Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA.

Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.

Carnitine palmitoyltransferase II (CPT II) deficiency responsible for refractory cardiac arrhythmias, acute multiorgan failure and early fatal outcome.

BACKGROUND: Carnitine palmitoyltransferase II (CPT II) deficiency is a rare inborn error of mitochondrial fatty acid metabolism with autosomal recessive pattern of inheritance. Its phenotype is highly variable (neonatal, infantile, and adult onset) on the base of mutations of the CPT II gene. In affected subjects, long-chain acylcarnitines cannot be subdivided into carnitine and acyl-CoA, leading to their toxic accumulation in different organs. Neonatal form is the most severe, and all the reported patients died within a few days to 6 months after birth. Hereby, we report on a male late-preterm newborn who presented refractory cardiac arrhythmias and acute multiorgan (hepatic, renal, muscular) injury, leading to cerebral hemorrhage, hydrocephalus, cardiovascular failure and early (day 5 of life) to death. Subsequently, extended metabolic screening and target next generation sequencing (NGS) analysis allowed the CPT II deficiency diagnosis. CASE PRESENTATION: The male proband was born at 36+ 4 weeks of gestation by spontaneous vaginal delivery. Parents were healthy and nonconsanguineous, although both coming from Nigeria. Family history was unremarkable. Apgar score was 9/9. At birth, anthropometric measures were as follows: weight 2850 g (47th centile, -0.07 standard deviations, SD), length 50 cm (81st centile, + 0.89 SD) and occipitofrontal circumference (OFC) 35 cm (87th centile, + 1.14 SD). On day 2 of life our newborn showed bradycardia (heart rate around 80 bpm) and hypotonia, and was then transferred to the Neonatal Intensive Care Unit (NICU). There, he subsequently manifested many episodes of ventricular tachycardia, which were treated with pharmacological (magnesium sulfate) and electrical cardioversion. Due to the critical conditions of the baby (hepatic, renal and cardiac dysfunctions) and to guarantee optimal management of the arrythmias, he was transferred to the Pediatric Cardiology Reference Center of our region (Sicily, Italy), where he died 2 days later. Thereafter, the carnitines profile evidenced by the extended metabolic screening resulted compatible with a fatty acid oxidation defect (increased levels of acylcarnitines C16 and C18, and low of C2); afterwards, the targeted next generation sequencing (NGS) analysis revealed the known c.680 C > T p. (Pro227Leu) homozygous missense mutation of the CPTII gene, for diagnosis of CPT II deficiency. Genetic investigations have been, then, extended to the baby's parents, who were identified as heterozygous carriers of the same variant. When we meet again the parents for genetic counseling, the mother was within the first trimester of her second pregnancy. Therefore, we offered to the couple and performed the prenatal target NGS analysis on chorionic villi sample, which did not detect any alterations, excluding thus the CPT II deficiency in their second child. CONCLUSIONS: CPTII deficiency may be suspected in newborns showing cardiac arrhythmias, associated or not with hypertrophic cardiomyopathy, polycystic kidneys, brain malformations, hepatomegaly. Its diagnosis should be even more suspected and investigated in cases of increased plasmatic levels of creatine phosphokinase and acylcarnitines in addition to kidney, heart and liver dysfunctions, as occurred in the present patient. Accurate family history, extended metabolic screening, and multidisciplinary approach are necessary for diagnosis and adequate management of affected subjects. Next generation sequencing (NGS) techniques allow the identification of the CPTII gene mutation, essential to confirm the diagnosis before or after birth, as well as to calculate the recurrence risk for family members. Our report broads the knowledge of the genetic and molecular bases of such rare disease, improving its clinical characterization, and provides useful indications for the treatment of patients.

Arrhythmias and cardiac MRI associations in patients with established cardiac dystrophinopathy.

