Survival Predictors of Heart Rate Variability After Myocardial Infarction With and Without Low Left Ventricular Ejection Fraction

Abstract

BackgroundHeart rate variability (HRV) and heart rate (HR) dynamics are used to predict the survival probability of patients after acute myocardial infarction (AMI), but the association has been established in patients with mixed levels of left ventricular ejection fraction (LVEF).ObjectiveWe investigated whether the survival predictors of HRV and HR dynamics depend on LVEF after AMI.MethodsWe studied 687 post-AMI patients including 147 with LVEF ≤35% and 540 with LVEF >35%, of which 23 (16%) and 22 (4%) died during the 25 month follow-up period, respectively. None had an implanted cardioverter-defibrillator. From baseline 24 h ECG, the standard deviation (SDNN), root mean square of successive difference (rMSSD), percentage of successive difference >50 ms (pNN50) of normal-to-normal R-R interval, ultra-low (ULF), very-low (VLF), low (LF), and high (HF) frequency power, deceleration capacity (DC), short-term scaling exponent (α1), non-Gaussianity index (λ25s), and the amplitude of cyclic variation of HR (Acv) were calculated.ResultsThe predictors were categorized into three clusters; DC, SDNN, α1, ULF, VLF, LF, and Acv as Cluster 1, λ25s independently as Cluster 2, and rMSSD, pNN50, and HF as Cluster 3. In univariate analyses, mortality was best predicted by indices belonging to Cluster 1 regardless of LVEF. In multivariate analyses, however, mortality in patients with low LVEF was best predicted by the combinations of Cluster 1 predictors or Cluster 1 and 3 predictors, whereas in patients without low LVEF, it was best predicted by the combinations of Cluster 1 and 2 predictors.ConclusionThe mortality risk in post-AMI patients with low LVEF is predicted by indices reflecting decreased HRV or HR responsiveness and cardiac parasympathetic dysfunction, whereas in patients without low LVEF, the risk is predicted by a combination of indices that reflect decreased HRV or HR responsiveness and indicator that reflects abrupt large HR changes suggesting sympathetic involvement.

Revisiting Left Ventricular Ejection Fraction Levels: A Circadian Heart Rate Variability-Based Approach

“…Additionally, [58] reported similar findings of noninvasive autonomic features identifying patients with increased risk of hospitalization due to heart failure among clinically stable patients with left ventricular systolic dysfunction, even when adjusting for other clinical parameters including medication. The utility or association of HRV with heart failure is further highlighted by [59], who reported that the mortality risk in post acute myocardial infarction patients with low LVEF is predicted by indices reflecting decreased HRV or heart rate responsiveness and cardiac parasympathetic dysfunction, whereas in patients without low LVEF, the risk is predicted by a combination of indices that reflect decreased HRV or heart rate responsiveness and indicator that reflects abrupt large heart rate changes suggesting sympathetic involvement [58]. The high accuracy obtained in the current work (> 70% GVF) for categorization of heart failure as well as the statistically significant differences between heart failure categories at known time periods (late-night to early morning and evening) for increased risk of heart attack indicate the importance of this approach.…”

“…Several studies have observed a strong association between HRV and cardiovascular diseases including CAD [15]- [21]. Specific cardiac function characteristics observed using different HRV features at certain times of the day/night cycle could suggest changes in cardiac function associated with heart failure progression in parallel with circadian rhythm changes.…”

Is the Median Hourly Ambulatory Heart Rate Range Helpful in Stratifying Mortality Risk among Newly Diagnosed Atrial Fibrillation Patients?

“…However, the utilization of AF instrumental monitors on the outcome prediction of AF is lacking. Heart rate variability (HRV) enables quantification of cardiac regulatory influences on the autonomic function and predicts the prognosis in patients with CHF, myocardial infarction (MI), and AF [6,[9][10][11][12]. HRV can be improved following intervention and is associated with better outcomes [13,14].…”

“…The hourly analysis may reflect quite different aspects of patients' conditions, so we proposed that AHRR 24hr may be more indicative among AF patients since the heart rate may fluctuate during several short periods [22]. HRV-related parameters were also examined among CHF patients with AF and myocardial infarction patients [12,16]. The feasibility of using HRV among patients with incident AF has not been tested, and the comparisons between HRV parameters and clinical scores are less reported.…”

Screening Cardiovascular Autonomic Neuropathy in Diabetic Patients With Microvascular Complications Using Machine Learning: A 24-Hour Heart Rate Variability Study

“…In the literature, a number of previous studies focused on the use of HRV for the analysis of cardiovascular pathologies [15]- [18]. Furthermore, several studies highlighted the association between HRV and diabetes using data analysis tools [19]- [21] and machine or deep learning algorithms [22]- [25].…”