ECG marker of adverse electrical remodeling post-myocardial infarction predicts outcomes in MADIT II study

PMC3522579Background Post-myocardial infarction (MI) structural remodeling is characterized by left ventricular dilatation, fibrosis, and hypertrophy of the non-infarcted myocardium. Objective The goal of our study was to quantify post-MI electrical remodeling by measuring the sum absolute QRST integral (SAI QRST). We hypothesized that adverse electrical remodeling predicts outcomes in MADIT II study participants. Methods Baseline orthogonal ECGs of 750 MADIT II study participants (448 [59.7%] ICD arm) were analyzed. SAI QRST was measured as the arithmetic sum of absolute QRST integrals over all three orthogonal ECG leads. The primary endpoint was defined as sudden cardiac death (SCD) or sustained ventricular tachycardia (VT)/ventricular fibrillation (VF) with appropriate ICD therapies. All-cause mortality served as a secondary endpoint. Results Adverse electrical remodeling in post-MI patients was characterized by wide QRS, increased magnitudes of spatial QRS and T vectors, J-point deviation, and QTc prolongation. In multivariable Cox regression analysis after adjustment for age, QRS duration, atrial fibrillation, New York Heart Association heart failure class and blood urea nitrogen, SAI QRST predicted SCD/VT/VF (HR 1.33 per 100 mV*ms (95%CI 1.11–1.59); P=0.002), and all-cause death (HR 1.27 per 100 mV*ms (95%CI 1.03–1.55), P=0.022) in both arms. No interaction with therapy arm and bundle branch block (BBB) status was found. Conclusions In MADIT II patients, increased SAI QRST is associated with increased risk of sustained VT/VF with appropriate ICD therapies and all-cause death in both ICD and in conventional medical therapy arms, and in patients with and without BBB. Further studies of SAI QRST are warranted.JH Libraries Open Access Fun

Early prediction of cardiac resynchronization therapy response by non-invasive electrocardiogram markers

