Noninvasive electrocardiographic risk factors for sudden cardiac death in dilated ca rdiomyopathy: is ambulatory electrocardiography still relevant?

Risk stratification for sudden cardiac death in dilated cardiomyopathy is a field of constant debate, and the currently proposed criteria have been widely questioned due to their low positive and negative predictive value. In this study, we conducted a systematic review of the literature utilizing the PubMed and Cochrane library platforms, in order to gain insight about dilated cardiomyopathy and its arrhythmic risk stratification utilizing noninvasive risk markers derived mainly from 24 h electrocardiographic monitoring. The obtained articles were reviewed in order to register the various electrocardiographic noninvasive risk factors used, their prevalence, and their prognostic significance in dilated cardiomyopathy. Premature ventricular complexes, nonsustained ventricular tachycardia, late potentials on Signal averaged electrocardiography, T wave alternans, heart rate variability and deceleration capacity of the heart rate, all have both some positive and negative predictive value to identify patients in higher likelihood for ventricular arrhythmias and sudden cardiac death. Corrected QT, QT dispersion, and turbulence slope-turbulence onset of heart rate have yet to establish a predictive correlation in the literature. Although ambulatory electrocardiographic monitoring is frequently used in clinical practice in DCM patients, no single risk marker can be used for the selection of patients at high-risk for malignant ventricular arrhythmic events and sudden cardiac death who could benefit from the implantation of a defibrillator. More studies are needed in order to establish a risk score or a combination of risk factors with the purpose of selecting high-risk patients for ICD implantation in the context of primary prevention.

Risk assessment of post-myocardial infarction patients with preserved ejection fraction using 45-min short resting Holter electrocardiographic recordings.

BACKGROUND: Risk stratification for sudden cardiac death in post-myocardial infarction (post-MI) patients remains a challenging task. Several electrocardiographic noninvasive risk factors (NIRFs) have been associated with adverse outcomes and were used to refine risk assessment. This study aimed to evaluate the performance of NIRFs extracted from 45-min short resting Holter ECG recordings (SHR), in predicting ventricular tachycardia inducibility with programmed ventricular stimulation (PVS) in post-MI patients with preserved left ventricular ejection fraction (LVEF). METHODS: We studied 99 post-MI ischemia-free patients (mean age: 60.5 ± 9.5 years, 86.9% men) with LVEF ≥40%, at least 40 days after revascularization. All the patients underwent PVS and a high-resolution SHR. The following parameters were evaluated: mean heart rate, ventricular arrhythmias (premature ventricular complexes, couplets, tachycardias), QTc duration, heart rate variability (HRV), deceleration capacity, heart rate turbulence, late potentials, and T-wave alternans. RESULTS: PVS was positive in 24 patients (24.2%). HRV, assessed by the standard deviation of normal-to-normal R-R intervals (SDNN), was significantly decreased in the positive PVS group (42 ms vs. 51 ms, p = .039). SDNN values <50 ms were also associated with PVS inducibility (OR 3.081, p = .032 in univariate analysis, and 4.588, p = .013 in multivariate analysis). No significant differences were identified for the other NIRFs. The presence of diabetes, history of ST-elevation MI (STEMI) and LVEF <50% were also important predictors of positive PVS. CONCLUSIONS: HRV assessed from SHR, combined with other noninvasive clinical and echocardiographic variables (diabetes, STEMI history, LVEF), can provide an initial, practical, and rapid screening tool for arrhythmic risk assessment in post-MI patients with preserved LVEF.

Atrial High-Rate Episodes in Patients with Devices Without a History of Atrial Fibrillation: a Systematic Review and Meta-analysis.

