Heartbeat Chronicles: Decoding the Interplay of Echocardiography and Heart Rate Variability in Chronic Heart Failure Patients – Unraveling the Mysteries with Traditional and Advanced 24-Hour Holter ECG Parameters

Heart failure transcends a mere diagnostic label, representing a multifaceted clinical syndrome arising from underlying structural and/ or functional abnormality within the heart. This syndrome manifests as elevated intracardiac pressures and/or compromised cardiac output during periods of both rest and exertion. Chronic heart failure (CHF) categorizes individuals who either present with a confirmed diagnosis of heart failure or exhibit a gradual progression of symptoms over time. This distinction underscores the heterogeneous nature of heart failure, wherein diverse etiologies and clinical presentations converge under a unified syndrome characterized by impaired cardiac function and hemodynamic derangements [1–4].

Clinically, CHF manifests as a constellation of symptoms encompassing dyspnea, fatigue, peripheral edema, and exercise intolerance. The New York Heart Association (NYHA) functional classification system stratifies the severity of heart failure based on its impact on physical activity [5,6]. Concurrently, alongside the NYHA functional classification, which gauges heart failure severity in accordance with symptomatic presentation, alternative prognostic markers such as N-terminal pro-B-type natriuretic peptides (NTproBNP) assume a pivotal role [7].

In the context of CHF, echocardiography serves as a valuable tool for elucidating cardiac abnormalities, facilitating in-depth evaluations of disease severity, and providing guidance for therapeutic decision-making [1,8]. An integral facet of echocardiographic scrutiny in CHF entails the evaluation of left ventricular function. Parameters such as left ventricular ejection fraction (LVEF), fractional shortening, and the identification of wall motion abnormalities are systematically measured to assess myocardial contractility and detect any impairment. Reduced LVEF is a prevalent observation in CHF and frequently serves as a basis for risk stratification and therapeutic strategizing [8,9]. Tissue Doppler imaging, as a technique, enables the quantification of myocardial velocities and assessment of diastolic function, thereby offering additional insights into the ventricles’ relaxation properties [9].

While imaging modalities elucidate structural changes indicative of myocardial substrate, Holter monitoring assumes a complementary role by providing insights into both myocardial vulnerability and autonomic nervous system dynamics [10–12]. Holter electrocardiography (ECG) offers a means to examine heart rate variability (HRV), which serves as a reflection of the autonomic nervous system’s impact on cardiac function. The association between reduced HRV in CHF and heightened susceptibility to arrhythmias, increased ventricular remodeling, and an elevated risk of sudden cardiac death has been established [13,14]. Furthermore, HRV emerges as a valuable tool for risk stratification in CHF patients, assisting clinicians in the identification of individuals at a heightened risk of adverse outcomes [14–18].

Within the realm of CHF, recently introduced HRV parameters—namely Acceleration Capacity (AC), Deceleration Capacity (DC), and the Triangular Index (TI)—have emerged as notable contributors to our comprehension of autonomic function. These non-traditional HRV parameters provide a more nuanced insight into autonomic function, surpassing the scope of traditional metrics [13,18–20]. Their evaluation in CHF augments our capability to comprehensively appraise sympathovagal balance and cardiovascular adaptability. AC, DC, and TI exhibit promise as valuable tools for risk stratification and guiding individualized therapeutic interventions in individuals with CHF [20,21]. As research in HRV progresses, these parameters may evolve into integral components of clinical assessments in CHF, thereby refining our approach to understanding and managing this intricate condition. The integrated assessment between echocardiography and HRV facilitates a more personalized and nuanced strategy in the management of CHF, considering both cardiac structure and autonomic regulation [17,19,22].

Echocardiography assumes an indispensable role in the comprehensive management of CHF, given its noninvasive nature, wide accessibility, and real-time information provision. Echocardiography is crucial not only for the initial diagnosis and characterization of CHF but also for monitoring disease progression and assessing the response to therapeutic interventions. This necessitates the confluence of clinical symptoms or signs indicative of CHF alongside objective evidence of cardiac dysfunction [1].

While continuous ambulatory Holter ECG monitoring has traditionally been considered secondary in heart failure diagnosis, its evolving understanding reveals its potential as a valuable tool for investigating the intricate factors contributing to the mechanisms of sudden death. Thus, continuous ambulatory Holter ECG monitoring, often downplayed in conventional wisdom, plays a pivotal role in risk stratification [25–27].

Our investigation results indicate that specific echocardiographic parameters in CHF patients lead to an augmentation of certain less conventional Holter ECG parameters related to HRV, even as other parameters maintain values within the normal range. Within the context of our study, statistically significant deviations from normal values are observed for both the TI and the acceleration and deceleration capacity concerning certain echocardiographic parameters.

