Impact of QRS misclassifications on heart-rate-variability parameters (results from the CARLA cohort study).

BACKGROUND: Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies. The aim of this study was therefore to evaluate MEANS for its ability to detect non-sinus ECG beats and artifacts and to compare HRV parameters in relation to ECG processing. Additionally, we analyzed how ECG processing affects the statistical association of HRV with cardiovascular disease (CVD) risk factors. METHODS: 20-min ECGs from 1,674 subjects of the population-based CARLA study were available for HRV analysis. All ECGs were processed with the ECG computer program MEANS. A reference standard was established by experienced clinicians who visually inspected the MEANS-processed ECGs and reclassified beats if necessary. HRV parameters were calculated for 5-minute segments selected from the original 20-minute ECG. The effects of misclassified typified normal beats on i) HRV calculation and ii) the associations of CVD risk factors (sex, age, diabetes, myocardial infarction) with HRV were modeled using linear regression. RESULTS: Compared to the reference standard, MEANS correctly classified 99% of all beats. The averaged sensitivity of MEANS across all ECGs to detect non-sinus beats was 76% [95% CI: 74.1;78.5], but for supraventricular extrasystoles detection sensitivity dropped to 38% [95% CI: 36.8;38.5]. Time-domain parameters were less affected by false sinus beats than frequency parameters. Compared to the reference standard, MEANS resulted in a higher SDNN on average (mean absolute difference 1.4ms [95% CI: 1.0;1.7], relative 4.9%). Other HRV parameters were also overestimated as well (between 6.5 and 29%). The effect estimates for the association of CVD risk factors with HRV did not differ between the editing methods. CONCLUSION: We have shown that the use of the automated MEANS algorithm may lead to an overestimation of HRV due to the misclassification of non-sinus beats, especially in frequency domain parameters. However, in population-based studies, this has no effect on the observed associations of HRV with risk factors, and therefore an automated ECG analyzing algorithm as MEANS can be recommended here for the determination of HRV parameters.

Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients.

Non-traumatic chest pain is a frequent reason for an urgent ambulance visit of a patient by the emergency medical services (EMS). Chest pain (or chest pain-equivalent symptoms) can be innocent, but it can also signal an acute form of severe pathology that may require prompt intervention. One of these pathologies is cardiac ischemia, resulting from a disbalance between blood supply and demand. One cause of a diminished blood supply to the heart is acute coronary syndrome (ACS, i.e., cardiac ischemia caused by a reduced blood supply to myocardial tissue due to plaque instability and thrombus formation in a coronary artery). ACS is dangerous due to the unpredictable process that drives the supply problem and the high chance of fast hemodynamic deterioration (i.e., cardiogenic shock, ventricular fibrillation). This is why an ECG is made at first medical contact in most chest pain patients to include or exclude ischemia as the cause of their complaints. For speedy and adequate triaging and treatment, immediate assessment of this prehospital ECG is necessary, still during the ambulance ride. Human diagnostic efforts supported by automated interpretation algorithms seek to answer questions regarding the urgency level, the decision if and towards which healthcare facility the patient should be transported, and the indicated acute treatment and further diagnostics after arrival in the healthcare facility. In the case of an ACS, a catheter intervention room may be activated during the ambulance ride to facilitate the earliest possible in-hospital treatment. Prehospital ECG assessment and the subsequent triaging decisions are complex because chest pain is not uniquely associated with ACS. The differential diagnosis includes other cardiac, pulmonary, vascular, gastrointestinal, orthopedic, and psychological conditions. Some of these conditions may also involve ECG abnormalities. In practice, only a limited fraction (order of magnitude 10%) of the patients who are urgently transported to the hospital because of chest pain are ACS patients. Given the relatively low prevalence of ACS in this patient mix, the specificity of the diagnostic ECG algorithms should be relatively high to prevent overtreatment and overflow of intervention facilities. On the other hand, only a sufficiently high sensitivity warrants adequate therapy when needed. Here, we review how the prehospital ECG can contribute to identifying the presence of myocardial ischemia in chest pain patients. We discuss the various mechanisms of myocardial ischemia and infarction, the typical patient mix of chest pain patients, the shortcomings of the ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) ECG criteria to detect a completely occluded culprit artery, the OMI ECG criteria (including the STEMI-equivalent ECG patterns) in detecting completely occluded culprit arteries, and the promise of neural networks in recognizing ECG patterns that represent complete occlusions. We also discuss the relevance of detecting any ACS/ischemia, not necessarily caused by a total occlusion, in the prehospital ECG. In addition, we discuss how serial prehospital ECGs can contribute to ischemia diagnosis. Finally, we discuss the diagnostic contribution of a serial comparison of the prehospital ECG with a previously made nonischemic ECG of the patient.