PURPOSE: Atrial high-rate episodes (AHREs) recorded with cardiac implantable electronic devices (CIEDs) have been associated with the development of clinical atrial fibrillation (AF) and increase in stroke and death risk. We sought to perform a systematic review with a meta-analysis to evaluate the prevalence of AHREs detected by CIEDs, their association with stroke risk, development of clinical AF, and mortality among patients without a documented history of AF. METHODS: We searched several databases, ClinicalTrials.gov, references of reviews, and meeting abstract books without any language restrictions up to 9 September 2020. We studied patients with CIEDs in whom AHREs were detected. Exclusion criterion was AF history. Our primary outcome was the risk of ischemic stroke in patients with AHREs. RESULTS: We deemed eligible eight studies for the meta-analysis enrolling a total of 4322 patients with CIED and without a documented AF history. The overall AHRE incidence ratio was estimated to be 17.56 (95% CI, 8.61 to 35.79) cases per 100 person-years. Evidence of moderate certainty suggests that patients with documented AHREs were 4.45 times (95% CI 2.87-6.91) more likely to develop clinical AF. Evidence of low confidence suggests that AHREs were associated with a 1.90-fold increased stroke risk (95% CI 1.19-3.05). AHREs were not associated with a statistically significant increased mortality risk. CONCLUSION: The present systematic review and meta-analysis demonstrated that among patients without a documented history of AF, the detection of AHREs by CIEDs was associated with significant increased risk of clinical AF and stroke. REGISTRATION NUMBER (DOI): Available in https://doi.org/10.17605/OSF.IO/ZRF6M .

Association of non-invasive electrocardiographic risk factors with left ventricular systolic function in post-myocardial infarction patients with mildly reduced or preserved ejection fraction: Insights from the PRESERVE-EF study.

BACKGROUND: Electrocardiographic non-invasive risk factors (NIRFs) have an important role in the arrhythmic risk stratification of post-myocardial infarction (post-MI) patients with preserved or mildly reduced left ventricular ejection fraction (LVEF). However, their specific relation to left ventricular systolic function remains unclear. We aimed to evaluate the association between NIRFs and LVEF in the patients included in the PRESERVE-EF trial. METHODS: We studied 575 post-MI ischemia-free patients with LVEF≥40% (mean age: 57.0 ± 10.4 years, 86.2% men). The following NIRFs were evaluated: premature ventricular complexes, non-sustained ventricular tachycardia (NSVT), late potentials (LPs), prolonged QTc, increased T-wave alternans, reduced heart rate variability, and abnormal deceleration capacity with abnormal turbulence. RESULTS: There was a statistically significant relationship between LPs (Chi-squared = 4.975; p < .05), nsVT (Chi-squared = 5.749, p < .05), PVCs (r= -.136; p < .01), and the LVEF. The multivariate linear regression analysis showed that LPs (p = .001) and NSVT (p < .001) were significant predictors of the LVEF. The results of the multivariate logistic regression analysis indicated that LPs (OR: 1.76; 95% CI: 1.02-3.05; p = .004) and NSVT (OR: 2.44; 95% CI: 1.18-5.04; p = .001) were independent predictors of the mildly reduced LVEF: 40%-49% versus the preserved LVEF: ≥50%. CONCLUSION: Late potentials and NSVT are independently related to reduced LVEF while they are independent predictors of mildly reduced LVEF versus the preserved LVEF. These findings may have important implications for the arrhythmic risk stratification of post-MI patients with mildly reduced or preserved LVEF.

Noninvasive risk factors for the prediction of inducibility on programmed ventricular stimulation in post-myocardial infarction patients with an ejection fraction ≥40% at risk for sudden cardiac arrest: Insights from the PRESERVE-EF study.