As previously delineated, among the echocardiographic parameters investigated, we observed a more pronounced and statistically significant correlation with DC compared to AC. The rationale underlying the potential predilection for DC to be affected before AC in certain circumstances may be attributed to the dynamic characteristics inherent in the autonomic nervous system response, influenced by various factors [28–31].

Nevertheless, as our results have shown, a weak correlation among those HRV parameters may stem from multifaceted determinants inherent within physiological, pathological, and environmental domains. These factors encompass a broad spectrum of influences, ranging from intrinsic autonomic modulation to extrinsic perturbations, each exerting variable effects on HRV metrics. Consequently, the presence of uncontrolled or heterogeneously distributed factors across the study cohort can attenuate the strength of associations observed among HRV parameters [32]. Moreover, HRV parameters encapsulate distinct facets of autonomic nervous system dynamics and cardiac physiology. Variations in their physiological underpinnings, including differential sensitivity to parasympathetic and sympathetic modulation, contribute to the nuanced landscape of inter-parameter correlations [33]. Consequently, weak associations between HRV metrics may reflect the intricate interplay between divergent autonomic branches and broader physiological mechanisms. Methodological considerations further underscore the complexity inherent in HRV research [32,34]. Variability in data acquisition methodologies, signal processing algorithms, and parameter selection can introduce inherent variability and systematic biases, thereby influencing the observed correlations between HRV parameters. Additionally, demographic heterogeneity within study cohorts, encompassing age, gender, prevalent comorbidities, and pharmacological interventions, introduces confounding influences that impinge upon the interpretative clarity of HRV data [32–34].

Concerning one of the widely utilized echocardiographic parameters, namely LVEF, our investigation has revealed a statistically significant association with RMSSD and PNN50. Elevated RMSSD values generally correlate with heightened parasympathetic tone and improved autonomic balance. Concurrently, PNN50 serves as an indicator of parasympathetic activity, with increased values signifying heightened parasympathetic tone. The positive correlation identified between LVEF and both RMSSD and PNN50 suggests a potential link wherein individuals exhibiting superior cardiac function (indicated by higher LVEF values) also demonstrate heightened parasympathetic activity (indicated by higher RMSSD and PNN50 values) [30,32,35]. However, while LVEF and both RMSSD and PNN50 parameters offer insights into cardiac performance, their disparate physiological underpinnings suggest that they may not consistently exhibit strong associations, as LVEF serves as an indicator of left ventricular contractile function, while Holter ECG parameters primarily reflect autonomic nervous system activity and its impact on short-term HRV. A weak association can be ascribed to the presence of confounding variables, such as age, medication usage, comorbidities, and physiological states, which could contribute to the attenuated correlation observed between RMSSD and LVEF. Pharmacological interventions affecting heart rate variability or myocardial contractility, for instance, may introduce variability in the relationship between these parameters [32,33,35]. In alignment with our findings, Alkhodari et al. reported a positive correlation between HRV parameters and LVEF, underscoring the significance of circadian 24-hour cycle analysis [36]. This suggests that compromised cardiac function might coincide with alterations in autonomic nervous system equilibrium [32–34]. Corroborating our study, Birand et al. observed a positive and significant correlation between the parasympathetic band of HRV and LVEF, reinforcing our findings (p < 0.0001) [37].

Concerning diastolic function, our investigation has revealed a positive correlation between the E/A ratio and TI, AC, and DC. Additionally, a positive correlation has been identified between the E/E’ ratio and TI. This indicates a potential connection between variations in diastolic function and the dynamics of blood flow during diastole, providing valuable insights into the hemodynamics, mechanics, and autonomic nervous system of the heart during the relaxation phase [38,39]. However, while both metrics relate to cardiac function, they represent distinct physiological aspects, and their correlations may be influenced by differential regulatory mechanisms. E/A ratio and HRV parameters may exhibit dynamic fluctuations over time, influenced by acute physiological changes or short-term interventions. As such, cross-sectional assessments may not fully capture the transient nature of these metrics, potentially leading to weaker associations in certain contexts [39,40].

The correlation between HRV and E/e’ (whether measured at the septal or lateral site) may be influenced by diverse physiological and clinical factors. The septal region, with its closer anatomical and functional relationship with the cardiac conduction system and autonomic nerve fibers, may be more directly linked to autonomic modulation, leading to a more robust correlation with HRV [38–40]. Conversely, the lateral annulus, influenced by the left ventricular free wall, may possess distinct mechanical and sympathetic innervation characteristics, impacting the relationship between E/e’ lateral and HRV. However, the weak correlation in our study between HRV and E/e’ (at either septal or lateral sites) can be influenced by specific clinical conditions of the patients under study. Pathological changes such as myocardial ischemia or fibrosis may impact the correlation differently at the septal and lateral annuli. Another echocardiographic parameter related to diastolic function that demonstrated a statistically significant correlation with TI, AC, and DC is MV Dec T [38–41].