AIMS: Some patients with cardiac dystrophinopathy die suddenly. Whether such deaths are preventable by specific antiarrhythmic management or simply indicate heart failure overwhelming medical therapies is uncertain. The aim of this prospective, cohort study was to describe the occurrence and nature of cardiac arrhythmias recorded during prolonged continuous ECG rhythm surveillance in patients with established cardiac dystrophinopathy and relate them to abnormalities on cardiac MRI. METHODS AND RESULTS: A cohort of 10 patients (36.3 years; 3 female) with LVEF<40% due to Duchenne (3) or Becker muscular (4) dystrophy or Duchenne muscular dystrophy-gene carrying effects in females (3) were recruited, had cardiac MRI, ECG signal-averaging and ECG loop-recorder implants. All were on standard of care heart medications and none had prior history of arrhythmias.No deaths or brady arrhythmias occurred during median follow-up 30 months (range 13-35). Self-limiting episodes of asymptomatic tachyarrhythmia (range 1-29) were confirmed in 8 (80%) patients (ventricular only 2; ventricular and atrial 6). Higher ventricular arrhythmia burden correlated with extent of myocardial fibrosis (extracellular volume%, p=0.029; native T1, p=0.49; late gadolinium enhancement, p=0.49), but not with LVEF% (p=1.0) on MRI and atrial arrhythmias with left atrial dilatation. Features of VT episodes suggested various underlying arrhythmia mechanisms. CONCLUSIONS: The overall prevalence of arrhythmias was low. Even in such a small sample size, higher arrhythmia counts occurred in those with larger scar burden and greater ventricular volume, suggesting key roles for myocardial stretch as well as disease progression in arrhythmogenesis. These features overlap with the stage of left ventricular dysfunction when heart failure also becomes overt. The findings of this pilot study should help inform the design of a definitive study of specific antiarrhythmic management in dystrophinopathy. TRIAL REGISTRATION NUMBER: ISRCTN15622536.

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia: a landmark study.

AIMS: Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. METHODS AND RESULTS: We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6-3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79-0.87) and calibration slope of 0.97. CONCLUSION: The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)-positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.

Management of patients with an electrical storm or clustered ventricular arrhythmias: a clinical consensus statement of the European Heart Rhythm Association of the ESC-endorsed by the Asia-Pacific Heart Rhythm Society, Heart Rhythm Society, and Latin-American Heart Rhythm Society.

Electrical storm (ES) is a state of electrical instability, manifesting as recurrent ventricular arrhythmias (VAs) over a short period of time (three or more episodes of sustained VA within 24 h, separated by at least 5 min, requiring termination by an intervention). The clinical presentation can vary, but ES is usually a cardiac emergency. Electrical storm mainly affects patients with structural or primary electrical heart disease, often with an implantable cardioverter-defibrillator (ICD). Management of ES requires a multi-faceted approach and the involvement of multi-disciplinary teams, but despite advanced treatment and often invasive procedures, it is associated with high morbidity and mortality. With an ageing population, longer survival of heart failure patients, and an increasing number of patients with ICD, the incidence of ES is expected to increase. This European Heart Rhythm Association clinical consensus statement focuses on pathophysiology, clinical presentation, diagnostic evaluation, and acute and long-term management of patients presenting with ES or clustered VA.

Machine learning based detection of T-wave alternans in real ambulatory conditions.

BACKGROUND AND OBJECTIVE: T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. METHODS: In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K-nearest-neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper-parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. RESULTS: We train ML methods to detect a wide variety of alternant voltage from 20 to 100 µV, i.e., ranging from non-visible micro-alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. CONCLUSIONS: We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

Continuous stellate ganglion block for ventricular arrhythmias: case series, systematic review, and differences from thoracic epidural anaesthesia.

AIMS: Percutaneous stellate ganglion block (PSGB) through single-bolus injection and thoracic epidural anaesthesia (TEA) have been proposed for the acute management of refractory ventricular arrhythmias (VAs). However, data on continuous PSGB (C-PSGB) are scant. The aim of this study is to report our dual-centre experience with C-PSGB and to perform a systematic review on C-PSGB and TEA. METHODS AND RESULTS: Consecutive patients receiving C-PSGB at two centres were enrolled. The systematic literature review follows the latest Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Our case series (26 patients, 88% male, 60 ± 16 years, all with advanced structural heart disease, left ventricular ejection fraction 23 ± 11%, 32 C-PSGBs performed, with a median duration of 3 days) shows that C-PSGB is feasible and safe and leads to complete VAs suppression in 59% and to overall clinical benefit in 94% of cases. Overall, 61 patients received 68 C-PSGBs and 22 TEA, with complete VA suppression in 63% of C-PSGBs (61% of patients). Most TEA procedures (55%) were performed on intubated patients, as opposed to 28% of C-PSGBs (P = 0.02); 63% of cases were on full anticoagulation at C-PSGB, none at TEA (P < 0.001). Ropivacaine and lidocaine were the most used drugs for C-PSGB, and the available data support a starting dose of 12 and 100 mg/h, respectively. No major complications occurred, yet TEA discontinuation rate due to side effects was higher than C-PSGB (18 vs. 1%, P = 0.01). CONCLUSION: Continuous PSGB seems feasible, safe, and effective for the acute management of refractory VAs. The antiarrhythmic effect may be accomplished with less concerns for concomitant anticoagulation compared with TEA and with a lower side-effect related discontinuation rate.

Implantable loop recorders in patients with Brugada syndrome: the BruLoop study.

BACKGROUND AND AIMS: Available data on continuous rhythm monitoring by implantable loop recorders (ILRs) in patients with Brugada syndrome (BrS) are scarce. The aim of this multi-centre study was to evaluate the diagnostic yield and clinical implication of a continuous rhythm monitoring strategy by ILRs in a large cohort of BrS patients and to assess the precise arrhythmic cause of syncopal episodes. METHODS: A total of 370 patients with BrS and ILRs (mean age 43.5 ± 15.9, 33.8% female, 74.1% symptomatic) from 18 international centers were included. Patients were followed with continuous rhythm monitoring for a median follow-up of 3 years. RESULTS: During follow-up, an arrhythmic event was recorded in 30.7% of symptomatic patients [18.6% atrial arrhythmias (AAs), 10.2% bradyarrhythmias (BAs), and 7.3% ventricular arrhythmias (VAs)]. In patients with recurrent syncope, the aetiology was arrhythmic in 22.4% (59.3% BAs, 25.0% VAs, and 15.6% AAs). The ILR led to drug therapy initiation in 11.4%, ablation procedure in 10.9%, implantation of a pacemaker in 2.5%, and a cardioverter-defibrillator in 8%. At multivariate analysis, the presence of symptoms [hazard ratio (HR) 2.5, P = .001] and age >50 years (HR 1.7, P = .016) were independent predictors of arrhythmic events, while inducibility of ventricular fibrillation at the electrophysiological study (HR 9.0, P < .001) was a predictor of VAs. CONCLUSIONS: ILR detects arrhythmic events in nearly 30% of symptomatic BrS patients, leading to appropriate therapy in 70% of them. The most commonly detected arrhythmias are AAs and BAs, while VAs are detected only in 7% of cases. Symptom status can be used to guide ILR implantation.

ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial-temporal and long-range dependency features.

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN-LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN-LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

Importance: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). Objective: To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG. Design, Setting, and Participants: This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals. Exposures: Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results. Main Outcomes and Measures: The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection. Results: A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78). Conclusions and Relevance: The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

An Overview of Arrhythmias in Pregnancy.

Cardiovascular disease significantly jeopardizes pregnancies in the United States, impacting 1% to 4% of pregnancies annually. Among complications, cardiac arrhythmias are prevalent, posing concerns for maternal and fetal health. The incidence of arrhythmias during pregnancy is rising, partly due to advances in congenital heart surgery and a growing population of women with structural heart disease. While most arrhythmias are benign, the increasing prevalence of more serious arrhythmias warrants a proactive approach. Guidance and reassurance suffice in many cases, but persistent symptoms require cautious use of antiarrhythmic drugs or other therapies for a safe outcome. Managing more serious arrhythmias requires a comprehensive, multidisciplinary approach involving specialists, including maternal-fetal medicine physicians, cardiologists, electrophysiologists, and anesthesiologists.

Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs.

Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based Encoder-Decoder (CNNED) structure with a shock advisory algorithm to improve the accuracy and reliability of shock versus non-shock decision-making without CPR pause in OHCA scenarios. Our approach employs a cascade of CNNEDs in conjunction with an AED shock advisory algorithm to process the ECG data for shock decisions. Initially, a CNNED trained on an equal number of shockable and non-shockable rhythms is used to filter the CPR-contaminated data. The resulting filtered signal is then fed into a second CNNED, which is trained on imbalanced data more tilted toward the specific rhythm being analyzed. A reliable shock versus non-shock decision is made when both classifiers from the cascade structure agree, while segments with conflicting classifications are labeled as indeterminate, indicating the need for additional segments to analyze. To evaluate our approach, we generated CPR-contaminated ECG data by combining clean ECG data with 52 CPR samples. We used clean ECG data from the CUDB, AFDB, SDDB, and VFDB databases, to which 52 CPR artifact cases were added, while a separate test set provided by the AED manufacturer Defibtech LLC was used for performance evaluation. The test set comprised 20,384 non-shockable CPR-contaminated segments from 392 subjects, as well as 3744 shockable CPR-contaminated samples from 41 subjects with coarse ventricular fibrillation (VF) and 31 subjects with rapid ventricular tachycardia (rapid VT). We observed improvements in rhythm analysis using our proposed cascading CNNED structure when compared to using a single CNNED structure. Specifically, the specificity of the proposed cascade of CNNED structure increased from 99.14% to 99.35% for normal sinus rhythm and from 96.45% to 97.22% for other non-shockable rhythms. Moreover, the sensitivity for shockable rhythm detection increased from 90.90% to 95.41% for ventricular fibrillation and from 82.26% to 87.66% for rapid ventricular tachycardia. These results meet the performance thresholds set by the American Heart Association and demonstrate the reliable and accurate analysis of heart rhythms during CPR using only ECG data without the need for CPR interruptions or a reference signal.

Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion.

The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.

Ventricular Arrhythmias in Left Ventricular Assist Device Patients-Current Diagnostic and Therapeutic Considerations.

Left ventricular assist devices (LVAD) are used in the treatment of advanced left ventricular heart failure. LVAD can serve as a bridge to orthotopic heart transplantation or as a destination therapy in cases where orthotopic heart transplantation is contraindicated. Ventricular arrhythmias are frequently observed in patients with LVAD. This problem is further compounded as a result of diagnostic difficulties arising from presently available electrocardiographic methods. Due to artifacts from LVAD-generated electromagnetic fields, it can be challenging to assess the origin of arrhythmias in standard ECG tracings. In this article, we will review and discuss common mechanisms, diagnostics methods, and therapeutic strategies for ventricular arrhythmia treatment, as well as numerous problems we face in LVAD implant patients.

Cardiac arrhythmias classification using photoplethysmography database.