BACKGROUND: In the PRESERVE-EF study, a two-step sudden cardiac death (SCD) risk stratification approach to detect post-myocardial infarction (MI) patients with left ventricle ejection fraction (LVEF) ≥40% at risk for major arrhythmic events (MAEs) was used. Seven noninvasive risk factors (NIRFs) were extracted from a 24-h ambulatory electrocardiography (AECG) and a 45-min resting recording. Patients with at least one NIRF present were referred for invasive programmed ventricular stimulation (PVS) and inducible patients received an Implantable Cardioverter - Defibrillator (ICD). METHODS: In the present study, we evaluated the performance of the NIRFs, as they were described in the PRESERVE-EF study protocol, in predicting a positive PVS. In the PRESERVE-EF study, 152 out of 575 patients underwent PVS and 41 of them were inducible. For the present analysis, data from these 152 patients were analyzed. RESULTS: Among the NIRFs examined, the presence of signal averaged ECG-late potentials (SAECG-LPs) ≥ 2/3 and non-sustained ventricular tachycardia (NSVT) ≥1 eposode/24 h cutoff points were important predictors of a positive PVS study, demonstrating in the logistic regression analysis odds ratios 2.285 (p = .027) and 2.867 (p = .006), respectively. A simple risk score based on the above cutoff points in combination with LVEF < 50% presented high sensitivity but low specificity for a positive PVS. CONCLUSION: Cutoff points of NSVT ≥ 1 episode/24 h and SAECG-LPs ≥ 2/3 in combination with a LVEF < 50% were important predictors of inducibility. However, the final decision for an ICD implantation should be based on a positive PVS, which is irreplaceable in risk stratification.

QT interval extracted from 30-minute short resting Holter ECG recordings predicts mortality in heart failure.

BACKGROUND: Prolonged repolarization duration is a significant total mortality (TM) predictor in post-myocardial infarction patients. AIM: We examined the clinical significance of QT interval that was extracted from a Short Resting Holter ECG (SRH ECG - 30-min duration) as a TM predictor in heart failure (HF) patients. METHODS: One hundred forty-five HF patients (male = 84%, mean age = 64 ± 12 years, mean LVEF = 33 ± 10%) underwent an SRH ECG recording for 30 min. These high-resolution ECG signals were analyzed and the QT interval was calculated and corrected according to the Fridericia formula. After 42.1 months, 26 patients died. RESULTS: Univariate analysis for Deceased and Living groups: QTc:455 ± 33 ms vs 441 ± 32 ms (p = 0.04), LVEF:32 ± 10% vs 34 ± 9% (p < 0.5), Mean Heart Rate: 73 ± 11 bpm vs 69 ± 12 bpm (p = 0.2), SDNN/HRV: 45 ± 42 ms vs 41 ± 29 ms (p = 0.4), QRS: 123 ± 26 ms vs 119 ± 29 ms (p = 0.5). Multivariate Cox regression analysis with model adjusted for QTc, Mean Heart Rate, LVEF, QRS, revealed that QTc-Fridericia interval was an independent TM predictor (H.R.:1.017, 95% C.I.: 1.003-1.030, p = 0.01). The cut-off point of 490 ms (90th percentile) in the same model presented HR: 2.9 for TM (95%C.I.: 1.066-7.882, p = 0.03). Kaplan Meier curves depicted a clear difference in survival between the two patients' groups (QTc Group≥490 ms vs QTc Group <490 ms). The curve diverge was important (log-rank, p = 0.02). CONCLUSION: A fast risk stratification approach with SRH ECG recording is an efficient method for flash evaluation of mortality risk in HF patients.

Syncope associated with supraventricular tachycardia: Diagnostic role of implantable loop recorders.

Syncope represents a relatively uncommon symptom of supraventricular tachycardia (SVT). It is likely that an impaired autonomic vasomotor response to the hemodynamic stress of tachycardia is the determinant of hemodynamic changes leading to cerebral hypoperfusion and syncope. In this regard, tilt-table test may detect abnormalities in the autonomic nervous function and predict the occurrence of syncope during SVT. Electrophysiology studies may reproduce the SVT, distinguish it from other life-threatening ventricular tachyarrhythmias, and exclude other causes of syncope. Not infrequently mixed syncope mechanisms are revealed during the above diagnostic workup raising doubts about the operating mechanism in the clinical setting. In such cases of uncertainty, an implantable loop recorder, providing long-term cardiac monitoring, may play a pivotal role in the establishment of the diagnosis, confirming the association of an arrhythmic event with the symptom. Herein, we present four such cases with recurrent unexplained syncope finally attributed to paroxysmal SVT guiding them to a potentially radical treatment through radiofrequency catheter ablation.