[EN] Cardiac resynchronization therapy (CRT) is an effective treatment for those patients with severe heart failure. Regrettably, there are about one third of CRT "non-responders", i.e. patients who have undergone this form of device therapy but do not respond to it, which adversely affects the utility and cost-effectiveness of CRT. In this paper, we assess the ability of a novel surface ECG marker to predict CRT response. We performed a retrospective exploratory study of the ECG previous to CRT implantation in 43 consecutive patients with ischemic (17) or non-ischemic (26) cardiomyopathy. We extracted the QRST complexes (consisting of the QRS complex, the S-T segment, and the T wave) and obtained a measure of their energy by means of spectral analysis. This ECG marker showed statistically significant lower values for non-responder patients and, joint with the duration of QRS complexes (the current gold-standard to predict CRT response), the following performances: 86% accuracy, 88% sensitivity, and 80% specificity. In this manner, the proposed ECG marker may help clinicians to predict positive response to CRT in a non-invasive way, in order to minimize unsuccessful procedures.This work was supported by MINECO under grants MTM2013-43540-P and MTM2016-76647-P.Ortigosa, N.; Pérez-Roselló, V.; Donoso, V.; Osca Asensi, J.; Martínez-Dolz, L.; Fernández Rosell, C.; Galbis Verdu, A. (2018). Early prediction of cardiac resynchronization therapy response by non-invasive electrocardiogram markers. Medical & Biological Engineering & Computing. 56(4):611-621. https://doi.org/10.1007/s11517-017-1711-1S611621564Boggiatto P, Fernández C, Galbis A (2009) A group representation related to the stockwell transform. Indiana University Mathematics Journal 58(5):2277–2296Brignole M, Auricchio A, Baron-Esquivias G, Bordachar P, Boriani G et al (2013) 2013 ESC guidelines on cardiac pacing and cardiac resynchronization therapy. Europace 15:1070–1118Brown RA, Lauzon ML, Frayne R (2010) A general description of linear time-frequency transforms and formulation of a fast, invertible transform that samples the continuous s-transform spectrum nonredundantly. IEEE Trans Signal Process 58(1): 281–290Carità P, Corrado E, Pontone G, Curnis A, Bontempi L et al (2016) Non-responders to cardiac resynchronization therapy: insights from multimodality imaging and electrocardiography. A brief review. Int J Cardiol 225:402–407Cazeau S, Leclercq C, Lavergne T, Walker S, Varma C, Linde C et al (2001) Effects of multisite biventricular pacing in patients with heart failure and intraventricular conduction delay. N Engl J Med 344:873–880Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:27Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357Cleland JGF, Abraham WT, Linde C, Gold MR, Young J et al (2013) An individual patient meta-analysis of five randomized trials assessing the effects of cardiac resyn- chronization therapy on morbidity and mortality in patients with symptomatic heart failure. Eur Heart Journal 34(46):3547–3556Cleland JGF, Calvert MJ, Verboven Y, Freemantle N (2009) Effects of cardiac resynchronization therapy on long-term quality of life: an analysis from the Cardiac Resynchronisation-Heart Failure (CARE-HF) study. Am Heart J 157:457–466Cleland JGF, Freemantle N, Erdmann E, Gras D, Kappenberger L et al (2012) Long-term mortality with cardiac resynchronization therapy in the Cardiac Resynchronization-Heart Failure (CARE-HF) trial. Eur J Heart Fail 14:628–634Egoavil CA, Ho RT, Greenspon AJ, Pavri BB (2005) Cardiac resynchronization therapy in patients with right bundle branch block: analysis of pooled data from the MIRACLE and Contak CD trials. Heart Rhythm 2(6):611–615Engels EB, Mafi-Rad M, van Stipdonk AM, Vernooy K, Prinzen FW (2016) Why QRS duration should be replaced by better measures of electrical activation to improve patient selection for cardiac resynchronization therapy. J Cardiovasc Transl Res 9(4):257–265Engels EB, Végh EM, Van Deursen CJ, Vernooy K, Singh JP, Prinzen FW (2015) T-wave area predicts response to cardiac resynchronization therapy in patients with left bundle branch block. J Cardiovasc Electrophysiol 26(2):176–183Eschalier R, Ploux S, Ritter P, Haïssaguerre M, Ellenbogen K, Bordachar P (2015) Nonspecific intraventricular conduction delay: definitions, prognosis, and implications for cardiac resynchronization therapy. Heart Rhythm 12(5):1071–1079Goldenberg I, Kutyifa V, Klein HU, Cannom DS, Brown MW et al (2014) Survival with cardiac-resynchronization therapy in mild heart failure. N Engl J Med 370:1694–1701He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: International joint conference on neural networks, pp 1322–1328Jacobsson J, Borgguist R, Reitan C, Ghafoori E, Chatterjee NA et al (2016) Usefulness of the sum absolute QRST integral to predict outcomes in patients receiving cardiac resynchronization therapy. J Cardiovasc Electrophysiol 118(3):389–395McMurray JJ (2010) Clinical practice. Systolic heart failure. N Engl J Med 3623:228–238Meyer CR, Keiser HN (1977) Electrocardiogram baseline noise estimation and removal using cubic splines and state-space computation techniques. Comput Biomed Res 10:459–470Ortigosa N, Giménez VM (2014) Raw data extraction from electrocardiograms with portable document format. Comput Meth Programs Biomed 113(1):284–289Ortigosa N, Osca J, Jiménez R, Rodríguez Y, Fernández C, Galbis A (2016) Predictive analysis of cardiac resynchronization therapy response by means of the ECG. 2016 Comput Cardio 43:753–756. https://doi.org/10.22489/CinC.2016.218-415Ponikowski P, Voors AA, Anker S, Bueno H, Cleland JG, Coats AJ et al (2016) 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail 18(8):891–975Rad MM, Wijntjens GW, Engels EB, Blaauw Y, Luermans JG et al (2016) Vectorcardiographic QRS area identifies delayed left ventricular lateral wall activation determined by electroanatomic mapping in candidates for cardiac resynchronization therapy. Heart Rhythm 13(1):217–225Shanks M, Delgado V, Bax JJ (2016) Cardiac resynchronization therapy in non-ischemic cardiomyopathy. Journal of Atrial Fibrillation 8(5):47–52Singh JP, Fan D, Heist EK, Alabiad CR, Taub C et al (2006) Left ventricular lead electrical delay predicts response to cardiac resynchronization therapy. Heart Rhythm 3(11):1285–1292Sohaib SM, Finegold JA, Nijjer SS, Hossain R, Linde C et al (2015) Opportunity to increase life span in narrow QRS cardiac resynchronization therapy recipients by deactivating ventricular pacing: evidence from randomized controlled trials. JACC Heart Fail 3:327–336Stockwell RG, Mansinha L, Lowe RP (1996) Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 44(4):998–1001Tang ASL, Wells GA, Talajic M, Arnold MO, Sheldon R et al (2010) Cardiac-resynchronization therapy for mild-to-moderate heart failure. N Engl J Med 363:2385–2395Tereshchenko LG, Cheng A, Park J, Wold N, Meyer TE, Gold MR et al (2015) Novel measure of electrical dyssynchrony predicts response in cardiac resynchronization therapy: results from the SMART-AV trial. Heart Rhythm 12(2):2402–2410van Deursen CJ, Vernooy K, Dudink E, Bergfeldt L, Crijns HJ et al (2015) Vectorcardiographic QRS area as a novel predictor of response to cardiac resynchronization therapy. J Electrocardiol 48(1):45–52Wang TJ (2003) Natural history of asymptomatic left ventricular systolic dysfunction in the community. Circulation 108:977–982Woods B, Hawkins N, Mealing S, Sutton A, Abraham WT et al (2015) Individual patient data network meta-analysis of mortality effects of implantable cardiac devices. Heart 101:1800–1806Ypenburg C, van Bommel RJ, Borleffs CJ, Bleeker GB, Boersma E et al (2009) Long-term prognosis after cardiac resynchronization therapy is related to the extent of left ventricular reverse remodeling at midterm follow-up. J Am Coll Cardiol 53(6):483–490Yu CM, Hayes DL (2013) Cardiac resynchronization therapy: state of the art 2013. Eur Heart J 34:1396–140

Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease.

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction

Beat-to-beat vectorcardiographic analysis of ventricular depolarization and repolarization in myocardial infarction

OBJECTIVES: Increased beat-to-beat variability in the QT interval has been associated with heart disease and mortality. The purpose of this study was to investigate the beat-to-beat spatial and temporal variations of ventricular depolarization and repolarization in vectorcardiogram (VCG) for characterising myocardial infarction (MI) patients. METHODS: Standard 12-lead ECGs of 84 MI patients (22 f, 63±12 yrs; 62 m, 56±10 yrs) and 69 healthy subjects (17 f, 42±18 yrs; 52 m, 40±13 yrs) were investigated. To extract the beat-to-beat QT intervals, a template-matching algorithm and the singular value decomposition method have been applied to synthesise the ECG data to VCG. Spatial and temporal variations in the QRS complex and T-wave loops were studied by investigating several descriptors (point-to-point distance variability, mean loop length, T-wave morphology dispersion, percentage of loop area, total cosine R-to-T). RESULTS: Point-to-point distance variability of QRS and T-loops (0.13±.04 vs. 0.10±0.04, p<0.0001 and 0.16±.07 vs. 0.13±.06, p<0.05) were significantly larger in the MI group than in the control group. The average T-wave morphology dispersion was significantly higher in the MI group than in the control group (62±8 vs. 38±16, p<.0001). Further, its beat-to-beat variability appeared significantly lower in the MI group than in the control group (12±5 v. 15±6u, p<0.005). Moreover, the average percentage of the T-loop area was found significantly lower in the MI group than the controls (46±17 vs. 55±15, p<.001). Finally, the average and beat-to-beat variability of total cosine R-to-T were not found statistically significant between both groups. CONCLUSIONS: Beat-to-beat assessment of VCG parameters may have diagnostic attributes that might help in identifying MI patients.Muhammad A. Hasan, Derek Abbott and Mathias Baumer