QRS micro-fragmentation as a mortality predictor

Abstract

Aims Fragmented QRS complex with visible notching on standard 12-lead electrocardiogram (ECG) is understood to represent depolarization abnormalities and to signify risk of cardiac events. Depolarization abnormalities with similar prognostic implications likely exist beyond visual recognition but no technology is presently suitable for quantification of such invisible ECG abnormalities. We present such a technology. Methods and results A signal processing method projects all ECG leads of the QRS complex into optimized three perpendicular dimensions, reconstructs the ECG back from this three-dimensional projection, and quantifies the difference (QRS ‘micro’-fragmentation, QRS-μf) between the original and reconstructed signals. QRS ‘micro’-fragmentation was assessed in three different populations: cardiac patients with automatic implantable cardioverter-defibrillators, cardiac patients with severe abnormalities, and general public. The predictive value of QRS-μf for mortality was investigated both univariably and in multivariable comparisons with other risk factors including visible QRS ‘macro’-fragmentation, QRS-Mf. The analysis was made in a total of 7779 subjects of whom 504 have not survived the first 5 years of follow-up. In all three populations, QRS-μf was strongly predictive of survival (P < 0.001 univariably, and P < 0.001 to P = 0.024 in multivariable regression analyses). A similar strong association with outcome was found when dichotomizing QRS-μf prospectively at 3.5%. When QRS-μf was used in multivariable analyses, QRS-Mf and QRS duration lost their predictive value. Conclusion In three populations with different clinical characteristics, QRS-μf was a powerful mortality risk factor independent of several previously established risk indices. Electrophysiologic abnormalities that contribute to increased QRS-μf values are likely responsible for the predictive power of visible QRS-Mf. Key question Key finding Take-home message QRS-μf is a strong predictor of worsened survival. It can be assessed in standard short-term 12-lead electrocardiograms.

Intra-subject stability of different expressions of spatial QRS-T angle and their relationship to heart rate

“…The predictive value of QRS-T angle was reported additive to HRV-based risk stratification ( Poulikakos et al, 2018 ) in a multivariate analysis of the follow-up cardiac events and mortality in a population of end-stage renal disease patients on haemodialysis. QRS-T angle was also reported predictive of mortality in atrial fibrillation patients who had an automatic cardioverter-defibrillator implanted for primary prophylactic reasons ( Hnatkova et al, 2022 ). At the same time, however, our observations of the absence of any clearly measurable time lag between heart rate changes and QRS-T angle changes, while in agreement with previous observations of rapid changes ( Hnatkova et al, 2010 ), are somewhat at odds with the report by Kenttä et al (2010) who described hysteresis-type differences between RR interval changes and QRS-T angle changes during exercise testing in a relatively small study of healthy volunteers undergoing exercise testing.…”

“…Frontiers in Physiology frontiersin.org an automatic cardioverter-defibrillator implanted for primary prophylactic reasons (Hnatkova et al, 2022). At the same time, however, our observations of the absence of any clearly measurable time lag between heart rate changes and QRS-T angle changes, while in agreement with previous observations of rapid changes (Hnatkova et al, 2010), are somewhat at odds with the report by Kenttä et al (2010) who described hysteresis-type differences between RR interval changes and QRS-T angle changes during exercise testing in a relatively small study of healthy volunteers undergoing exercise testing.…”

“…Subsequently, the risk stratification strength of the QRS-T angle has been confirmed in a large number of studies including investigations of other ischaemic heart disease populations ( de Torbal et al, 2004 ; Malik et al, 2004 ), acute coronary syndrome ( Lown et al, 2012 ), heart failure ( Gotsman et al, 2013 ; Selvaraj et al, 2014 ; Sweda et al, 2020 ), hypertrophic ( Cortez et al, 2017a ; Cortez et al, 2017b ; Jensen et al, 2021 ) and dilated cardiomyopathy ( Li et al, 2016 ), diabetic patients ( Voulgari et al, 2010 ; Cardoso et al, 2013 ; May et al, 2017 ; May et al, 2018 ), renal patients on haemodialysis ( de Bie et al, 2013 ; Poulikakos et al, 2018 ); and many other populations and conditions ranging from systemic sclerosis ( Gialafos et al, 2012 ) and Chagas disease ( Zampa et al, 2014 ) to overall hospital ( Yamazaki et al, 2005 ) and general populations ( Kardys et al, 2003 ; Kors et al, 2003 ; Walsh et al, 2013 ). It has also recently been shown that QRS-T angle might be meaningfully combined with other ECG-based risk factors ( Hnatkova et al, 2022 ).…”

“…The risk-prediction properties of the QRS-T angle are thus now proven beyond any reasonable doubt. The strength of the QRS-T angle-based prediction of cardiovascular risk and/or of cardiovascular death has been shown comparable if not exceeding that of established risk factors including left ventricular ejection fraction, QRS complex width, and heart rate ( Hnatkova et al, 2022 ). Nevertheless, several physiological aspects of the parameter are either unknown or poorly understood.…”

Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence

“…In a recent study, Hnatkova et al showed that QRS microfragmentation, which is imperceptible to the human eye, is strongly related to death. 18 In addition, previous close observations of ECGs have shown that subtle abnormalities increase the risk of ventricular arrhythmias. 19,20 The AI model developed in the present study may automatically detect these complexes and subtle ECG abnormalities.…”