학술논문
Automated Quantification of Abnormal QRS Peaks From High-Resolution ECGs Predicts Late Ventricular Arrhythmias in Hypertrophic Cardiomyopathy: A 5-Year Prospective Multicenter Study.
Document Type
Academic Journal
Author
Suszko AM; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Chakraborty P; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Viswanathan K; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Barichello S; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Sapp J; Division of Cardiology Queen Elizabeth II Health Sciences Center Halifax Canada.; Talajic M; Montreal Heart Institute Montreal Canada.; Laksman Z; Division of Cardiology St. Paul's Hospital Vancouver Canada.; Yee R; Division of Cardiology London Health Sciences Center London Canada.; Woo A; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Spears D; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Adler A; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Rakowski H; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.; Chauhan VS; Division of Cardiology, Peter Munk Cardiac Center University Health Network Toronto Canada.
Source
Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101580524 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2047-9980 (Electronic) Linking ISSN: 20479980 NLM ISO Abbreviation: J Am Heart Assoc Subsets: MEDLINE
Subject
Language
English
Abstract
Background Patients with hypertrophic cardiomyopathy (HCM) are at risk of ventricular arrhythmia (VA) attributed to abnormal electrical activation arising from myocardial fibrosis and myocyte disarray. We sought to quantify intra-QRS peaks (QRSp) in high-resolution ECGs as a measure of abnormal activation to predict late VA in patients with HCM. Methods and Results Prospectively enrolled patients with HCM (n=143, age 53±14 years) with prophylactic implantable cardioverter-defibrillators had 3-minute, high-resolution (1024 Hz), digital 12-lead ECGs recorded during intrinsic rhythm. For each precordial lead, QRSp was defined as the total number of peaks detected in the QRS complex that deviated from a smoothing filtered version of the QRS. The VA end point was appropriate implantable cardioverter-defibrillator therapy during 5-year prospective follow-up. After 5 years, 21 (16%) patients had VA. Patients who were VA positive had greater QRSp (6.0 [4.0-7.0] versus 4.0 [2.0-5.0]; P <0.01) and lower left ventricular ejection fraction (57±11 versus 62±9; P =0.038) compared with patients who were VA negative, but had similar established HCM risk metrics. Receiver operating characteristic analysis revealed that QRSp discriminated VA (area under the curve=0.76; P <0.001), with a QRSp ≥4 achieving 91% sensitivity and 39% specificity. The annual VA rate was greater in patients with QRSp ≥4 versus QRSp <4 (4.4% versus 0.98%; P =0.012). In multivariable Cox regression, age <50 years (hazard ratio [HR], 2.53; P =0.009) and QRSp (HR per QRS peak, 1.41; P =0.009) predicted VA after adjusting for established HCM risk metrics. In patients aged <50 years, the annual VA rate was 0.0% for QRSp <4 compared with 6.9% for QRSp ≥4 ( P =0.012). Conclusions QRSp predicted VA in patients with HCM who were eligible for an implantable cardioverter-defibrillator after adjusting for established HCM risk metrics, such that each additional QRS peak increases VA risk by 40%. QRSp <4 was associated with a <1% annual VA risk in all patients, and no VA risk among those aged <50 years. This novel ECG metric may improve patient selection for prophylactic implantable cardioverter-defibrillator therapy by identifying those with low VA risk. These findings require further validation in a lower risk HCM cohort. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02560844.