The final significant echocardiographic parameter demonstrating a correlation with HRV parameters is the LAVI. Heightened parasympathetic activity, as indicated by elevated RMSSD and PNN50, may engender increased HRV, potentially influencing left atrial filling and size [41,42]. However, it is imperative to note that vagal tone, represented by higher RMSSD, directly affects the heart, including the atria. Elevated parasympathetic activity fosters atrial relaxation and impacts atrial compliance. Shifts in atrial function, orchestrated by autonomic modulation, may consequently contribute to alterations in left atrial volume over time [8,9,42].

The established correlation bears clinical significance, as changes in left atrial volume can serve as indicative markers for various cardiovascular conditions, encompassing atrial fibrillation, heart failure, or other structural heart abnormalities. Parsi et al. employed ECGs in their study to predict the presence of paroxysmal atrial fibrillation [43]. Analogously, comparable scoring systems utilizing HRV parameters derived from Holter ECG investigations could be applied on a broader scale, particularly in patients exhibiting no paroxysmal atrial fibrillation on standard ECG assessments [42,43].

Crucially, these alterations of AC, DC, and TI were not discernible in other frequently employed parameters assessing HRV. However, various comorbidities, such as diabetes, CKD, or obesity, possess the capacity to exert influence on the dysregulation of the autonomic nervous system, consequently impacting the parameters of HRV [44]. The disruption of the autonomic nervous system, in turn, perturbs the electrical stability of the heart, potentially leading to alterations in HRV. Perturbations in blood glucose levels, disruptions in electrolyte balance, chronic inflammatory processes, oxidative stress, insulin resistance, and the presence of metabolic syndrome further contribute to disturbances in cardiac rhythm. It is noteworthy that diabetes can engender structural and functional changes in cardiac tissue, a condition commonly referred to as diabetic cardiomyopathy [44,45]. CKD, characterized by the accumulation of uremic toxins within the body, engenders a myriad of repercussions for the cardiovascular system. These toxins have the capacity to stimulate myocardial fibrosis, incite inflammatory responses, and induce oxidative stress, collectively influencing the electrical properties of the heart [44,46]. These alterations collectively foster an environment conducive to the development of arrhythmogenic tendencies within the cardiac milieu [44–46]. Given the statistically significant nature of these identified parameters, which have not undergone exhaustive exploration, we advocate for subsequent investigations to systematically examine these parameters, also in relation to different comorbidities and echocardiographic parameters. This recommendation is rooted in the potential of these parameters to function as early indicators of autonomic nervous system dysregulation, facilitating timely identification and intervention in individuals affected by such dysregulation.

As previously outlined, all participants underwent supervised therapy for heart failure, inclusive of beta-blockade, recognized as a primary determinant influencing HRV. Prospective investigations, encompassing diverse beta-blockers and their respective dosages, are imperative to ascertain not only their direct impact on HRV parameters but also to elucidate potential interactions between different beta-blockers and HRV. Such comprehensive exploration is essential for refining our understanding of the nuanced effects of beta-blockade on HRV across various patient populations and clinical contexts. Additionally, considering the wide range of beta-blockers available, including those with varying pharmacokinetic and pharmacodynamic profiles, a thorough investigation can provide valuable insights into optimizing treatment strategies for heart failure while minimizing adverse effects on HRV. Furthermore, forthcoming research endeavors should explore the potential influence of environmental factors, lifestyle choices, and habits such as smoking or alcohol consumption on HRV parameters, with a view toward extending current understanding. Concerning the etiology of HF within our cohort, all individuals presented with ischemic origins, confirmed via coronary angiography, revealing lesions exceeding 75% in at least one coronary artery. Nonetheless, it is noteworthy that some patients may concurrently manifest alcohol-induced cardiomyopathy. Hence, future studies should contemplate diverse etiological pathways to comprehensively investigate their implications on HRV.

While our study did not assess long-term adverse cardiovascular outcomes, numerous investigations have established their prognostic capacity for significant adverse cardiovascular events [28–30,34,40]. To address this limitation, we aim to integrate data on long-term cardiovascular events and extend our follow-up in future investigations.