Temporal changes of noninvasive electrocardiographic risk factors for sudden cardiac death in post-myocardial infarction patients with preserved ejection fraction: Insights from the PRESERVE-EF study.

BACKGROUND: Several noninvasive risk factors (NIRFs) have been proposed for sudden cardiac death risk stratification in post-myocardial infarction (post-MI) patients with preserved ejection fraction (EF). However, it remains unclear if these factors change over time. METHODS: We evaluated seven electrocardiographic NIRFs as they were described in the PRESERVE-EF trial in 80 post-MI patients with EF ≥ 40%, at least 40 days after revascularization and 1 year later. RESULTS: Mean patient age was 56 ± 10 years, and 88% were men. Mean EF was 50 ± 5%. The prevalence of (a) positive late potentials (27.5% vs. 28.8%, p = .860), (b) >30 premature ventricular complexes/hour (8.8% vs. 11.3%, p = .598), (c) nonsustained ventricular tachycardia (8.8% vs. 5%, p = .349), (d) standard deviation of normal RR intervals <75 ms (3.8% vs. 3.8%, p = 1.000), (e) QTc derived from 24-hr electrocardiography >440 ms (men) or >450 ms (women) (17.5% vs. 17.5%, p = 1.000), (f) deceleration capacity ≤4.5 ms and heart rate turbulence onset ≥0% and slope ≤2.5 ms (2.5% vs. 3.8%. p = 1.000), and (g) ambulatory T-wave alternans ≥65 µV in two Holter channels (6.3% vs. 6.3%, p = 1.000) were similar between the two measurements. However, five patients (6.3%) without any NIRFs during the first assessment had at least one positive NIRF at the second assessment and six patients (7.5%) with at least one NIRF at baseline had no positive NIRFs at 1 year. CONCLUSIONS: While the prevalence of the examined electrocardiographic NIRFs between the two examinations was similar on a population basis, some patients without NIRFs at baseline developed NIRFs at 1 year and vice versa, highlighting the need for risk factor reassessment during follow-up.

Arrhythmic risk stratification in post-myocardial infarction patients with preserved ejection fraction: the PRESERVE EF study.

AIMS: Sudden cardiac death (SCD) annual incidence is 0.6-1% in post-myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF)≥40%. No recommendations for implantable cardioverter-defibrillator (ICD) use exist in this population. METHODS AND RESULTS: We introduced a combined non-invasive/invasive risk stratification approach in post-MI ischaemia-free patients, with LVEF ≥ 40%, in a multicentre, prospective, observational cohort study. Patients with at least one positive electrocardiographic non-invasive risk factor (NIRF): premature ventricular complexes, non-sustained ventricular tachycardia, late potentials, prolonged QTc, increased T-wave alternans, reduced heart rate variability, abnormal deceleration capacity with abnormal turbulence, were referred for programmed ventricular stimulation (PVS), with ICDs offered to those inducible. The primary endpoint was the occurrence of a major arrhythmic event (MAE), namely sustained ventricular tachycardia/fibrillation, appropriate ICD activation or SCD. We screened and included 575 consecutive patients (mean age 57 years, LVEF 50.8%). Of them, 204 (35.5%) had at least one positive NIRF. Forty-one of 152 patients undergoing PVS (27-7.1% of total sample) were inducible. Thirty-seven (90.2%) of them received an ICD. Mean follow-up was 32 months and no SCDs were observed, while 9 ICDs (1.57% of total screened population) were appropriately activated. None patient without NIRFs or with NIRFs but negative PVS met the primary endpoint. The algorithm yielded the following: sensitivity 100%, specificity 93.8%, positive predictive value 22%, and negative predictive value 100%. CONCLUSION: The two-step approach of the PRESERVE EF study detects a subpopulation of post-MI patients with preserved LVEF at risk for MAEs that can be effectively addressed with an ICD. CLINICALTRIALS.GOV IDENTIFIER: NCT02124018.

