Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high-grade lesions.

PURPOSE: To assess the feasibility of grading soft tissue sarcomas (STSs) using MRI features (radiomics). MATERIALS AND METHODS: MRI (echo planar SE, 1.5T) from 19 patients with STSs and a known histological grading, were retrospectively analyzed. The apparent diffusion coefficient (ADC) maps, obtained by diffusion-weighted imaging acquisitions, were analyzed through 65 radiomic features, intensity-based (first order statistics, FOS) and texture (gray level co-occurrence matrix, GLCM; and gray level run length matrix, GLRLM) features. Feature selection (sequential forward floating search) and classification (k-nearest neighbor classifier) were performed to distinguish intermediate- from high-grade STSs. Classification was performed using the three different sub-groups of features separately as well as all the features together. The entire dataset was divided in three subsets: the training, validation and test set, containing, respectively, 60, 30, and 10% of the data. RESULTS: Intermediate-grade lesions had a higher and less disperse ADC values compared with high-grade ones: most of FOS related to intensity are higher for the intermediate-grade STSs, while FOS related to signal variability were higher in the high grade (e.g., the feature variance is 2.6*105 ± 0.9*105 versus 3.3*105 ± 1.6*105 , P = 0.3). The GLCM features related to entropy and dissimilarity were higher in the high-grade. When performing classification, the best accuracy is obtained with a maximum of three features for each subgroup, FOS features being those leading to the best classification (validation set: FOS accuracy 0.90 ± 0.11, area under the curve [AUC] 0.85 ± 0.16; test set: FOS accuracy 0.88 ± 0.25, AUC 0.87 ± 0.34). CONCLUSION: Good accuracy and AUC could be obtained using only few Radiomic features, belonging to the FOS class. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:829-840.

Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images.

The objectives of the study are to develop a new way to assess stability and discrimination capacity of radiomic features without the need of test-retest or multiple delineations and to use information obtained to perform a preliminary feature selection. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of two groups of patients: 18 with soft tissue sarcomas (STS) and 18 with oropharyngeal cancers (OPC). Sixty-nine radiomic features were computed, using three different histogram discretizations (16, 32, and 64 bins). Geometrical transformations (translations) of increasing entity were applied to the regions of interest (ROIs), and the intra-class correlation coefficient (ICC) was used to compare the features computed on the original and modified ROIs. The distribution of ICC values for minimal and maximal entity translations (ICC10 and ICC100, respectively) was used to adjust thresholds of ICC (ICCmin and ICCmax) used to discriminate between good, unstable (ICC10 < ICCmin), and non-discriminative features (ICC100 > ICCmax). Fifty-four and 59 radiomic features passed the stability-based selection for all the three histogram discretizations for the OPC and STS datasets, respectively. The excluded features were similar across the different histogram discretizations (Jaccard's index 0.77 ± 0.13 and 0.9 ± 0.1 for OPC and STS, respectively) but different between datasets (Jaccard's index 0.19 ± 0.02). The results suggest that the observed radiomic features are mainly stable and discriminative, but the stability depends on the region of the body under observation. The method provides a way to assess stability without the need of test-retest or multiple delineations.

Contrast mechanisms associated with neuromelanin-MRI.

PURPOSE: To investigate the physical mechanisms associated with the contrast observed in neuromelanin MRI. METHODS: Phantoms having different concentrations of synthetic melanins with different degrees of iron loading were examined on a 3 Tesla scanner using relaxometry and quantitative magnetization transfer (MT). RESULTS: Concentration-dependent T1 and T2 shortening was most pronounced for the melanin pigment when combined with iron. Metal-free melanin had a negligible effect on the magnetization transfer spectra. On the contrary, the presence of iron-laden melanins resulted in a decreased magnetization transfer ratio. The presence of melanin or iron (or both) did not have a significant effect on the macromolecular content, represented by the pool size ratio. CONCLUSION: The primary mechanism underlying contrast in neuromelanin-MRI appears to be the T1 reduction associated with melanin-iron complexes. The macromolecular content is not significantly influenced by the presence of melanin with or without iron, and thus the MT is not directly affected. However, as T1 plays a role in determining the MT-weighted signal, the magnetization transfer ratio is reduced in the presence of melanin-iron complexes. Magn Reson Med 78:1790-1800, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Pool size ratio of the substantia nigra in Parkinson's disease derived from two different quantitative magnetization transfer approaches.

