Almanac 2015: atrial fibrillation research in Heart.

Fibrilacija atrija (FA) ne prestaje biti predmet zanimanja kardiovaskularne zajednice i časopisa Heart. U 2014. i 2015. godini u Heartu je objavljeno više od 60 istraživanja i preglednih članaka o raznim oblicima FA, od pridruženih stanja i trigerirajućih čimbenika do novih pristupa liječenju. Ovdje sažimamo članke o FA-u objavljene u Heartu tijekom 2014. i 2015. godine, uz naglasak na novim istraživanjima, idejama i pristupima liječenju.Atrial fibrillation continues to attract interest in the cardiovascular community and in Heart. Over 60 original research and review papers published in Heart in 2014–2015 cover various aspects of atrial fibrillation, from associated conditions and precipitating factors to new approaches to management. Here, we provide an overview of articles on atrial fibrillation published in Heart in 2014–2015, highlighting new developments, emerging concepts and novel approaches to treatment

Holter electrocardiographic and clinical predictors of incident paroxysmal atrial fibrillation in patients with acute ischemic stroke

Atrial fibrillation (AF) if left untreated, due to its silent nature, could lead to considerable morbidity and mortality due to its thromboembolic complications, especially ischemic stroke. Prolonged ECG monitoring is an increasingly advocated method to detect silent AF and other arrhythmias. The optimum duration of Holter ECG monitoring to detect underlying AF is not clear leading to a variation in practice based on differences in trial results and relevant clinical guidelines. 7-day Holter ECG appears to provide a convenient way of prolonged non-invasive monitoring for AF detection. I looked at the 7-day Holter ECG data from an observational registry from Sandwell and West Birmingham Hospital (SWBH) with an unselected all-comer cohort of 476 patients and the interventional arm of MonDAFIS trial of 1714 patients with acute ischemic stroke to look at detection of new AF. Clinical, echocardiographic and Holter ECG parameters associated with newly detected AF where available went through association testing and logistic regression. The final model fit was tested through the ROC curve analysis. The AF pick-up rate in SWBH cohort was 8.8%. In this cohort, the median age in the AF group was higher than the non-AF group and there was no difference in the gender. AF was more frequently seen when the 7-day Holter ECG was done to investigate palpitations and stroke. With regards to comorbidities, AF was associated with hypertension, coronary artery disease and left-sided valvular disease and for Holter ECG variables. AF patients had a longer duration of recording and higher mean heart rate, more sinus pauses and supraventricular ectopic (SVE) activity. Logistic regression analysis showed that hypertension, previous stroke, left-sided valvular disease and palpitations were independently associated with underlying AF. In the MonDAFIS cohort, the overall AF detection was 4.6% with incremental increase per each day or recording. AF patients were older and had more underlying hypertension, diabetes, renal insufficiency increased LA size and worse LV systolic function. AF patients also had a longer duration of recording, more SVE and ventricular ectopic (VE) activity. Logistic regression analysis showed older age, frequent isolated supraventricular ectopics, SVE runs and LA dilatation as significant predictors of AF. 7-day Holter ECG monitoring has a good diagnostic yield for AF both in stroke survivors as well as an all-comer patient cohort. In general, the AF group had longer monitoring duration. There are other important similarities in the two groups in terms of clinical parameters (advancing age and hypertension in the AF group) and Holter ECG parameters (higher mean heart rates and more supraventricular ectopic activity in AF group). Combining these important clinical and Holter ECG findings can prove useful to identify patients with a high risk of underlying AF. These findings need testing through external validation and can potentially have an important real-time impact on patient care

Characterisation of ictal and interictal states of epilepsy: A system dynamic approach of principal dynamic modes analysis.

Epilepsy is a brain disorder characterised by the recurrent and unpredictable interruptions of normal brain function, called epileptic seizures. The present study attempts to derive new diagnostic indices which may delineate between ictal and interictal states of epilepsy. To achieve this, the nonlinear modeling approach of global principal dynamic modes (PDMs) is adopted to examine the functional connectivity of the temporal and frontal lobes with the occipital brain segment using an ensemble of paediatric EEGs having the presence of epileptic seizure. The distinct spectral characteristics of global PDMs are found to be in line with the neural rhythms of brain dynamics. Moreover, we find that the linear trends of associated nonlinear functions (ANFs) associated with the 2nd and 4th global PDMs (representing delta, theta and alpha bands) of Fp1-F3 may differentiate between ictal and interictal states of epilepsy. These findings suggest that global PDMs and their associated ANFs may offer potential utility as diagnostic neural measures for ictal and interictal states of epilepsy

The estimated five global PDMs for the T7–P7 (left panels) and Fp1–F3 (right panels) inputs in connection with P3–O1 (as an output) in the time- (top panels) and frequency-domain (bottom panels).

<p>PDMs, principal dynamic modes.</p

The ensemble averages of estimated linear gain coefficients (i.e., slopes of best linear lines fitted to cubic ANFs) for the T7–P7 (upper panel) and Fp1–F3 (bottom panel) for interictal and ictal states of the training data set.

<p>No significant changes were found across any ANF of either input for ictal versus interictal states of the training data set (<i>p</i> > 0.05, paired <i>t</i>-test). The error bars represent standard deviation. ANFs, associated nonlinear functions.</p

The ensemble averages of estimated linear gain coefficients (i.e., slopes of best linear lines fitted to cubic ANFs) for the T7–P7 (upper panel) and Fp1–F3 (bottom panel) for interictal and ictal states of the test data set.

<p>No significant changes were found across any ANF of either input for ictal versus interictal states of the test data set (<i>p</i> > 0.05, paired <i>t</i>-test). The error bars represent standard deviation. ANFs, associated nonlinear functions.</p

The ensemble averages of estimated cubic ANFs along with their standard deviation bounds for the T7–P7 (top panels) and Fp1–F3 (bottom panels) for the interictal states of the training data set.

<p>The solid lines represent means and dotted lines represent standard deviation bounds. Coefficients of cubic ANFs were utilized to determine the mean and standard deviation bounds. ANFs, associated nonlinear functions.</p

The estimated five global PDMs for the T7–P7 (left panels) and Fp1–F3 (right panels) inputs in connection with P3–O1 (as an output) in the time- (top panels) and frequency-domain (bottom panels).

<p>PDMs, principal dynamic modes.</p