Observational studyJournal of electrocardiology
Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome.
Observational study in Journal of electrocardiology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06699056 (AI-Enabled Direct-from-ECG Ejection Fraction), which is not on this map. Cited by 9 papers.
What it found
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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.
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.
AI-Enabled Direct-from-ECG Ejection Fraction (EF) Severity Using COR ECG Wearable Monitor
Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.Sensors (Basel, Switzerland) · 2026Article
- Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction.medRxiv : the preprint server for health sciences · 2025Article
- Detection of Right and Left Ventricular Dysfunction in Pediatric Patients Using Artificial Intelligence-Enabled ECGs.Journal of the American Heart Association · 2024Article
- The Role of QRS Complex and ST-Segment in Major Adverse Cardiovascular Events Prediction in Patients with ST Elevated Myocardial Infarction: A 6-Year Follow-Up Study.Diagnostics (Basel, Switzerland) · 2024Article
- Estimating Ejection Fraction from the 12 Lead ECG among Patients with Acute Heart Failure.medRxiv : the preprint server for health sciences · 2024Article
- Electrocardiographic and biochemical predictors of left ventricular remodeling early after ST-segment elevation myocardial infarction.Future cardiology · 2024Article
- Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.JACC. Cardiovascular imaging · 2022Article
- Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study.The Lancet. Digital health · 2022Observational
- In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.Journal of the American Heart Association · 2021Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundNon-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting.
methodsWe performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features.
resultsOur final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%-65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R
conclusionsPoor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.
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Registered trials
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.