ejection fraction from the 12 Lead ECG among patients with acute heart failure.

Sunita Pokhrel Bhattarai, Dillon J Dzikowicz, Ying Xue, Robert Block, Rebecca G Tucker, Shilpa Bhandari, Victoria E Boulware, Breanne Stone, Mary G Carey

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of electrocardiology. 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
–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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Sunita Pokhrel BhattaraiUniversity of Rochester School of Nursing, NY, USA. Electronic address: sunita_pokhrelbhattarai@urmc.rochester.edu.
Dillon J DzikowiczUniversity of Rochester School of Nursing, NY, USA; University of Rochester Medical Center, NY, USA; Clinical Cardiovascular Research Center, University of Rochester Medical Center, NY, USA.
Ying XueUniversity of Rochester School of Nursing, NY, USA.
Robert BlockDepartment of Public Health Sciences, University of Rochester Medical Center, NY, USA; Cardiology Division, Department of Medicine, University of Rochester Medical Center, USA.
Rebecca G TuckerUniversity of Rochester School of Nursing, NY, USA.
Shilpa BhandariK Force Inc., OH, USA.
Victoria E BoulwareUniversity of Rochester Medical Center, NY, USA.
Breanne StoneUniversity of Rochester Medical Center, NY, USA.
Mary G CareyUniversity of Rochester School of Nursing, NY, USA; University of Rochester Medical Center, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying patients with low left ventricular ejection fraction (LVEF) in the emergency department using an electrocardiogram (ECG) may optimize acute heart failure (AHF) management. We aimed to assess the efficacy of 527 automated 12‑lead ECG features for estimating LVEF among patients with AHF.

methodMedical records of patients >18 years old and AHF-related ICD codes, demographics, LVEF %, comorbidities, and medication were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) identified important ECG features and evaluated performance.

resultsAmong 851 patients, the mean age was 74 years (IQR:11), male 56 % (n = 478), and the median body mass index was 29 kg/m

conclusionsAn explainable machine learning model with physiologically feasible predictors may help screen patients with low LVEF in AHF.

Indexed as

ElectrocardiographyHeart FailureStroke VolumeAcute DiseaseAgedFemaleHumansMaleRetrospective StudiesAcute heart failureElectrocardiogramLASSOLeft ventricular ejection fractionMachine learningReduced ejection fraction

Identifiers

PMID39854935

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.