PURPOSE: We sought to measure quantitative magnetization transfer (qMT) properties of the substantia nigra pars compacta (SNc) in patients with Parkinson's disease (PD) and healthy controls (HCs) using a full qMT analysis and determine whether a rapid single-point measurement yields equivalent results for pool size ratio (PSR). METHODS: Sixteen different MT-prepared MRI scans were obtained at 3 T from 16 PD patients and eight HCs, along with B1, B0, and relaxation time maps. Maps of PSR, free and macromolecular pool transverse relaxation times ([Formula: see text], [Formula: see text]) and rate of MT exchange between pools (k mf ) were generated using a full qMT model. PSR maps were also generated using a single-point qMT model requiring just two MT-prepared images. qMT parameter values of the SNc, red nucleus, cerebral crus, and gray matter were compared between groups and methods. RESULTS: PSR of the SNc was the only qMT parameter to differ significantly between groups (p < 0.05). PSR measured via single-point analysis was less variable than with the full MT model, provided slightly better differentiation of PD patients from HCs (area under curve 0.77 vs. 0.75) with sensitivity of 0.75 and specificity of 0.87, and was better than transverse relaxation time in distinguishing PD patients from HCs (area under curve 0.71, sensitivity 0.87, and specificity 0.50). CONCLUSION: The increased PSR observed in the SNc of PD patients may provide a novel biomarker of PD, possibly associated with an increased macromolecular content. Single-point PSR mapping with reduced variability and shorter scan times relative to the full qMT model appears clinically feasible.

Rate-control drugs affect variability and irregularity measures of RR intervals in patients with permanent atrial fibrillation.

INTRODUCTION: Irregularity measures have been suggested as risk indicators in patients with atrial fibrillation (AF); however, it is not known to what extent they are affected by commonly used rate-control drugs. We aimed at evaluating the effect of metoprolol, carvedilol, diltiazem, and verapamil on the variability and irregularity of the ventricular response in patients with permanent AF. METHODS AND RESULTS: Sixty patients with permanent AF were part of an investigator-blind cross-over study, comparing 4 rate-control drugs (diltiazem, verapamil, metoprolol, and carvedilol). We analyzed five 20-minute segments per patient: baseline and the 4 drug regimens. On every segment, heart rate (HR) variability and irregularity of RR series were computed. The variability was assessed as standard deviation, pNN20, pNN50, pNN80, and rMSSD. The irregularity was assessed by regularity index, approximate (ApEn), and sample entropy. A significantly lower HR was obtained with all drugs, the HR was lowest using the calcium channel blockers. All drugs increased the variability of ventricular response in respect to baseline (as an example, rMSSD: baseline 171 ± 47 milliseconds, carvedilol 229 ± 58 milliseconds; P < 0.05 vs. baseline, metoprolol 226 ± 66 milliseconds; P < 0.05 vs. baseline, verapamil 228 ± 84; P < 0.05 vs. baseline, diltiazem 256 ± 87 milliseconds; P < 0.05 vs. baseline and all other drugs). Only ß-blockers significantly increased the irregularity of the RR series (as an example, ApEn: baseline 1.86 ± 0.13, carvedilol 1.92 ± 0.09; P < 0.05 vs. baseline, metoprolol 1.93 ± 0.08; P < 0.05 vs. baseline, verapamil 1.86 ± 0.22 ns, diltiazem 1.88 ± 0.16 ns). CONCLUSION: Modification of AV node conduction by rate-control drugs increase RR variability, while only ß-blockers affect irregularity.

