Evidence map›Paper›PMID 37956651›Full record

ArticleEuropean heart journal2024

Dynamic electrocardiogram changes are a novel risk marker for sudden cardiac death.

Hoang Nhat Pham, Lauri Holmstrom, Harpriya Chugh, Audrey Uy-Evanado, Kotoka Nakamura, Zijun Zhang, Angelo Salvucci, Jonathan Jui, Kyndaron Reinier, Sumeet S Chugh

Open access · hybridAbstract read
In one paragraph

Article in European heart journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
7.9field-weighted citation impact, top 2% 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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 36 citations in OpenAlex.

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  16. Ventricular fibrillation and the proteome problem: can we solve it?European heart journal. Acute cardiovascular care · 2024
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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

10 authors at 3 institutions in 1 country.

Hoang Nhat PhamCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Lauri HolmstromCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Harpriya ChughCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Audrey Uy-EvanadoCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Kotoka NakamuraCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Zijun ZhangDivision of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Angelo SalvucciVentura County Health Care Agency, Ventura, CA, USA.
Jonathan JuiDepartment of Emergency Medicine, Oregon Health & Science University, Portland, OR, USA.
Kyndaron ReinierCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.
Sumeet S ChughCenter for Cardiac Arrest Prevention, Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Advanced Health Sciences Pavilion, Suite A3100, 127 S. San Vicente Blvd., Los Angeles, CA 90048, USA.ORCID 0000-0002-4417-5326
Cedars-Sinai Medical Center · USOregon Health & Science University · USVentura County Health Care Agency · US

Funding

Prediction of imminent sudden cardiac death based on warning signsR01HL147358 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI CHUGH, SUMEET S. · 2020 to 2023
$3.2M
Community-Based Evaluation of Sudden Cardiac Death in Hispanics/LatinosR01HL145675 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI CHUGH, SUMEET S. · 2019 to 2023
$2.4M
NHLBI NIH HHS R01 HL145675NHLBI NIH HHS R01 HL147358NIH HHS
6 · The paper itself

Abstract

BACKGROUND AND

aimsElectrocardiogram (ECG) abnormalities have been evaluated as static risk markers for sudden cardiac death (SCD), but the potential importance of dynamic ECG remodelling has not been investigated. In this study, the nature and prevalence of dynamic ECG remodelling were studied among individuals who eventually suffered SCD.

methodsThe study population was drawn from two prospective community-based SCD studies in Oregon (2002, discovery cohort) and California, USA (2015, validation cohort). For this present sub-study, 231 discovery cases (2015-17) and 203 validation cases (2015-21) with ≥2 archived pre-SCD ECGs were ascertained and were matched to 234 discovery and 203 validation controls based on age, sex, and duration between the ECGs. Dynamic ECG remodelling was measured as progression of a previously validated cumulative six-variable ECG electrical risk score.

resultsOregon SCD cases displayed greater electrical risk score increase over time vs. controls [+1.06 (95% confidence interval +0.89 to +1.24) vs. -0.05 (-0.21 to +0.11); P < .001]. These findings were successfully replicated in California [+0.87 (+0.7 to +1.04) vs. -0.11 (-0.27 to 0.05); P < .001]. In multivariable models, abnormal dynamic ECG remodelling improved SCD prediction over baseline ECG, demographics, and clinical SCD risk factors in both Oregon [area under the receiver operating characteristic curve 0.770 (95% confidence interval 0.727-0.812) increased to area under the receiver operating characteristic curve 0.869 (95% confidence interval 0.837-0.902)] and California cohorts.

conclusionsDynamic ECG remodelling improved SCD risk prediction beyond clinical factors combined with the static ECG, with successful validation in a geographically distinct population. These findings introduce a novel concept of SCD dynamic risk and warrant further detailed investigation.

Indexed as

Arrhythmias, CardiacDeath, Sudden, CardiacElectrocardiographyHumansProspective StudiesRisk FactorsDynamicECGGeneral populationRisk predictionSudden cardiac death

Identifiers

PMID37956651
PMCPMC10919917
OpenAlexW4388620769

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.