Heterogeneous Dynamical Pathways Preceding Ventricular Arrhythmia: A Multi-Cohort Phase-Space Analysis of Cardiovascular Criticality

AbstractBackground: Whether ventricular arrhythmia follows a canonical dynamical sequence or multiple distinct collapse pathways remains unresolved. Standard risk metrics fail to discriminate sudden cardiac death (SCD) from pump failure death (PFD), motivating a dynamical characterisation of pre-arrhythmic cardiovascular states.Objective: We asked whether multiple distinct collapse pathways coexist or a single canonical sequence predominates, and introduced ECSoC (Emergent Criticality and Self-Organised Collapse) to characterise pre-arrhythmic dynamics across multiple disease severities.Methods: ECSoC integrates detrended fluctuation analysis (DFA) scaling exponents, the Criticality Heterogeneity Index (CHI = 2(α₁−α₂)), and scaling fit quality (R²) within a three-layer phase-space framework. Nine publicly available PhysioNet cohorts (total N > 1,500 records) spanning healthy controls through end-stage structural heart disease were analysed retrospectively. All analyses are hypothesis-generating.Results: The canonical Suppress→Late-rise trajectory was observed in only 1.6–6.7% of arrhythmic events, with 93–98% proceeding via other routes, suggesting the presence of apparent pathway heterogeneity in this retrospective dataset, pending prospective confirmation (H1). Scaling manifold breakdown (Phase V) was absent across all healthy and low-risk subjects and showed a pattern directionally consistent with increasing disease severity across strata; however, adjacent strata were drawn from different cohorts with heterogeneous recording durations and patient selection criteria, precluding precise quantitative comparison (H3). Post-hoc external replication is consistent with R² functioning as a continuous discriminator of arrhythmic from healthy dynamics; the fixed threshold of 0.93 was derived empirically from a single cohort and requires prospective calibration before generalisation.Conclusion: Retrospective exploratory evidence is consistent with ventricular arrhythmia involving apparent dynamical pathway heterogeneity across this multi-cohort dataset, pending prospective confirmation. Three prospectively falsifiable hypotheses (H2–H4) define the roadmap for prospective validation in outcome Keywords: self-organised criticality; cardiovascular dynamics; ventricular arrhythmia; detrended fluctuation analysis; heart rate variability; sudden cardiac death; heart failure; phase space; network physiology1. Introduction1.1 Clinical Context and MotivationSudden cardiac death (SCD) and pump failure death (PFD) account for the majority of mortality in patients with structural heart disease [1,2]. Although left ventricular ejection fraction and standard heart rate variability (HRV) indices identify patients at elevated aggregate risk, they fail to discriminate between these two mechanistically distinct outcomes [1,2]. This limitation is clinically consequential: implantable cardioverter-defibrillator therapy prevents arrhythmic death but not haemodynamic deterioration [7].Cardiac dynamics exhibit complex, multi-scale variability characterised through fractal analysis [3,5]. Self-organised criticality (SOC) offers a unifying framework: healthy physiological systems may operate near a critical point maximising adaptability and information transmission [6]. Shahrbabaki et al. demonstrated that the power-law exponent of nocturnal arrhythmia avalanche durations predicts long-term cardiovascular events, establishing SOC-consistent scaling of arrhythmia episode statistics in clinical populations [16]. Subramanyan has framed sudden circulatory collapse as an avalanche-like failure of compensated cardiac states [17]. At the cellular level, Nivala, Ko et al. showed that self-organisation of calcium sparks near a critical point underlies arrhythmogenic calcium waves in cardiac myocytes, providing a mechanistic cellular substrate for criticality-based arrhythmogenesis [18]. The present work operates at the organ-system level and is intended to complement these perspectives.The present work also connects to the emerging field of Network Physiology, which examines how physiological systems—spanning multiple organs and regulatory sub-systems—interact and coordinate across timescales to maintain health, and how the breakdown of these interactions underlies disease [19,20,21]. The cardiovascular system is a canonical network node in this framework: its scaling dynamics, as captured by DFA-based metrics, reflect the integrated output of autonomic, haemodynamic, and electrophysiological regulatory networks operating across multiple timescales [21]. The ECSoC framework characterises the structural integrity and directional dynamics of this multi-scale output. Pathway heterogeneity in arrhythmic collapse—the central finding of the present study—may therefore reflect heterogeneity in the pattern of cross-system dysregulation preceding arrhythmic events, a hypothesis that warrants investigation in multi-signal datasets incorporating respiratory, blood pressure, and neuroautonomic recordings. The ECSoC framework proposes that characterising the trajectory and structural integrity of the critical regime—rather than static amplitude-based metrics—may provide mechanistically distinct prognostic information. Unlike conventional HRV metrics, ECSoC integrates structural and dynamical features—scaling manifold integrity (R²) and trajectory direction (ΔP(III))—within a unified phase-space representation, enabling discrimination of dynamical pathways that static amplitude metrics cannot resolve.