Noninvasive Assessment of Atrioventricular Nodal Function: Effect of Rate-Control Drugs during Atrial Fibrillation.

BACKGROUND: During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of tecadenoson and esmolol using a novel ECG-based method. METHODS: Fourteen patients (age 58 ± 8 years, 10 men) with AF were randomly assigned to either 75 or 300 µg intravenous tecadenoson. After tecadenoson wash-out, patients received esmolol continuously (100 µg/kg per min for 10 mins, then 50 µg/kg per min for 50 mins). Atrial fibrillatory rate (AFR) and heart rate (HR) were assessed in 15-min segments. Using the novel method, we assessed the absolute refractory periods of the slow and fast pathways (aRPs and aRPf) of the AV node to produce an estimate of the functional refractory period. RESULTS: During esmolol infusion, AFR and HR were significantly decreased and the absolute refractory period was significantly prolonged in both pathways (aRPs: 387 ± 73 vs 409 ± 62 ms, P < 0.05; aRPf: 490 ± 80 vs 529 ± 58 ms, P < 0.05). During both tecadenoson doses, HR decreased significantly and AFR was unchanged. Both aRPs and aRPf were prolonged for a 75 µg dose (aRPs: 322 ± 97 vs 476 ± 75 ms, P < 0.05; aRPf: 456 ± 102 vs 512 ± 55 ms, P < 0.05) whereas a trend toward prolongation was observed for a 300 µg dose. CONCLUSIONS: The estimated parameters reflect expected changes in AV nodal properties, i.e., slower conduction through the AV node for tecadenoson and prolongation of the AV node refractory period for esmolol. Thus, the proposed approach may be used to assess drug effects on the AV node in AF patients.

Noninvasive characterization of atrioventricular conduction in patients with atrial fibrillation.

The atrioventricular (AV) node plays a fundamental role in patients with atrial fibrillation (AF), acting as a filter to the numerous irregular atrial impulses which bombard the node. A phenomenological approach to better understand AV nodal electrophysiology is to analyze the ventricular response with respect to irregularity. In different cohorts of AF patients, such analysis has been performed with the aim to evaluate the association between ventricular response characteristics and long-term clinical outcome and to determine whether irregularity is affected by rate-control drugs. Another approach to studying AV nodal characteristics is to employ a mathematical model which accounts for the refractory periods of the two AV nodal pathways. With atrial fibrillatory rate and RR intervals as input, the model has been considered for analyzing data during (i) rest and head-up tilt test, (ii) tecadenoson and esmolol, and (iii) rate-control drugs. The present paper provides an overview of our recent work on the characterization and assessment of AV nodal conduction using these two approaches.

Non-invasive assessment of the effect of beta blockers and calcium channel blockers on the AV node during permanent atrial fibrillation.

AIM: We aimed at assessing changes in AV nodal properties during administration of the beta blockers metoprolol and carvedilol, and the calcium channel blockers diltiazem and verapamil from electrocardiographic data. METHODS: Parameters accounting for the functional refractory periods of the slow and fast pathways (aRPs and aRPf) were estimated using atrial fibrillatory rate (AFR) and ventricular response assessed from 15-min ECG segments recorded at baseline and on drug treatment from sixty patients with permanent AF. RESULTS: The results showed that AFR and HR were significantly reduced for all drugs, and that aRPs and aRPf were significantly prolonged in both pathways. The prolongation in aRP was significantly larger for the calcium channel blockers than for the beta blockers. CONCLUSIONS: The changes observed in the AV node parameters are in line with the results of previous electrophysiological studies performed in patients during sinus rhythm, therefore supporting the clinical value of the method.

Beta-blockade and A1-adenosine receptor agonist effects on atrial fibrillatory rate and atrioventricular conduction in patients with atrial fibrillation.