Post hoc labeling an acute ECG as ischemic or non-ischemic based on clinical data: A necessary challenge.

The ECG is crucial in the prehospital (and early inhospital) phase of patients with symptoms suggestive of myocardial ischemia. Therefore, new algorithms for ECG-based myocardial ischemia detection are continuously being researched. Development and validation of these algorithms require a database of acute ECGs (from the prehospital or emergency department setting) including a representative mix of cases (ischemia present) and controls (no ischemia present). Therefore, for every patient in this mix, the "truth" regarding the actual presence or absence of myocardial ischemia during the recording of the acute ECG has to be determined to compare the newly developed algorithm against. This post hoc adjudication process of determining whether an acute (either prehospitally acquired or acquired in the emergency department) ECG was made under ischemic conditions should use all available clinical data (the clinical diagnosis, cardiac imaging data, and laboratory values) of the subsequent patient's admission. Even with all data at hand, post hoc labeling a patient and their acute ECG as a myocardial ischemia case or control cannot be forced into a binary division between definite cases and definite controls. More specifically, to be used for the development of a new algorithm, the patients' ECG has to be scored for the presence or absence of myocardial ischemia at the exact moment of its recording, which renders the classification even more difficult. For instance, even though it may be plausible that myocardial ischemia was present at a given moment during the patient's admission, this is not necessarily proof that the prehospital (or early inhospital) ECG was also made in ischemic conditions: ischemia can be a fluctuating process (as is, e.g., the case in unstable angina pectoris). Therefore, post hoc classification of an acute ECG in terms of the absence or presence of ischemia requires a multipoint scale ranging between definite ischemic to definite non-ischemic, for instance using a 5-point scale (presumed non-ischemic, probably non-ischemic, uncertain, probably ischemic, presumed ischemic). To summarize, the post hoc adjudication process of ECGs of ambulance (and emergency department) patients cannot result in a binary division into definite cases and controls (i.e., patients with or without myocardial ischemia during the recording of the acute ECG), as myocardial ischemia is often dynamic rather than constant. ECGs could be labeled on a multi-point scale, in which the label represents the probability of the actual presence (or absence) of myocardial ischemia at the exact moment of the recording of that ECG. Further development of algorithms for myocardial ischemia detection should consider this concept.

Advanced repeated structuring and learning procedure to detect acute myocardial ischemia in serial 12-lead ECGs.

Objectives. Acute myocardial ischemia in the setting of acute coronary syndrome (ACS) may lead to myocardial infarction. Therefore, timely decisions, already in the pre-hospital phase, are crucial to preserving cardiac function as much as possible. Serial electrocardiography, a comparison of the acute electrocardiogram with a previously recorded (reference) ECG of the same patient, aids in identifying ischemia-induced electrocardiographic changes by correcting for interindividual ECG variability. Recently, the combination of deep learning and serial electrocardiography provided promising results in detecting emerging cardiac diseases; thus, the aim of our current study is the application of our novel Advanced Repeated Structuring and Learning Procedure (AdvRS&LP), specifically designed for acute myocardial ischemia detection in the pre-hospital phase by using serial ECG features.Approach. Data belong to the SUBTRACT study, which includes 1425 ECG pairs, 194 (14%) ACS patients, and 1035 (73%) controls. Each ECG pair was characterized by 28 serial features that, with sex and age, constituted the inputs of the AdvRS&LP, an automatic constructive procedure for creating supervised neural networks (NN). We created 100 NNs to compensate for statistical fluctuations due to random data divisions of a limited dataset. We compared the performance of the obtained NNs to a logistic regression (LR) procedure and the Glasgow program (Uni-G) in terms of area-under-the-curve (AUC) of the receiver-operating-characteristic curve, sensitivity (SE), and specificity (SP).Main Results. NNs (median AUC = 83%, median SE = 77%, and median SP = 89%) presented a statistically (Pvalue lower than 0.05) higher testing performance than those presented by LR (median AUC = 80%, median SE = 67%, and median SP = 81%) and by the Uni-G algorithm (median SE = 72% and median SP = 82%).Significance. In conclusion, the positive results underscore the value of serial ECG comparison in ischemia detection, and NNs created by AdvRS&LP seem to be reliable tools in terms of generalization and clinical applicability.

