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ECG quantification of myocardial scar and risk stratification in MADIT-II.

ABSTRACT: BACKGROUND:Low left ventricular ejection fraction (LVEF) increases risk for both sudden cardiac death (SCD) and for heart failure (HF) death; however, implantable cardioverter-defibrillators (ICDs) reduce the incidence of SCD, not HF death. Distinguishing individuals at risk for HF death (non-SCD) versus SCD could improve ICD patient selection. OBJECTIVE:This study evaluated whether electrocardiogram (ECG) quantification of myocardial infarction (MI) could discriminate risk for SCD versus non-SCD. METHODS:Selvester QRS scoring was performed on 995 MADIT-II trial subjects' ECGs to quantify MI size. MIs were categorized as small (0-3 QRS points), medium (4-7) or large (? 8). Mortality, SCD and non-SCD rates in the conventional medical therapy (CMT) arm and mortality and ventricular tachycardia/fibrillation (VT/VF) rates in the ICD arm were analyzed by QRS score group. Both arms were analyzed to determine ICD efficacy by QRS score group. RESULTS:In the CMT arm, mortality, SCD and non-SCD rates were similar across QRS score groups (P = 0.73, P = 0.92, and P = 0.77). The ICD arm showed similar rates of mortality (P = 0.17) and VT/VF (P = 0.24) across QRS score groups. ICD arm mortality was lower than CMT arm mortality across QRS score groups with greatest benefit in the large scar group. CONCLUSION:Recently, QRS score was shown to be predictive of VT/VF in the SCD-HeFT population consisting of both ischemic and nonischemic HF and having a maximum LVEF of 35% versus 30% for MADIT-II. Our study found that QRS score did not add prognostic value in the MADIT-II population exhibiting relatively more severe cardiac dysfunction.

SUBMITTER: Loring Z

PROVIDER: S-EPMC3779916 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

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ECG quantification of myocardial scar and risk stratification in MADIT-II.

Loring Zak Z Zareba Wojciech W McNitt Scott S Strauss David G DG Wagner Galen S GS Daubert James P JP

Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc 20130609 5

<h4>Background</h4>Low left ventricular ejection fraction (LVEF) increases risk for both sudden cardiac death (SCD) and for heart failure (HF) death; however, implantable cardioverter-defibrillators (ICDs) reduce the incidence of SCD, not HF death. Distinguishing individuals at risk for HF death (non-SCD) versus SCD could improve ICD patient selection.<h4>Objective</h4>This study evaluated whether electrocardiogram (ECG) quantification of myocardial infarction (MI) could discriminate risk for SC ...[more]

PMID: 24047486

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Project description:BackgroundMyocardial scarring from infarction or nonischemic fibrosis forms an arrhythmogenic substrate. The Selvester QRS score has been extensively validated for estimating myocardial infarction scar size in the absence of ECG confounders, but has not been tested to quantify scar in patients with hypertrophy, bundle branch/fascicular blocks, or nonischemic cardiomyopathy. We assessed the hypotheses that (1) QRS scores (modified for each ECG confounder) correctly identify and quantify scar in ischemic and nonischemic patients when compared with the reference standard of cardiac magnetic resonance using late-gadolinium enhancement, and (2) QRS-estimated scar size predicts inducible sustained monomorphic ventricular tachycardia during electrophysiological testing.Methods and resultsOne hundred sixty-two patients with left ventricular ejection fraction < or =35% (95 ischemic, 67 nonischemic) received 12-lead ECG and cardiac magnetic resonance using late-gadolinium enhancement before implantable cardioverter defibrillator placement for primary prevention of sudden cardiac death. QRS scores correctly diagnosed cardiovascular magnetic resonance scar presence with receiver operating characteristics area under the curve of 0.91 and correlation for scar quantification of r=0.74 (P<0.0001) for all patients. Performance within hypertrophy, conduction defect, and nonischemic subgroups ranged from area under the curve of 0.81 to 0.94 and r=0.60 to 0.80 (P<0.001 for all). Among the 137 patients undergoing electrophysiological or device testing, each 3-point QRS-score increase (9% left ventricular scarring) was associated with an odds ratio for inducing monomorphic ventricular tachycardia of 2.2 (95% CI, 1.5 to 3.2; P<0.001) for all patients, 1.7 (1.0 to 2.7, P=0.04) for ischemics, and 2.2 (1.0 to 5.0, P=0.05) for nonischemics.ConclusionsQRS scores identify and quantify scar in ischemic and nonischemic cardiomyopathy patients despite ECG confounders. Higher QRS-estimated scar size is associated with increased arrhythmogenesis and warrants further study as a risk-stratifying tool.

Project description:BackgroundAtherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal.MethodsUsing deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023.FindingsECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age.InterpretationECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired.FundingAnumana.

Dataset Information

ECG quantification of myocardial scar and risk stratification in MADIT-II.

Publications

ECG quantification of myocardial scar and risk stratification in MADIT-II.

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