The application of Holter ECG monitoring and echocardiography in individuals with CHF holds the potential not only to enhance risk stratification but also to inform early therapeutic interventions, thereby contributing to an improvement in overall quality of life. LVEF assumes paramount significance in the decision-making process regarding eligibility for defibrillator implantation, with LVEF values at or below a commonly accepted threshold of 35% serving as an indication for primary prevention. However, risk assessment in this context is characterized by its multifactorial nature. Guidelines extend considerations beyond LVEF, encompassing additional variables such as the NYHA functional class, NT-proBNP levels, age, comorbidities, and medication usage. Moreover, the underlying etiology of heart failure, whether ischemic or non-ischemic, plays a pivotal role in prognosis, with ischemic heart disease potentially associated with an elevated risk of adverse events [1].

In terms of future perspectives, the application of machine learning (ML) algorithms holds promise in the analysis of extensive datasets comprising echocardiographic images and HRV measurements. These algorithms possess the capability to discern subtle patterns and associations that may elude human observers, thereby advancing diagnostic capabilities and enhancing risk prediction [47,48]. There exists the potential to develop predictive models utilizing echocardiographic parameters and HRV data, enabling the assessment of an individual’s cardiovascular disease risk and facilitating early intervention through personalized treatment plans. Additionally, ML algorithms may be leveraged to forecast episodes of decompensation or exacerbation in CHF [49]. Timely detection of impending heart failure decompensation could facilitate interventions, potentially reducing hospitalizations and enhancing patient outcomes [49–51].

Furthermore, ML models can analyze longitudinal changes in HRV and echocardiographic parameters, offering insights into treatment responses. This continuous monitoring approach enables clinicians to tailor interventions based on individual patient responses, thereby optimizing therapeutic strategies [47,50]. Lastly, ML technologies stand poised to support the remote monitoring of CHF patients via wearable devices capable of capturing real-time HRV data. The integration of machine learning with such wearable devices holds the potential to provide continuous and non-invasive insights into cardiovascular health [52,53].

The principal constraint arises from the monolithic nature of the study’s singular-center framework and the comparatively limited enrollment of patients. Nevertheless, it is imperative to underscore the scrupulous application of comprehensive exclusion criteria designed to scrutinize HRV parameters while mitigating the influence of concurrent comorbidities. Additional investigations are imperative to delineate forthcoming trajectories that integrate a more comprehensive population, encompassing varied comorbidities, with adherence to exclusionary parameters, thereby facilitating a nuanced understanding of their potential implications on HRV. Moreover, broadening the scope of the investigation to encompass a more expansive patient cohort would facilitate the implementation of multivariable regression analysis and the formulation of a multi-parameter risk stratification score. The statistical methodology employed encompassed HRV variables and echocardiographic parameters. Future investigations should employ more advanced statistical models, integrating potential confounding factors such as NTproBNP or other biomarkers. Furthermore, longitudinal studies focusing on the enduring impact of these HRV parameters on the studied patient cohort constitute a focal point of our forthcoming investigation. Additionally, the primary endpoint for future research endeavors will center on the longitudinal trajectory, specifically examining whether these parameters correlate with in-hospital mortality rates, incidences of hospital readmission, or the development of various atrial or ventricular arrhythmias. Significantly, the omission of patients with heart failure with preserved ejection fraction necessitates exploration in subsequent research endeavors. Finally, it is imperative to accentuate that our study cohort, predominantly comprising patients with chronic heart failure, spans diverse disease stages, introducing heterogeneity that demands consideration in forthcoming inquiries.

Our study aimed to broaden the clinical applicability of HRV measured through a 24-hour Holter ECG in individuals with CHF and explore its correlation with echocardiographic parameters. Holter ECG parameters, including acceleration and deceleration capacity, as well as the triangular index, represent fewer mainstream metrics within the realm of cardiac monitoring. Despite their relatively limited utilization, these metrics exhibit considerable potential as focal points for in-depth investigation in forthcoming research pursuits. The incorporation of these parameters into future studies could hold promise for elucidating novel aspects of cardiac function and autonomic regulation, thereby enriching our understanding of cardiovascular physiology and pathophysiology.

It is important to highlight that well-established HRV parameters such as SDNN, SDNN index, and SDANN did not exhibit discernible associations with echocardiographic parameters. Conversely, non-traditional parameters displayed statistically significant correlations, hinting at a potential influence of heart failure medications on conventional HRV parameters and necessitating further exploration.

These findings underscore the importance of integrating non-traditional HRV parameters into the risk stratification process for CHF patients, proposing a transformative approach to utilizing these parameters as prognostic tools across diverse patient populations. However, it is crucial to underscore the need for comprehensive multicenter studies to validate the long-term prognostic implications of these findings.