Intra-Individual Comparison of Sinus and Ectopic Beats Probing the Ventricular Gradient's Activation Dependence.

Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. This paradigm has often been refuted and was never convincingly corroborated. We sought to validate Wilson's concept by intra-individual comparison of the VG of sinus beats and ectopic beats, thus assessing the effects of both altered ventricular conduction (caused by the ectopic focus) and restitution (caused by ectopic prematurity). We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles: normally conducted supraventricular ectopic beats (SN, N = 6) and aberrantly conducted supraventricular ectopic beats (SA, N = 20) or ventricular ectopic beats (V, N = 92). In each patient, we computed the VG vectors of the predominant beat, VGp→, of the ectopic beat, VGe→, and of the VG difference vector, ΔVGep→, and compared their sizes. VGe→ of the SA and V ectopic beats were significantly larger than VGp→ (53.7 ± 25.0 vs. 47.8 ± 24.6 mV∙ms, respectively; p < 0.001). ΔVGep→ were three times larger than the difference of VGe→ and VGp→ (19.94 ± 9.76 vs. 5.94 mV∙ms, respectively), demonstrating differences in the VGp→ and VGe→ spatial directions. The amount of ectopic prematurity was not correlated with ΔVGep→, although the larger VG difference vectors were observed for the more premature (<80%) extrasystoles. Electrical restitution properties and electrotonic interactions likely explain our findings. We conclude that the concept of a conduction-independent VG should be tested at equal heart rates and without including premature extrasystoles.

Repeated Structuring & Learning Procedure for Detection of Myocardial Ischemia: a Robustness Analysis.

Myocardial ischemia, consisting in a reduction of blood flow to the heart, may cause sudden cardiac death by myocardial infarction or trigger serious abnormal rhythms. Thus, its timely identification is crucial. The Repeated Structuring and Learning Procedure (RS&LP), an innovative constructive algorithm able to dynamically create neural networks (NN) alternating structuring and learning phases, was previously found potentially useful for myocardial ischemia detection. However, performance of created NN depends on three parameters, the values of which need to be set a priori by the user: maximal number of layers (NL), maximal number of initializations (NI) and maximal number of confirmations (NC). A robustness analysis of RS&LP to varying values of NL, NI and NC is fundamental for clinical applications concerning myocardial ischemia detection but was never performed before; thus, it was the aim the present study. Thirteen serial ECG features were extracted by pairs of ECGs belonging to 84 cases (patients with induced myocardial ischemia) and 398 controls (patients with no myocardial ischemia) and used as inputs to learn (50% of population) and test (50% of population) NNs with varying values of NL (1,2,3,4,10), NI (50,250,500,1000,1500) and NC (2,5,10,20,50). Performance of obtained NNs was compared in terms of area under the curve (AUC) of the receiver operating characteristics. Overall, 13 NNs were considered; 12 (92%) were characterized by AUC≥80% and 4 (31%) by AUC≥85%. Thus, RS&LP proved to be robust when creating NNs for detecting of myocardial ischemia.

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the "AF Classification from a Short Single Lead ECG Recording" database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1-93.0%), 90.2% (CI: 86.2-94.3%) and 90.8% (CI: 88.1-93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

Prevalence of ECGs Exceeding Thresholds for ST-Segment-Elevation Myocardial Infarction in Apparently Healthy Individuals: The Role of Ethnicity.