AIMS: Reduced irregularity of RR intervals in permanent atrial fibrillation (AF) has been associated with poor outcome. It is not fully understood, however, whether modification of atrioventricular (AV) conduction using rate-control drugs affects RR variability and irregularity measures. We aimed at assessing whether atrial fibrillatory rate (AFR) and variability and irregularity of the ventricular rate are modified by a selective A1-adenosine receptor agonist tecadenoson, beta-blocker esmolol, and their combination. METHODS AND RESULTS: Twenty-one patients (age 58 ± 7 years, 13 men) with AF were randomly assigned to either 75, 150, or 300 µg intravenous tecadenoson. Tecadenoson was administered alone (Dose Period 1) and in combination (Dose Period 2) with esmolol (100 µg/kg/min for 10 min then 50 µg/kg/min for 50 min). Heart rate (HR) and AFR were estimated for every 10 min long recording segment. Similarly, for every 10 min segment, the variability of RR intervals was assessed, as standard deviation, pNN20, pNN50, pNN80, and the root of the mean squared differences of successive RR intervals, and irregularity was assessed by non-linear measures such as regularity index (R) and approximate entropy. A marked decrease in HR was observed after both tecadenoson injections, whereas almost no changes could be seen in the AFR. The variability parameters were increased after the first tecadenoson bolus injection. In contrast, the irregularity parameters did not change after tecadenoson. When esmolol was infused, all the variability parameters further increased. CONCLUSION: Modification of AV node conduction can increase RR variability but does not affect regularity of RR intervals or AFR.

Non-invasive evaluation of the effect of metoprolol on the atrioventricular node during permanent atrial fibrillation.

AIMS: During atrial fibrillation (AF), conventional electrophysiological techniques for assessment of refractory period or conduction velocity of the atrioventricular (AV) node cannot be used. We aimed at evaluating changes in AV nodal properties during administration of metoprolol from electrocardiogram data, and to support our findings with simulated data based on results from an electrophysiological study. METHODS AND RESULTS: Sixty patients (age 71 ± 9 years, 42 men) with permanent AF were included in the RATe control in Atrial Fibrillation (RATAF) study. Two 15 min segments, during baseline and metoprolol administration, starting at 2 pm were analysed in this study. Atrial fibrillatory rate (AFR), heart rate (HR), and AV nodal parameters were assessed. The AV nodal parameters account for the probability of an impulse not taking the fast pathway, the absolute refractory periods of the slow and fast pathways (aRPs and aRPf), representing the functional refractory period, and their respective prolongation in refractory period. In addition, simulated RR series were generated that mimic metoprolol administration through prolonged AV conduction interval and AV node effective refractory period. During metoprolol administration, AFR and HR were significantly decreased and aRP was significantly prolonged in both pathways (aRPs: 337 ± 60 vs. 398 ± 79 ms, P < 0.01; aRPf: 430 ± 91 vs. 517 ± 100 ms, P < 0.01). Similar results were found for the simulated RR series, both aRPs and aRPf being prolonged with metoprolol (aRPs: 413 ± 33 vs. 437 ± 43 ms, P = 0.01; aRPf: 465 ± 40 vs. 502 ± 69 ms, P = 0.02). CONCLUSION: The AV nodal parameters reflect expected changes after metoprolol administration, i.e. a prolongation in functional refractory period. The simulations confirmed that aRPs and aRPf may serve as an estimate of the functional refractory period.

Association between atrial fibrillatory rate and heart rate variability in patients with atrial fibrillation and congestive heart failure.

