Background: The prolongation of intraatrial and interatrial conduction time and the inhomogeneous propagation of sinus impulses are well known electrophysiologic characteristics in patients with paroxysmal atrial fibrillation (AF). Previous studies have demonstrated that individuals with a clinical history of paroxysmal AF show a significantly increased P‐wave duration in 12‐lead surface electrocardiograms (ECG) and signal‐averaged ECG recordings.Methods: The inhomogeneous and discontinuous atrial conduction in patients with paroxysmal AF has recently been studied with a new ECG index, P‐wave dispersion. P‐wave dispersion is defined as the difference between the longest and the shortest P‐wave duration recorded from multiple different surface ECG leads. Up to now the most extensive clinical evaluation of P‐wave dispersion has been performed in the assessment of the risk for AF in patients without apparent heart disease, in hypertensives, in patients with coronary artery disease and in patients undergoing coronary artery bypass surgery. P‐wave dispersion has proven to be a sensitive and specific ECG predictor of AF in the various clinical settings. However, no electrophysiologic study has proven up to now the suspected relationship between the dispersion in the atrial conduction times and P‐wave dispersion. The methodology used for the calculation of P‐wave dispersion is not standardized and more efforts to improve the reliability and reproducibility of P‐wave dispersion measurements are needed.Conclusions: P‐wave dispersion constitutes a recent contribution to the field of noninvasive electrocardiology and seems to be quite promising in the field of AF prediction. A.N.E. 2001;6(2):159–165

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

Objective: To evaluate the impact of meteorological variables on daily and monthly deaths caused by acute myocardial infarction (AMI). Methods: All death certificate data from the Athens territory were analysed for AMI deaths in 2001. Daily atmospheric temperature, pressure and relative humidity data were obtained from the National Meteorological Society for Athens for the same year. Results: The total annual number of deaths caused by AMI was 3126 (1953 men) from a population of 2 664 776 (0.117%). Seasonal variation in deaths was significant, with the average daily AMI deaths in winter being 31.8% higher than in summer (9.89 v 7.35, p < 0.001). Monthly variation was more pronounced for older people (mean daily AMI deaths of people older than 70 years was 3.53 in June and 7.03 in December; p < 0.001) and of only marginal significance for younger people. The best predictor of daily AMI deaths was the average temperature of the previous seven days; the relation between daily AMI deaths and seven-day average temperature (R² = 0.109, p < 0.001) was U-shaped. Considering monthly AMI death rates, only mean monthly humidity was independently associated with total deaths from AMI (R² = 0.541, p = 0.004). Conclusion: Ambient temperature is an important predictor of AMI mortality even in the mild climate of a Mediterranean city like Athens, its effects being predominantly evident in the elderly. Mean monthly humidity is another meteorological factor that appears to affect monthly numbers of AMI deaths. These findings may be useful for healthcare and civil protection planning.

Commercially available health technologies such as smartphones and smartwatches, activity trackers and eHealth applications, commonly referred to as wearables, are increasingly available and used both in the leisure and healthcare sector for pulse and fitness/activity tracking. The aim of the Position Paper is to identify specific barriers and knowledge gaps for the use of wearables, in particular for heart rate (HR) and activity tracking, in clinical cardiovascular healthcare to support their implementation into clinical care. The widespread use of HR and fitness tracking technologies provides unparalleled opportunities for capturing physiological information from large populations in the community, which has previously only been available in patient populations in the setting of healthcare provision. The availability of low-cost and high-volume physiological data from the community also provides unique challenges. While the number of patients meeting healthcare providers with data from wearables is rapidly growing, there are at present no clinical guidelines on how and when to use data from wearables in primary and secondary prevention. Technical aspects of HR tracking especially during activity need to be further validated. How to analyse, translate, and interpret large datasets of information into clinically applicable recommendations needs further consideration. While the current users of wearable technologies tend to be young, healthy and in the higher sociodemographic strata, wearables could potentially have a greater utility in the elderly and higher-risk population. Wearables may also provide a benefit through increased health awareness, democratization of health data and patient engagement. Use of continuous monitoring may provide opportunities for detection of risk factors and disease development earlier in the causal pathway, which may provide novel applications in both prevention and clinical research. However, wearables may also have potential adverse consequences due to unintended modification of behaviour, uncertain use and interpretation of large physiological data, a possible increase in social inequality due to differential access and technological literacy, challenges with regulatory bodies and privacy issues. In the present position paper, current applications as well as specific barriers and gaps in knowledge are identified and discussed in order to support the implementation of wearable technologies from gadget-ology into clinical cardiology.