Background Early prehospital recognition of critical conditions such as ST-segment-elevation myocardial infarction (STEMI) has prognostic relevance. Current international electrocardiographic STEMI thresholds are predominantly based on individuals of Western European descent. However, because of ethnic electrocardiographic variability both in health and disease, there is a need to reevaluate diagnostic ST-segment elevation thresholds for different populations. We hypothesized that fulfillment of ST-segment elevation thresholds of STEMI criteria (STE-ECGs) in apparently healthy individuals is ethnicity dependent. Methods and Results HELIUS (Healthy Life in an Urban Setting) is a multiethnic cohort study including 10 783 apparently healthy subjects of 6 different ethnicities (African Surinamese, Dutch, Ghanaian, Moroccan, South Asian Surinamese, and Turkish). Prevalence of STE-ECGs across ethnicities, sexes, and age groups was assessed with respect to the 2 international STEMI thresholds: sex and age specific versus sex specific. Mean prevalence of STE-ECGs was 2.8% to 3.4% (age/sex-specific and sex-specific thresholds, respectively), although with large ethnicity-dependent variability. Prevalences in Western European Dutch were 2.3% to 3.0%, but excessively higher in young (<40 years) Ghanaian males (21.7%-27.5%) and lowest in older (≥40 years) Turkish females (0.0%). Ethnicity (sub-Saharan African origin) and other variables (eg, younger age, male sex, high QRS voltages, or anterolateral early repolarization pattern) were positively associated with STE-ECG occurrence, resulting in subgroups with >45% STE-ECGs. Conclusions The accuracy of diagnostic tests partly relies on background prevalence in healthy individuals. In apparently healthy subjects, there is a highly variable ethnicity-dependent prevalence of ECGs with ST-segment elevations exceeding STEMI thresholds. This has potential consequences for STEMI evaluations in individuals who are not of Western European descent, putatively resulting in adverse outcomes with both over- and underdiagnosis of STEMI.

Diagnostic Accuracy Of The Electrocardiographic Decision Support - Myocardial Ischaemia (EDS-MI) Algorithm In Detection Of Acute Coronary Occlusion.

Electrocardiographic Decision Support - Myocardial Ischaemia (EDS-MI) is a graphical decision support for detection and localization of acute transmural ischaemia. A recent study indicated that EDS-MI performs well for detection of acute transmural ischaemia. However, its performance has not been tested in patients with non-ischaemic ST-deviation. We aimed to optimize the diagnostic accuracy of EDS-MI in patients with verified acute coronary occlusion as well as patients with non-ischaemic ST deviation and compare its performance with STEMI criteria. We studied 135 patients with non-ischaemic ST deviation (perimyocarditis, left ventricular hypertrophy, takotsubo cardiomyopathy and early repolarization) and 117 patients with acute coronary occlusion. In 63 ischaemic patients, the extent and location of the ischaemic area (myocardium at risk) was assessed by both cardiovascular magnetic resonance imaging and EDS-MI. Sensitivity and specificity of ST elevation myocardial infarction criteria were 85% (95% confidence interval (CI) 77, 90) and 44% (95% CI 36, 53) respectively. Using EDS-MI, sensitivity and specificity increased to 92% (95% CI 85, 95) and 81% (95% CI 74, 87) respectively (p=0.035 and p<0.001). Agreement was strong (83%) between cardiovascular magnetic resonance imaging and EDS-MI in localization of ischaemia. Mean myocardium at risk was 32% (± 10) by cardiovascular magnetic resonance imaging and 33% (± 11) by EDS-MI when the estimated infarcted area according to Selvester QRS scoring was included in myocardium at risk estimation. In conclusion, EDS-MI increases diagnostic accuracy and may serve as an automatic decision support in the early management of patients with suspected acute coronary syndrome. The added clinical benefit in a non-selected clinical chest pain population needs to be assessed.

An initial exploration of subtraction electrocardiography to detect myocardial ischemia in the prehospital setting.

BACKGROUND: In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG-patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography. METHODS: SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in-hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni-G ECG Analysis Program was used for state-of-the-art single-ECG interpretation of the ambulance ECG. RESULTS: In 1,229 patients, the mean area-under-the-curve of subtraction electrocardiography was 0.80 (95%CI: 0.77-0.82). The performance of our new method was comparable to single-ECG analysis using the Uni-G algorithm: sensitivities were 66% versus 67% (p-value > .05), respectively; specificities were 80% versus 81% (p-value > .05), respectively. CONCLUSIONS: In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state-of-the-art automated single-ECG analysis by the Uni-G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection.

Corrigendum: Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

[This corrects the article DOI: 10.3389/fphys.2018.00424.].

Normal Values of QT Variability in 10-s Electrocardiograms for all Ages.