BACKGROUND: Even if atrial fibrillatory rate (AFR) has been related to clinical outcome in patients with atrial fibrillation (AF), its relation with ventricular response has not been deeply studied. The aim of this study was to investigate the relation between AFR and RR series variability in patients with AF. METHODS: Twenty-minute electrocardiograms in orthogonal leads were processed to extract AFR, using spatiotemporal QRST cancellation and time frequency analysis, and RR series in 127 patients (age 69 ± 11 years) with congestive heart failure (NYHA II-III) enrolled in the MUSIC study (MUerte Subita en Insufficiencia Cardiaca). Heart rate variability and irregularity were assessed by time domain parameters and entropy-based indices, respectively and their correlation with AFR investigated. RESULTS: Variability measures seem not to be related to AFR, while irregularity measures do. A significant correlation between AFR and variability parameters of heart rate variability during AF was found only in patients not treated with antiarrhythmics drugs (correlation = 0.56 P < 0.05 for pNN50), while this correlation was lost in patients taking rate- or rhythm-control drugs. A significant positive correlation between AFR and indices of RR irregularity was found, showing that a higher AFR is related to a less organized RR series (correlation = 0.33 P < 0.05 for regularity index for all patients, correlation increased in subgroups of patients treated with the same drug). CONCLUSIONS: These results suggest that a higher AFR is associated with a higher degree of irregularity of ventricular response that is observed regardless of the use of rate-controlling drugs.

Theoretical comments on reproducibility and normalization of TWA measures.

Using a simple stochastic model of ventricular repolarization and the equivalent surface source (ESS) model, an electrophysiological formulation relating surface ECG to variations at the myocytes' level, we recently pointed out a few theoretical results regarding T-wave alternans (TWA). In this paper, stimulated by the comments of John E. Madias on our paper (J Electrocardiol, 2012), we further explored the consequences implied by the theoretical model. First, we verified the reproducibility of TWA measures, in clinically stable patients repeatedly tested. The sensitivity to displacement was evaluated simulating lead mislocations of up to 20mm. The numerical simulations were performed on data obtained solving the inverse electrocardiographically problem from three subjects (ECGSIM). The results showed that TWA sensitivity varies across leads, being maximal in V1 and decreases towards V6. Globally, the maximal percent error found was 6.1%. Thus, TWA measures do not seem to add more stringent requirements on lead placement's precision, than the usual diagnostic practice. Finally, we further discussed the implications of normalizing TWA measures. While clinical studies are necessary to sort out the issue, the theoretical model suggests that normalization might be appropriate only is certain cases.

Heart rate characteristic based modelling of atrial fibrillatory rate using implanted cardiac monitor data.

Objective. The objective of the present study is to investigate the feasibility of using heart rate characteristics to estimate atrial fibrillatory rate (AFR) in a cohort of atrial fibrillation (AF) patients continuously monitored with an implantable cardiac monitor. We will use a mixed model approach to investigate population effect and patient specific effects of heart rate characteristics on AFR, and will correct for the effect of previous ablations, episode duration, and onset date and time.Approach. The f-wave signals, from which AFR is estimated, were extracted using a QRST cancellation process of the AF episodes in a cohort of 99 patients (67% male; 57 ± 12 years) monitored for 9.2(0.2-24.3) months as median(min-max). The AFR from 2453 f-wave signals included in the analysis was estimated using a model-based approach. The association between AFR and heart rate characteristics, prior ablations, and episode-related features were modelled using fixed-effect and mixed-effect modelling approaches.Main results. The mixed-effect models had a better fit to the data than fixed-effect models showing h.c. of determination (R2 = 0.49 versusR2 = 0.04) when relating the variations of AFR to the heart rate features. However, when correcting for the other factors, the mixed-effect model showed the best fit (R2 = 0.04). AFR was found to be significantly affected by previous catheter ablations (p< 0.05), episode duration (p< 0.05), and irregularity of theRRinterval series (p< 0.05).Significance. Mixed-effect models are more suitable for AFR modelling. AFR was shown to be faster in episodes with longer duration, less organizedRRintervals and after several ablation procedures.

Atrial fibrillation episode patterns as predictor of clinical outcome of catheter ablation.