T wave alternans extracted from 30-minute short resting Holter ECG recordings predicts mortality in heart failure.

BACKGROUND: We extracted T Wave Alternans (TWA) from a 30 minute Short Resting Holter ECG (SRH ECG) in the supine position, as a Total Mortality (TM) predictor in Heart Failure (HF). METHODS: Signals from 146 HF patients (LVEF = 33 ±â€¯10%), were analyzed with Modified Moving Average method. After 42.1 months, 26 patients died. RESULTS: (Deceased vs Living group): TWA:31 ±â€¯18 µV vs 25 ±â€¯13 µV(p = 0.05), LVEF:32 ±â€¯10% vs 34 ±â€¯9% (p = 0.5), Heart Rate:73 ±â€¯11 bpm vs 69 ±â€¯12 bpm (p = 0.2), SDNN/HRV:45 ±â€¯42 ms vs 41 ±â€¯29 ms (p = 0.4), QRS:123 ±â€¯26 ms vs 119 ±â€¯29 ms (p = 0.5).Cox regression model adjusted for TWA, LVEF and QRS, revealed that the TWA was an independent TM predictor (H.R.: 1.022, 95% C.I.: 0.999-1.046, p = 0.05).The TWA ≥ 42 µV demonstrated HR: 2.521, (95% C.I.: 0.982-6.472, p = 0.05). CONCLUSIONS: In severely affected HF patients, TWA from a SRH ECG may be present even during slow resting heart rates and proved to be an important and independent TM predictor. The SRH ECG recording is an efficient and fast method for mortality risk evaluation in HF patients.

Blunted cyclic variation of heart rate predicts mortality risk in post-myocardial infarction, end-stage renal disease, and chronic heart failure patients.

AIMS: Cyclic variation of heart rate (CVHR) associated with sleep-disordered breathing is thought to reflect cardiac autonomic responses to apnoeic/hypoxic stress. We examined whether blunted CVHR observed in ambulatory ECG could predict the mortality risk. METHODS AND RESULTS: CVHR in night-time Holter ECG was detected by an automated algorithm, and the prognostic relationships of the frequency (FCV) and amplitude (ACV) of CVHR were examined in 717 patients after myocardial infarction (post-MI 1, 6% mortality, median follow-up 25 months). The predictive power was prospectively validated in three independent cohorts: a second group of 220 post-MI patients (post-MI 2, 25.5% mortality, follow-up 45 months); 299 patients with end-stage renal disease on chronic haemodialysis (ESRD, 28.1% mortality, follow-up 85 months); and 100 patients with chronic heart failure (CHF, 35% mortality, follow-up 38 months). Although CVHR was observed in ≥96% of the patients in all cohorts, FCV did not predict mortality in any cohort. In contrast, decreased ACV was a powerful predictor of mortality in the post-MI 1 cohort (hazard ratio [95% CI] per 1 ln [ms] decrement, 2.9 [2.2-3.7], P < 0.001). This prognostic relationship was validated in the post-MI 2 (1.8 [1.4-2.2], P < 0.001), ESRD (1.5 [1.3-1.8], P < 0.001), and CHF (1.4 [1.1-1.8], P = 0.02) cohorts. The prognostic value of ACV was independent of age, gender, diabetes, ß-blocker therapy, left ventricular ejection fraction, sleep-time mean R-R interval, and FCV. CONCLUSION: Blunted CVHR detected by decreased ACV in a night-time Holter ECG predicts increased mortality risk in post-MI, ESRD, and CHF patients.

Deceleration Capacity of Heart Rate Predicts Arrhythmic and Total Mortality in Heart Failure Patients.

