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. 2022 Oct 21;43(40):4177-4191.
doi: 10.1093/eurheartj/ehac085.

QRS micro-fragmentation as a mortality predictor

Katerina Hnatkova  1 , Irena Andršová  2   3 , Tomáš Novotný  2   3 , Annie Britton  4 , Martin Shipley  4 , Bert Vandenberk  5 , David J Sprenkeler  6 , Juhani Junttila  7 , Tobias Reichlin  8 , Simon Schlögl  9   10 , Marc A Vos  6 , Tim Friede  10   11 , Axel Bauer  12 , Heikki V Huikuri  7 , Rik Willems  5 , Georg Schmidt  13   14 , Michael R Franz  15 , Christian Sticherling  16 , Markus Zabel  9   10 , Marek Malik  1   3
Affiliations

QRS micro-fragmentation as a mortality predictor

Katerina Hnatkova et al. Eur Heart J. .

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.

Keywords: Electrocardiogram; Fragmentation; Mortality prediction; QRS complex.

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Conflict of interest statement

Conflict of interest: Dr Hnatkova reports support from European Community’s Seventh Framework Programme, during the study, and from British Heart Foundation, during the study, and personal royalties from St Paul’s Cardiac Electrophysiology; Dr Andršová reports personal consulting fees from St Paul’s Cardiac Electrophysiology, and personal lecture honoraria from Servier Laboratories; Dr Novotný reports support from European Community’s Seventh Framework Programme, during the study, and personal consulting fees from St Paul’s Cardiac Electrophysiology; Dr Britton reports no conflict of interest; Dr Shipley reports no conflict of interest; Dr Vandenberk reports fellowship funding from Frans Van de Werf Fund for Clinical Cardiovascular Research; Dr Sprenkeler reports no conflict of interest; Dr Junttila reports no conflict of interest; Dr Reichlin reports institutional consultation fees from Biosense Webster, Boston Scientific, Biotronik, Farapulse, Bayer, BMS-Pfizer, and Medtronic, institutional lecture honoraria from Biosense Webster, Bayer, BMS-Pfizer, and Medtronic, and institutional meeting support from Bayer, BMS-Pfizer, and Biotronik; Dr Schlögl reports from European Community’s Seventh Framework Programme, during the study, and private investor income from Johnson and Johnson, and Bayer AG; Dr Vos reports a grant from the Horizon 2020 programme; Dr Friede reports support from European Community’s Seventh Framework Programme, during the study, personal consultation fees from Bayer, CSL Behring, Galapagos, Minoryx, Vifor, Novartis, and LivaNova, lecture fees from Fresenius Kabi, and personal fees for Advisory and Data Safety Monitoring boards from Bayer, Biosense Webster, Janssen, Novartis, Roche, and BSM; Dr Bauer reports grant from Medtronic Bakken Research Center, and lecture fees from Medtronic; Dr Huikuri reports no conflict of interest; Dr Willems reports support from European Community’s Seventh Framework Programme, during the study, post-doctoral clinical researcher support by the Fund for Scientific Research Flanders, during the study, institutional research grants from Abbott, Biotronik, Boston Scientific, and Medtronic, institutional lecture fees from Abbott, Biotronik, Boston Scientific, Medtronic, Daichi Sankyo, and Boehringer Ingelheim, meeting support from Daichi Sankyo, and unpaid Secretary position at the Belgian Heart Rhythm Society; Dr Schmidt reports his Chair positions of the Association of Medical Ethics Committees in the Federal Republic of Germany; Dr Franz reports no conflict of interest; Dr Sticherling reports support from European Community’s Seventh Framework Programme, during the study, consulting fees from Abbott, Biotronik, Boston Scientific, and Medtronic, and lecture honoraria from Abbott, Biotronik, Boston Scientific, and Medtronic; Dr Zabel reports support from European Community’s Seventh Framework Programme, during the study; Dr Malik reports support from European Community’s Seventh Framework Programme, during the study, and from British Heart Foundation, during the study, personal royalties from St Paul’s Cardiac Electrophysiology, personal royalties from Elsevier Academic Press, and institutional consultation fees from Sosei Heptares, BioCryst Pharmaceuticals, and Helsinn Healthcare.