Methods for characterization of atrial fibrillation (AF) episode patterns have been introduced without establishing clinical significance. This study investigates, for the first time, whether post-ablation recurrence of AF can be predicted by evaluating episode patterns. The dataset comprises of 54 patients (age 56 ± 11 years; 67% men), with an implantable cardiac monitor, before undergoing the first AF catheter ablation. Two parameters of the alternating bivariate Hawkes model were used to characterize the pattern: AF dominance during the monitoring period (log(mu)) and temporal aggregation of episodes (beta1). Moreover, AF burden and AF density, a parameter characterizing aggregation of AF burden, were studied. The four parameters were computed from an average of 29 AF episodes before ablation. The risk of AF recurrence after catheter ablation using the Hawkes parameters log(mu) and beta1, AF burden, and AF density was evaluated. While the combination of AF burden and AF density is related to a non-significant hazard ratio, the combination of log(mu) and beta1 is related to a hazard ratio of 1.95 (1.03-3.70; p < 0.05). The Hawkes parameters showed increased risk of AF recurrence within 1 year after the procedure for patients with high AF dominance and high episode aggregation and may be used for pre-ablation risk assessment.

Repeatability and reproducibility of MRI-radiomic features: A phantom experiment on a 1.5 T scanner.

PURPOSE: Aim of this study is to assess the repeatability of radiomic features on magnetic resonance images (MRI) and their stability to variations in time of repetition (TR), time of echo (TE), slice thickness (ST), and pixel spacing (PS) using vegetable phantoms. METHODS: The organic phantom was realized using two cucumbers placed inside a cylindrical container, and the analysis was performed using T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted images. One dataset was used to test the repeatability of the radiomic features, whereas other four datasets were used to test the sensitivity of the different MRI sequences to image acquisition parameters (TR, TE, ST, and PS). Four regions of interest (ROIs) were segmented: two for the central part of each cucumber and two for the external parts. Radiomic features were extracted from each ROI using Pyradiomics. To assess the effect of preprocessing on the reduction of variability, features were extracted both before and after the preprocessing. The coefficient of variation (CV) and intra-class correlation coefficient (ICC) were used to evaluate variability. RESULTS: The use of intensity standardization increased the stability for the first-order statistics features. Shape and size features were always stable for all the analyses. Textural features were particularly sensitive to changes in ST and PS, although some increase in stability could be obtained by voxel size resampling. When images underwent image preprocessing, the number of stable features (ICC > 0.75 and mean absolute CV < 0.3) was 33 for apparent diffusion coefficient (ADC), 52 for T1w, and 73 for T2w. CONCLUSIONS: The most critical source of variability is related to changes in voxel size (either caused by changes in ST or PS). Preprocessing increases features stability to both test-retest and variation of the image acquisition parameters for all the types of analyzed MRI (T1w, T2w, and ADC), except for ST.

T-wave alternans: lessons learned from a biophysical ECG model.

T-wave alternans (TWA) is an alteration of the ECG T-wave which repeats every other beat. An alternating pattern has been also observed at myocytes level, involving both action potential duration and morphology (mainly in phases 2 and 3). While this might happen in a specific region (i.e., myocardial ischemia), it can also involve the entire myocardium. It is still unclear how alternations at the myocytes level are reflected on surface ECG modification of T-waves, especially when in vivo human hearts are considered. We have recently proposed a simple stochastic model of ventricular repolarization (IEEE Trans. Biomed. Eng., 2011), which takes into account both repolarization heterogeneity across the myocardium as well as random beat-to-beat variations in cells' activity. In this work, we generalized that model incorporating a term which describes myocytes alternans related to T-wave variability. Starting from the model and using the electrophysiological formulation developed by van Oosterom, we derived an analytical formula relating surface ECG to variations at the myocytes' level. Several theoretical results were then obtained. First, temporal small random variations in repolarization heterogeneity affect the precision of TWA estimates in a significant way. Second, TWA theoretically differs across leads, but multilead configuration can be used to reduce the effect of noise. Finally, the dependency between TWA and T-wave amplitude was analyzed.

Ensemble classification combining ResNet and handcrafted features with three-steps training.