Aims: QT variability is a promising electrocardiographic marker. It has been studied as a screening tool for coronary artery disease and left ventricular hypertrophy, and increased QT variability is a known risk factor for sudden cardiac death. Considering that comprehensive normal values for QT variability were lacking, we set out to establish these in standard 10-s electrocardiograms (ECGs) covering both sexes and all ages. Methods: Ten-second, 12-lead ECGs were provided by five Dutch population studies (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study). ECGs were recorded digitally and processed by well-validated analysis software. We selected cardiologically healthy participants, 46% being women. Ages ranged from 11 days to 91 years. After quality control, 13,828 ECGs were available. We assessed three markers: standard deviation of QT intervals (SDqt), short-term QT variability (STVqt), and QT variability index (QTVI). Results: For SDqt and STVqt, the median and the lower limit of normal remained stable with age. The upper limit of normal declined until around age 45, and increased strongly in the elderly, notably so in women. This implies that a subset of the population, small enough not to have appreciable effect on the median, shows a high degree of QT variability with a possible risk of arrhythmias or worse, especially in women. Otherwise, sex differences were negligible in all three measurements. For QTVI, median, and normal limits decreased until age 20, and steadily went up afterwards except for the lower limit of normal, which flattens off after age 65. Conclusion: We report the first set of normal values for QT variability based on 10-s ECGs, for all ages and both sexes.

Electrical remodeling after percutaneous atrial septal defect closure in pediatric and adult patients.

BACKGROUND: Several studies have reported changes in electrocardiographic variables after atrial septal defect (ASD) closure. However no temporal electro-and vectorcardiographic changes have been described from acute to long-term follow-up at different ages. We aimed to study electrical remodeling after percutaneous ASD closure in pediatric and adult patients. METHODS: ECGs of 69 children and 75 adults (median age 6 [IQR 4-11] years and 45 [IQR 33-54] years, respectively) were retrospectively selected before percutaneous ASD closure and at acute (1-7 days), intermediate (4-14 weeks) and late (6-18 months) follow-up. Apart from electrocardiographic variables, spatial QRS-T angle and ventricular gradient (VG) were derived from mathematically-synthesized vectorcardiograms. RESULTS: In both pediatric and adult patients, the heart rate decreased immediately post-closure, which persisted to late follow-up. The P-wave amplitude also decreased acutely post-closure, but remained unchanged at later follow-up. The PQ duration shortened immediately in children and at intermediate follow-up in adults. The QRS duration and QTc interval decreased at intermediate-term follow-up in both children and adults. In both groups the spatial QRS-T angle decreased at late follow-up. The VG magnitude increased at intermediate follow-up in children and at late follow-up in adults, after an initial decrease in children. CONCLUSION: In both pediatric and adult ASD patients, electrocardiographic changes mainly occurred directly after ASD closure except for shortening of QRS duration and QTc interval, which occurred at later follow-up. Adults also showed late changes in PQ duration. At 6-to-18 month post-closure, the spatial QRS-T angle decreased, reflecting increased electrocardiographic concordance. The initial acute decrease in VG in children, which was followed by a significant increase, may be the effect of action potential duration dynamics directly after percutaneous ASD closure.

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-learning neural networks (NNs) that are specialized in detecting newly emerging or aggravating existing cardiac pathology in serial ECGs. METHODS: We developed a novel deep-learning method for serial ECG analysis and tested its performance in detection of heart failure in post-infarction patients, and in the detection of ischemia in patients who underwent elective percutaneous coronary intervention. Core of the method is the repeated structuring and learning procedure that, when fed with 13 serial ECG difference features (intra-individual differences in: QRS duration; QT interval; QRS maximum; T-wave maximum; QRS integral; T-wave integral; QRS complexity; T-wave complexity; ventricular gradient; QRS-T spatial angle; heart rate; J-point amplitude; and T-wave symmetry), dynamically creates a NN of at most three hidden layers. An optimization process reduces the possibility of obtaining an inefficient NN due to adverse initialization. RESULTS: Application of our method to the two clinical ECG databases yielded 3-layer NN architectures, both showing high testing performances (areas under the receiver operating curves were 84% and 83%, respectively). CONCLUSIONS: Our method was successful in two different clinical serial ECG applications. Further studies will investigate if other problem-specific NNs can successfully be constructed, and even if it will be possible to construct a universal NN to detect any pathologic ECG change.

Normal Values of Corrected Heart-Rate Variability in 10-Second Electrocardiograms for All Ages.

Purpose: Heart-rate variability (HRV) measured on standard 10-s electrocardiograms (ECGs) has been associated with increased risk of cardiac and all-cause mortality, but age- and sex-dependent normal values have not been established. Since heart rate strongly affects HRV, its effect should be taken into account. We determined a comprehensive set of normal values of heart-rate corrected HRV derived from 10-s ECGs for both children and adults, covering both sexes. Methods: Five population studies in the Netherlands (Pediatric Normal ECG Study, Leiden University Einthoven Science Project, Prevention of Renal and Vascular End-stage Disease Study, Utrecht Health Project, Rotterdam Study) provided 10-s, 12-lead ECGs. ECGs were stored digitally and analyzed by well-validated analysis software. We included cardiologically healthy participants, 42% being men. Their ages ranged from 11 days to 91 years. After quality control, 13,943 ECGs were available. Heart-rate correction formulas were derived using an exponential model. Two time-domain HRV markers were analyzed: the corrected standard deviation of the normal-to-normal RR intervals (SDNNc) and corrected root mean square of successive RR-interval differences (RMSSDc). Results: There was a considerable age effect. For both SDNNc and RMSSDc, the median and the lower limit of normal decreased steadily from birth until old age. The upper limit of normal decreased until the age of 60, but increased markedly after that age. Differences of the median were minimal between men and women. Conclusion: We report the first comprehensive set of normal values for heart-rate corrected 10-s HRV, which can be of value in clinical practice and in further research.

Normal values of the ventricular gradient and QRS-T angle, derived from the pediatric electrocardiogram.

BACKGROUND: Normal values of the mathematically-synthesized vectorcardiogram (VCG) are lacking for children. Therefore, the objective of this study was to assess normal values of the pediatric synthesized VCG (spatial QRS-T angle [SA] and ventricular gradient [VG]). METHODS: Electrocardiograms (ECGs) of 1263 subjects (0-24 years) with a normal heart were retrospectively selected. VCGs were synthesized by the Kors matrix. Normal values (presented as 2nd and 98th percentiles) were assessed by quantile regression with smoothing by splines. RESULTS: Our results show that heart rate decreased over age, QRS duration increased and QTc interval remained constant. The SA initially decreased and increased again from the age of 8 years. The VG magnitude was relatively stable until the age of 2 years, after which it increased. CONCLUSION: Normal values of the pediatric ECG and VCG (VG and SA) were established. These normal values could be important for future studies using VG and SA for risk stratification in heart disease in children.

Longitudinal association of short-term, metronome-paced heart rate variability and echocardiographically assessed cardiac structure at a 4-year follow-up: results from the prospective, population-based CARLA cohort.

AIMS: To assess the value of cardiac structure/function in predicting heart rate variability (HRV) and the possibly predictive value of HRV on cardiac parameters. METHODS AND RESULTS: Baseline and 4-year follow-up data from the population-based CARLA cohort were used (790 men, 646 women, aged 45-83 years at baseline and 50-87 years at follow-up). Echocardiographic and HRV recordings were performed at baseline and at follow-up. Linear regression models with a quadratic term were used. Crude and covariate adjusted estimates were calculated. Missing values were imputed by means of multiple imputation. Heart rate variability measures taken into account consisted of linear time and frequency domain [standard deviation of normal-to-normal intervals (SDNN), high-frequency power (HF), low-frequency power (LF), LF/HF ratio] and non-linear measures [detrended fluctuation analysis (DFA1), SD1, SD2, SD1/SD2 ratio]. Echocardiographic parameters considered were ventricular mass index, diastolic interventricular septum thickness, left ventricular diastolic dimension, left atrial dimension systolic (LADS), and ejection fraction (Teichholz). A negative quadratic relation between baseline LADS and change in SDNN and HF was observed. The maximum HF and SDNN change (an increase of roughly 0.02%) was predicted at LADS of 3.72 and 3.57 cm, respectively, while the majority of subjects experienced a decrease in HRV. There was no association between further echocardiographic parameters and change in HRV, and there was no evidence of a predictive value of HRV in the prediction of changes in cardiac structure. CONCLUSION: In the general population, LADS predicts 4-year alteration in SDNN and HF non-linearly. Because of the novelty of the result, analyses should be replicated in other populations.

Galen Wagner, M.D., Ph.D. (1939-2016) as international mentor of young investigators in electrocardiology.

This paper describes a substantial part of the international mentoring network of students and young investigators in electrocardiology that developed around Dr. Galen Wagner (1939-2016), including many experiences of his mentees and co-mentors. The paper is meant to stimulate thinking about international mentoring as a means to achieve important learning experiences and personal development of young investigators, to intensify international scientific cooperation, and to stimulate scientific production.