12-Lead electrocardiogram as a predictor of sudden cardiac death: from epidemiology to clinical practice

A randomized clinical trial of omega-3 fatty acid and vitamin D supplementation on electrocardiographic risk profiles

Beneficial and adverse associations with arrhythmias have been reported for omega-3 fatty acids (omega-3 FA) and Vitamin D. The 12 lead electrocardiogram (ECG) contains quantitative measures reflecting diverse aspects of electrophysiology that might provide insights into mechanisms underlying these associations. In a pre-specified ancillary study of the VITaminD and omegA-3 (VITAL) trial, we examined the effect of 1 g of marine omega-3 FA per day, comprised of 460 mg eicosapentanoic acid and 380 mg of docosahexaenoic acid, and 2000 IU VitaminD3 per day on ECG characteristics associated with atrial and ventricular arrhythmias among individuals age 50 years or greater. A total of 911 study participants underwent ECGs at baseline and again at 2 years after the randomization. Individuals randomized to active omega-3 FA demonstrated significant net increase in PR-interval duration (p = 0.005) and P-wave duration (p = 0.03) as well significant net decrease in P-wave amplitude (p = 0.037) as compared to placebo. RMSSD increased to a greater extent in the omega-3 FA arm compared to placebo (p = 0.040). For Vitamin D3, the Cornell voltage increased to a lesser extent in the participants assigned to active treatment as compared to placebo (p = 0.044). There were no other significant differences in QRS, QTc, Cornell voltage or heart rate. Thus, randomized treatment with omega-3 FA supplements resulted in changes on the ECG that are potentially reflective of heightened vagal tone and/or slowing of intraatrial and AV conduction. Vitamin D3 supplementation resulted in modest reductions in progressive LV voltage suggestive of a potential antihypertrophic effect.Trial registration ClinicalTrials.gov Identifiers: NCT01169259, NCT02178410 (06/26/2010 and 06/30/2014).

Poor R-wave progression as a predictor of sudden cardiac death in general population and subjects with coronary artery disease

BACKGROUND

Poor R wave progression (PRWP) is a common clinical finding in the standard 12-lead ECG, but its prognostic significance is unclear.

OBJECTIVE

The purpose of this study was to examine the prognosis associated with PRWP in terms of sudden cardiac death (SCD), cardiac death, and overall mortality in general population subjects with and without coronary artery disease (CAD).

METHODS

Data and 12-lead ECGs were collected from a Finnish general population health examination survey conducted during 1978-1980 with follow-up until 2011. The study population consisted of 6854 subjects. Main end-points were SCD, cardiac death, and overall mortality. PRWP was defined as R-wave amplitude ≤ 0.3mV in V₃ and R-wave amplitude in V₂ ≤ R-wave amplitude in V₃.

RESULTS

PRWP occurred in 213 subjects (3.1%). During the follow-up of 24.3 ± 10.4 years 3723 subjects died (54.3%). PRWP was associated with older age, higher prevalence of heart failure and CAD, and beta blocker medication. In multivariate analyses, PRWP was associated with SCD (hazard ratio [HR] 2.13, 95% confidence interval [CI] 1.34-3.39), cardiac death (HR 1.75, 95% CI 1.35-2.15), and all-cause mortality (HR 1.29, 95% CI 1.08-1.54). In the subgroup with CAD, PRWP had a stronger association with cardiac mortality (HR 1.71, 95% CI 1.19-2.46) compared to the subgroup without CAD, whilst the association with SCD was significant only in subgroup with CAD (HR 2.62, 95% CI 1.38-4.98).

CONCLUSION

PRWP was associated with adverse prognosis in general population and with SCD among subjects with CAD.

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.