Worldwide, Cardiovascular Diseases (CVDs) are the leading cause of death. Patients at high cardiovascular risk require long-term follow-up for early CVDs detection. Generally, cardiac arrhythmia detection through the electrocardiogram (ECG) signal has been the basis of many studies. This technique does not provide sufficient information in addition to a high false alarm potential. In addition, the electrodes used to record the ECG signal are not suitable for long-term monitoring. Recently, the photoplethysmogram (PPG) signal has attracted great interest among scientists as it provides a non-invasive, inexpensive, and convenient source of information related to cardiac activity. In this paper, the PPG signal (online database Physio Net Challenge 2015) is used to classify different cardiac arrhythmias, namely, tachycardia, bradycardia, ventricular tachycardia, and ventricular flutter/fibrillation. The PPG signals are pre-processed and analyzed utilizing various signal-processing techniques to eliminate noise and artifacts, which forms a stage of signal preparation prior to the feature extraction process. A set of 41 PPG features is used for cardiac arrhythmias' classification through the application of four machine-learning techniques, namely, Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNNs), and Ensembles. Principal Component Analysis (PCA) technique is used for dimensionality reduction and feature extraction while preserving the most important information in the data. The results show a high-throughput evaluation with an accuracy of 98.4% for the KNN technique with a sensitivity of 98.3%, 95%, 96.8%, and 99.7% for bradycardia, tachycardia, ventricular flutter/fibrillation, and ventricular tachycardia, respectively. The outcomes of this work provide a tool to correlate the properties of the PPG signal with cardiac arrhythmias and thus the early diagnosis and treatment of CVDs.

Elevated Interleukin-6 Levels Are Associated With an Increased Risk of QTc Interval Prolongation in a Large Cohort of US Veterans.

BACKGROUND: Although accumulating data indicate that IL-6 (interleukin-6) can promote heart rate-corrected QT interval (QTc) prolongation via direct and indirect effects on cardiac electrophysiology, current evidence comes from basic investigations and small clinical studies only. Therefore, IL-6 is still largely ignored in the clinical management of long-QT syndrome and related arrhythmias. The aim of this study was to estimate the risk of QTc prolongation associated with elevated IL-6 levels in a large population of unselected subjects. METHODS AND RESULTS: An observational study using the Veterans Affairs Informatics and Computing Infrastructure was performed. Participants were US veterans who had an ECG and were tested for IL-6. Descriptive statistics and univariate and multivariate regression analyses were performed to study the relationship between IL-6 and QTc prolongation risk. Study population comprised 1085 individuals, 306 showing normal (<5 pg/mL), 376 moderately high (5-25 pg/mL), and 403 high (>25 pg/mL) IL-6 levels. Subjects with elevated IL-6 showed a concentration-dependent increase in the prevalence of QTc prolongation, and those presenting with QTc prolongation exhibited higher circulating IL-6 levels. Stepwise multivariate regression analyses demonstrated that increased IL-6 level was significantly associated with a risk of QTc prolongation up to 2 times the odds of the reference category of QTc (e.g. QTc >470 ms men/480 ms women ms: odds ratio, 2.28 [95% CI, 1.12-4.50] for IL-6 >25 pg/mL) regardless of the underlying cause. Specifically, the mean QTc increase observed in the presence of elevated IL-6 was quantitatively comparable (IL-6 >25 pg/mL:+6.7 ms) to that of major recognized QT-prolonging risk factors, such as hypokalemia and history of myocardial infarction. CONCLUSIONS: Our data provide evidence that a high circulating IL-6 level is a robust risk factor for QTc prolongation in a large cohort of US veterans, supporting a potentially important arrhythmogenic role for this cytokine in the general population.

Pharmacological Management of Cardiac Arrhythmias in the Fetal and Neonatal Periods: A Scientific Statement From the American Heart Association: Endorsed by the Pediatric & Congenital Electrophysiology Society (PACES).

Disorders of the cardiac rhythm may occur in both the fetus and neonate. Because of the immature myocardium, the hemodynamic consequences of either bradyarrhythmias or tachyarrhythmias may be far more significant than in mature physiological states. Treatment options are limited in the fetus and neonate because of limited vascular access, patient size, and the significant risk/benefit ratio of any intervention. In addition, exposure of the fetus or neonate to either persistent arrhythmias or antiarrhythmic medications may have yet-to-be-determined long-term developmental consequences. This scientific statement discusses the mechanism of arrhythmias, pharmacological treatment options, and distinct aspects of pharmacokinetics for the fetus and neonate. From the available current data, subjects of apparent consistency/consensus are presented, as well as future directions for research in terms of aspects of care for which evidence has not been established.