BACKGROUND: Deceleration capacity (DC) of heart rate proved an independent mortality predictor in postmyocardial infarction patients. The original method (DCorig) may produce negative values (9% in our analyzed sample). We aimed to improve the method and to investigate if DC also predicts the arrhythmic mortality. METHODS: Time series from 221 heart failure patients was analyzed with DCorig and a new variant, the DCsgn, in which decelerations are characterized based on windows of four consecutive beats and not on anchors. After 41.2 months, 69 patients experienced sudden cardiac death (SCD) surrogate end points, while 61 died. RESULTS: (SCD+ vs SCD-group) DCorig: 3.7 ± 1.6 ms versus 4.6 ± 2.6 ms (P = 0.020) and DCsgn: 4.9 ± 1.7 ms versus 6.1 ± 2.2 ms (P < 0.001). After Cox regression (gender, age, left ventricular ejection fraction, filtered QRS, NSVT≥1/24h, VPBs≥240/24h, mean 24-h QTc, and each DC index added on the model separately), DCsgn (continuous) was an independent SCD predictor (hazard ratio [H.R.]: 0.742, 95% confidence intervals (C.I.): 0.631-0.871, P < 0.001). DCsgn ≤ 5.373 (dichotomous) presented 1.815 H.R. for SCD (95% C.I.: 1.080-3.049, P = 0.024), areas under curves (AUC)/receiver operator characteristic (ROC): 0.62 (DCorig) and 0.66 (DCsgn), P = 0.190 (chi-square). Results for deceased versus alive group: DCorig: 3.2 ± 2.0 ms versus 4.8 ± 2.4 ms (P < 0.001) and DCsgn: 4.6 ± 1.4 ms versus 6.2 ± 2.2 ms (P < 0.001). In Cox regression, DCsgn (continuous) presented H.R.: 0.686 (95% C.I. 0.546-0.862, P = 0.001) and DCsgn ≤ 5.373 (dichotomous) presented an H.R.: 2.443 for total mortality (TM) (95% C.I. 1.269-4.703, P = 0.008). AUC/ROC: 0.71 (DCorig) and 0.73 (DCsgn), P = 0.402. CONCLUSIONS: DC predicts both SCD and TM. DCsgn avoids the negative values, improving the method in a nonstatistical important level.

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning.

Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic condition exhibits a growing prevalence and entails substantial healthcare expenditures, with anticipated escalation in the future. It is essential to classify HF patients into three groups based on their ejection fraction: reduced (HFrEF), mid-range (HFmEF), and preserved (HFpEF), such as for diagnosis, risk assessment, treatment choice, and the ongoing monitoring of heart failure. Nevertheless, obtaining a definitive prediction poses challenges, requiring the reliance on echocardiography. On the contrary, an electrocardiogram (ECG) provides a straightforward, quick, continuous assessment of the patient's cardiac rhythm, serving as a cost-effective adjunct to echocardiography. In this research, we evaluate several machine learning (ML)-based classification models, such as K-nearest neighbors (KNN), neural networks (NN), support vector machines (SVM), and decision trees (TREE), to classify left ventricular ejection fraction (LVEF) for three categories of HF patients at hourly intervals, using 24-hour ECG recordings. Information from heterogeneous group of 303 heart failure patients, encompassing HFpEF, HFmEF, or HFrEF classes, was acquired from a multicenter dataset involving both American and Greek populations. Features extracted from ECG data were employed to train the aforementioned ML classification models, with the training occurring in one-hour intervals. To optimize the classification of LVEF levels in coronary artery disease (CAD) patients, a nested cross-validation approach was employed for hyperparameter tuning. HF patients were best classified using TREE and KNN models, with an overall accuracy of 91.2% and 90.9%, and average area under the curve of the receiver operating characteristics (AUROC) of 0.98, and 0.99, respectively. Furthermore, according to the experimental findings, the time periods of midnight-1 am, 8-9 am, and 10-11 pm were the ones that contributed to the highest classification accuracy. The results pave the way for creating an automated screening system tailored for patients with CAD, utilizing optimal measurement timings aligned with their circadian cycles.

Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.

BACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information. METHODS: In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information. RESULTS: Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage. CONCLUSIONS: The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare.