The Diagnostic Value of the 12-Lead ECG in Arrhythmogenic Left Ventricular Cardiomyopathy: Novel ECG Signs.

BACKGROUND: Electrocardiographic (ECG) findings in arrhythmogenic left ventricular cardiomyopathy (ALVC) are limited to small case series. OBJECTIVES: This study aimed to analyze the ECG characteristics of ALVC patients and to correlate ECG with cardiac magnetic resonance and genotype data. METHODS: We reviewed data of 54 consecutive ALVC patients (32 men, age 39 ± 15 years) and compared them with 84 healthy controls with normal cardiac magnetic resonance. RESULTS: T-wave inversion was often noted (57.4%), particularly in the inferior and lateral leads. Low QRS voltages in limb leads were observed in 22.2% of patients. The following novel ECG findings were identified: left posterior fascicular block (LPFB) (20.4%), pathological Q waves (33.3%), and a prominent R-wave in V1 with a R/S ratio ≥0.5 (24.1%). The QRS voltages were lower in ALVC compared with controls, particularly in lead I and II. At receiver-operating characteristic analysis, the sum of the R-wave in I to II ≤8 mm (AUC: 0.909; P < 0.0001) and S-wave in V1 plus R-wave in V6 ≤12 mm (AUC: 0.784; P < 0.0001) effectively discriminated ALVC patients from controls. It is noteworthy that 4 of the 8 patients with an apparently normal ECG were recognized by these new signs. Transmural late gadolinium enhancement was associated to LPFB, a R/S ratio ≥0.5 in V1, and inferolateral T-wave inversion, and a ringlike pattern correlated to fragmented QRS, SV1+RV6 ≤12 mm, low QRS voltage, and desmoplakin alterations. CONCLUSIONS: Pathological Q waves, LPFB, and a prominent R-wave in V1 were common ECG signs in ALVC. An R-wave sum in I to II ≤8 mm and SV1+RV6 ≤12 mm were specific findings for ALVC phenotypes compared with controls.

Electrocardiogram in arrhytmogenic cardiomyopathy.

Criteria for diagnosis of arrhythmogenic cardiomyopathy (ACM) were first proposed in 1994 and subsequently revised in 2010 and in 2020 by an international task force. According to the last consensus of 2020, ACM is defined as a heart muscle disease affecting right ventricle, left ventricle or both, whose principal pathologic feature is fibrofatty myocardial replacement that impairs systolic ventricular function and predisposes to lethal ventricular arrhythmias. ECG findings not only could help to early recognize affected patients but also could identify the ones with maximum risk of ventricular arrhythmias and sudden cardiac death.

Heart failure and telemedicine: where are we and where are we going? Opportunities and critical issues.

Heart failure (HF) is one of the main causes of morbidity in the world and is responsible for an enormous amount of health costs, mostly due to hospitalizations. The remote control techniques of vital signs and health status have the potential to help prevent factors leading to HF instability by stimulating early therapeutic interventions. The goal of telemedicine is to change the intervention strategy from a 'reactive' type, in which therapy is optimized in response to the worsening of symptoms, to a 'pro-active' type, in which therapeutic changes are undertaken based on changes in the monitored parameters during the sub-clinical phase. This article is aimed at exploring the major results obtained by telemedicine application in HF patients with and without cardiac electronic devices or in those with haemodynamic sensors and to analyse the critical issues and the opportunities of its use.

Arrhythmic risk prediction in arrhythmogenic right ventricular cardiomyopathy: external validation of the arrhythmogenic right ventricular cardiomyopathy risk calculator.

AIMS: Arrhythmogenic right ventricular cardiomyopathy (ARVC) causes ventricular arrhythmias (VAs) and sudden cardiac death (SCD). In 2019, a risk prediction model that estimates the 5-year risk of incident VAs in ARVC was developed (ARVCrisk.com). This study aimed to externally validate this prediction model in a large international multicentre cohort and to compare its performance with the risk factor approach recommended for implantable cardioverter-defibrillator (ICD) use by published guidelines and expert consensus. METHODS AND RESULTS: In a retrospective cohort of 429 individuals from 29 centres in North America and Europe, 103 (24%) experienced sustained VA during a median follow-up of 5.02 (2.05-7.90) years following diagnosis of ARVC. External validation yielded good discrimination [C-index of 0.70 (95% confidence interval-CI 0.65-0.75)] and calibration slope of 1.01 (95% CI 0.99-1.03). Compared with the three published consensus-based decision algorithms for ICD use in ARVC (Heart Rhythm Society consensus on arrhythmogenic cardiomyopathy, International Task Force consensus statement on the treatment of ARVC, and American Heart Association guidelines for VA and SCD), the risk calculator performed better with a superior net clinical benefit below risk threshold of 35%. CONCLUSION: Using a large independent cohort of patients, this study shows that the ARVC risk model provides good prognostic information and outperforms other published decision algorithms for ICD use. These findings support the use of the model to facilitate shared decision making regarding ICD implantation in the primary prevention of SCD in ARVC.