Figures

Structured Graphical Abstract
Structured Graphical Abstract
Principles of QRS micro-fragmentation.
Figure 1
Figure 1
Example of ECG processing of a recordings in a 69-year-old male survivor (top row) and a 60-year-old patient who died 11 months later (bottom row). In both cases, the QRS duration was 109 ms. Filtered QRS complex patterns of independent Leads I, II, V1, V2, …, V6 are considered together as if on the same isoelectric axis (A). Singular value decomposition transforms the signals into eight algebraically orthogonal signals which are sorted according to their contribution to the original ECG leads (Components 1–3 are shown in red, 4–6 in green, and 7 and eight in amber in panels (B); the 7th and 8th components are almost invisible in these cases). The Components 1–3 create the optimized three-dimensional QRS vector projection. When these components are used to reconstruct the original ECG, patterns in panels (C) are obtained while reconstruction based on Components 1–6 gives patterns in panels (D). (E) and (F) show the differences between the original ECG are the reconstruction based on 1–3 and 1–6 components, respectively (i.e. E = A–C, F = A–D). The residuals shown in panels (F) (corresponding to the contribution of 7th and 8th components) are considered noise and eliminated. QRS micro-fractionation is calculated as the averaged absolute area under contribution by Components 4–6 shown in panels G (G = D–C = E–F). This area is related to the absolute area under the original ECG signal and was 0.887 and 5.754% in the top and bottom row ECGs, respectively. Note that the differences between panels (A) and (C) cannot be visually quantified.
Figure 2
Figure 2
For each of the investigated populations, the left panel shows the comparison between distributions of QRS micro-fragmentation values in survivors (green line) and non-survivors (red line). The distributions were compared by Kolmogorov–Smirnov test (K–S statistics values shown). The yellow vertical lines mark the 3.5% dichotomy. The right panels show the Kaplan–Meier probabilities of non-survival for subjects with QRS micro-fragmentation ≤3.5% (green line) and >3.5% (red line). Numbers of subjects at risk are shown below the panel in corresponding colours. The non-survival probabilities were compared by log-rank test.
Figure 3
Figure 3
All panels show Kaplan–Meier probabilities of death in different subgroups of the EU-CERT-ICD population. Green and red lines correspond to patients with QRS micro-fragmentation ≤3.5 and >3.5%, respectively. The top two panels show sub-populations with ischaemic heart disease and non-ischaemic heart disease. The bottom four panels correspond to the sub-populations of four different centres that contributed more than 100 patients. Numbers of patients at risk are shown below each panel in corresponding colours. The non-survival probabilities were compared by log-rank test.
Figure 4
Figure 4
For each of the investigated populations, the scaled Venn diagram on the left shows the proportions of subjects observed. Red circle: QRS micro-fragmentation >3.5% (QRS-μf >3.5%). Blue circle: QRS macro-fragmentation (QRS-Mf). Violet overlap between the red and blue circle: Both QRS macro-fragmentation and QRS micro-fragmentation >3.5%. Green reminder of the background circle: No QRS macro-fragmentation and QRS micro-fragmentation ≤3.5%. The sizes of the red and blue circles are in proportion of the background circle corresponding to the total population. The percentages of the categories are shown. The panels on the right show the comparisons between distributions of QRS micro-fragmentation values in subjects with (blue line) and without observed QRS macro-fragmentation (green line). The distributions were compared by Kolmogorov–Smirnov test (K–S statistics values shown). The yellow vertical lines mark the 3.5% dichotomy.
Figure 5
Figure 5
For each of the investigated populations, the panel on the left shows Kaplan–Meier probabilities of non-survival for subjects with (blue line) and without observed QRS macro-fragmentation (green line). The panels on the right show Kaplan–Meier probabilities of non-survival in subjects without QRS macro-fragmentation stratified by QRS micro-fragmentation above (red line) and below (green line) of the 3.5% dichotomy. Numbers of patients at risk are shown below each panel in corresponding colours. The non-survival probabilities were compared by log-rank test.
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