Evidence map›Paper›PMID 31726979›Full record

ArticleBMC cardiovascular disorders2019

Dynamic predictive accuracy of electrocardiographic biomarkers of sudden cardiac death within a survival framework: the Atherosclerosis Risk in Communities (ARIC) study.

Erick A Perez-Alday, Aron Bender, David German, Srini V Mukundan, Christopher Hamilton, Jason A Thomas, Yin Li-Pershing, Larisa G Tereshchenko

Open access · goldAbstract readComparative StudyMulticenter Study
In one paragraph

Article in BMC cardiovascular disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
2.7field-weighted citation impact, top 8% 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

18 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

  1. Pooled it
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  6. Competing risks of monomorphic vs. non-monomorphic ventricular arrhythmias in primary prevention implantable cardioverter-defibrillator recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) study.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024
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  9. Digitizing ECG image: A new method and open-source software code.Computer methods and programs in biomedicine · 2022
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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

8 authors at 4 institutions in 1 country.

Erick A Perez-AldayKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
Aron BenderKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
David GermanKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
Srini V MukundanKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
Christopher HamiltonKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
Jason A ThomasKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
Yin Li-PershingKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA.
Larisa G TereshchenkoKnight Cardiovascular Institute, Department of Medicine, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd; UHN62, Portland, OR, 97239, USA. tereshch@ohsu.edu.ORCID 0000-0002-6976-1313
Oregon Health & Science University · USEmory University · USJohns Hopkins University · USUniversity of California, Los Angeles · US

Funding

Novel ECG Measures and Risk of Sudden Cardiac DeathR01HL118277 · NHLBI · OREGON HEALTH & SCIENCE UNIVERSITY · PI TERESHCHENKO, LARISA GENNADIEVNA · 2013 to 2016
$1.5M
Novel ECG measures and risk of sudden cardiac deathR56HL118277 · NHLBI · OREGON HEALTH & SCIENCE UNIVERSITY · PI TERESHCHENKO, LARISA GENNADIEVNA · 2018 to 2018
$396k
NHLBI NIH HHS HHSN268201700001INHLBI NIH HHS HHSN268201700002INHLBI NIH HHS HHSN268201700003INHLBI NIH HHS HHSN268201700004INHLBI NIH HHS HHSN268201700005INHLBI NIH HHS R01 HL118277NHLBI NIH HHS R56 HL118277
6 · The paper itself

Abstract

backgroundThe risk of sudden cardiac death (SCD) is known to be dynamic. However, the accuracy of a dynamic SCD prediction is unknown. We aimed to measure the dynamic predictive accuracy of ECG biomarkers of SCD and competing non-sudden cardiac death (non-SCD).

methodsAtherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n = 15,716; 55% female, 73% white, age 54.2 ± 5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics (heart rate, QRS, QTc) were measured. Adjudicated SCD was the primary outcome; non-SCD was the competing outcome. Time-dependent area under the receiver operating characteristic curve (ROC(t) AUC) analysis was performed to assess the prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years using a survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured.

resultsOver a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. No ECG biomarkers predicted SCD within 3 months after ECG recording. Within 6 months, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD (AUC 0.527; 95%CI 0.303-0.75). SVG elevation more accurately predicted SCD if the ECG was recorded 6 months before SCD (AUC 0.706; 95%CI 0.526-0.886) than 2 years before SCD (AUC 0.608; 95%CI 0.515-0.701). Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors: 18% of SCD events were reclassified from low or intermediate risk to a high-risk category. QRS-T angle was the strongest long-term predictor of SCD (AUC 0.710; 95%CI 0.668-0.753 for ECG recorded within 10 years before SCD).

conclusionShort-term and long-term predictive accuracy of ECG biomarkers of SCD differed, reflecting differences in transient vs. persistent SCD substrates. The dynamic predictive accuracy of ECG biomarkers should be considered for competing SCD risk scores. The distinction between markers predicting short-term and long-term events may represent the difference between markers heralding SCD (triggers or transient substrates) versus markers identifying persistent substrate.

Indexed as

ElectrocardiographyHeart RateArrhythmias, CardiacDeath, Sudden, CardiacFemaleHeart Conduction SystemHumansIncidenceMaleMiddle AgedPredictive Value of TestsPrognosisProspective StudiesRisk AssessmentRisk FactorsTime FactorsDynamic predictionElectrocardiographyGlobal electrical heterogeneitySudden cardiac deathVectorcardiography

Identifiers

PMID31726979
PMCPMC6854807
OpenAlexW2988568711

What OpenQuestion holds

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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.