Active fixation coronary sinus lead in a patient with persistence of left superior vena cava.

In patients with PLSVC, the use of an active fixation lead (like the Medtronic "Attain Stability") on the coronary sinus can lead to a successful and safe cardiac resynchronization, facilitating its positioning with a low long-term displacement rate.

QT spatial dispersion and sudden cardiac death in hypertrophic cardiomyopathy: Time for reappraisal.

BACKGROUND: The 12-lead surface electrocardiographic (ECG) analysis is able to provide independent predictors of prognosis in several cardiovascular settings, including hypertrophic cardiomyopathy (HCM). The present single-center study investigated the possible ability of several ECG-derived variables in stratifying sudden cardiac death (SCD) risk and, possibly, in improving the accuracy of the 2014 European Society of Cardiology guidelines. METHODS: A total of 221 consecutive HCM outpatients were recruited and prospectively followed. All of them underwent a full clinical and instrumental examination, including a 12-lead surface ECG to calculate the dispersion for the following intervals: QRS, Q-Tend (QT), Q-Tpeak (QTp), Tpeak-Tend (TpTe), J-Tpeak (JTp), and J-Tend (JT). The study composite end-point was SCD, aborted SCD, and appropriate implantable cardioverter defibrillator (ICD) interventions. RESULTS: During a median follow-up of 4.4 years (25th-75th interquartile range: 2.4-9.4 years), 23 patients reached the end-point at 5-years (3 SCD, 3 aborted SCD, 17 appropriate ICD interventions). At multivariate analysis, the spatial QT dispersion corrected according to Bazett's formula (QTcd) remains independently associated to the study endpoint over the HCM Risk-SCD score (C-index 0.737). A QTcd cut-off value of 93ms showed the best accuracy in predicting the SCD endpoint within the entire HCM study cohort (sensitivity 56%, specificity 75%, positive predictive value 22%, negative predictive value 97%). CONCLUSION: Our data suggest that the QTcd might be helpful in SCD risk stratification, particularly in those HCM categories classified at low-intermediate SCD risk according to the contemporary guidelines.

Spatial QT Dispersion Predicts Nonsustained Ventricular Tachycardia and Correlates with Confined Systodiastolic Dysfunction in Hypertrophic Cardiomyopathy.

OBJECTIVES: An increased dispersion of myocardial repolarization represents one of the mechanisms underlying the arrhythmic risk in hypertrophic cardiomyopathy (HCM). We investigated spatial myocardial repolarization dispersion indices in HCM patients with nonsustained ventricular tachycardia (NSVT) and, contextually, their main clinical determinants. METHODS: Fifty-two well-matched HCM outpatients were categorized into two groups according to the presence or the absence of NSVT at 24-hour Holter electrocardiogram (ECG) monitoring. Each patient underwent a clinical examination, including Doppler echocardiogram integrated with tissue Doppler imaging, cardiac magnetic resonance, and 12-lead surface ECG to calculate the dispersion for the following intervals: QRS, Q-Tend (QTe), Q-Tpeak, Tpeak-Tend (TpTe), J-Tpeak, and J-Tend. RESULTS: The NSVT group showed only QTe dispersion and TpTe dispersion values to be significantly higher than their counterparts. NSVT occurrence was independently predicted by late gadolinium enhancement presence (p=0.021) and QTe Bazett dispersion (p=0.030), the latter strongly associated with the myocardial performance index (MPI) obtained at the basal segment of the interventricular septum (p=0.0004). CONCLUSION: Our data support QTe dispersion as an easy and noninvasive tool for identifying HCM patients with NSVT propensity. The strong relationship between QTe dispersion and MPI allows us to hypothesize an intriguing link between electrical instability and confined myocardial areas of systodiastolic dysfunction.