Objective.This work presents an ECG classifier for variable leads as a contribution to the Computing in Cardiology Challenge/CinC Challenge 2021. It aims to integrate deep and classic machine learning features into a single model, exploring the proper structure and training procedure.Approach.From the initial 88 253 signals, only 84 210 were included. Low quality and unscored recordings were excluded. Three different database subsets of 40 365 recording each were created by dividing in three normal sinus rhythm and sinus bradycardia recordings. Each subset was used to train a different model with shared architecture integrated as an ensemble to provide the final classification through major voting. Models contained a deep branch composed of a modified ResNet with dilation convolutional layers and squeeze and excitation Block that took as input windowed ECG signals. This was concatenated with a wide branch that integrated 20 cardiac rhythm features into a fully connected 3-layered network. Three different training steps were studied: just the deep branch (D), wide integration and training (D+W), and a final fine tuning of the deep branch posterior to wide training (D+W+D).Main Results.Results obtained in a local test set formed by a stratified 12.5% split of the given full dataset were presented for 2-lead and 12-lead models. The best training method was the 3-step D + W + D procedure obtaining a challenge metric of 0.709 and 0.677 for 12 and 2-lead models respectively.Significance.Integration of handcrafted features and deep learning model not only may increase the generalization capacity of the network but also provide a path to add explicit information into the classification decision process. To the best of our knowledge this is the first work studying the training procedure to properly integrate both types of information for ECG signals classification.

Association between ventricular repolarization parameters and cardiovascular death in patients of the SWISS-AF cohort.

BACKGROUND: The effect of the ventricular repolarization heterogeneity has not been systematically assessed in patients with atrial fibrillation (AF). Aim of this study is to assess ventricular repolarization heterogeneity as predictor of cardiovascular (CV) death and/or other CV events in patients with AF. METHODS: From the multicenter prospective Swiss-AF (Swiss Atrial Fibrillation) Cohort Study, we enrolled 1711 patients who were in sinus rhythm (995) or AF (716). Resting ECG recordings of 5-min duration were obtained at baseline. Parameters assessing ventricular repolarization were computed (QTc, Tpeak-Tend, J-Tpeak and V-index). RESULTS: During AF, the V-index was found repeatable (no differences when computed over the whole recording, on the first 2.5-min and on the last 2.5-min segments). During a mean follow-up time of 2.6 ± 1.0 years, 90 patients died for CV reasons. In bivariate Cox regression analysis (adjusted for age only), the V-index was associated with an increased risk of CV death, both in the subgroup of patients in sinus rhythm (SR) as well as those in AF. In multivariate analysis adjusted for clinical risk factors and medications, both prolonged QTc and V-index were independently associated with an increased risk of CV death (QTc: hazard ratio [HR] 2.78, 95% CI 1.79-4.32, p < 0.001; V-index: HR 1.73, 95% CI 1.12-2.69, p = 0.014). CONCLUSIONS: QTc and V-index, measured in a single 5-min ECG recording, were independent predictors of CV death in a cohort of patients with AF and might be a valuable tool for further risk stratification to guide patient management. Clinical Trial Identifier Swiss-AF study: NCT02105844.

Heart Rate Variability and Clinical Features as Predictors of Atrial Fibrillation Recurrence After Catheter Ablation: A Pilot Study.

Single-procedure catheter ablation success rate is as low as 52% in atrial fibrillation (AF) patients. This study evaluated the feasibility of using clinical data and heart rate variability (HRV) features extracted from an implantable cardiac monitor (ICM) to predict recurrences in patients prior to undergoing catheter ablation for AF. HRV-derived features were extracted from the 500 beats preceding the AF onset and from the first 2 min of the last AF episode recorded by an ICM of 74 patients (67% male; 57 ± 12 years; 26% non-paroxysmal AF; 57% AF recurrence) before undergoing their first AF catheter ablation. Two types of classification algorithm were studied to predict AF recurrence: single classifiers including support vector machines, classification and regression trees, and K-nearest neighbor classifiers as well as ensemble classifiers. The sequential forward floating search algorithm was used to select the optimum feature set for each classification method. The optimum weighted voting method, which used an optimum combination of the single classifiers, was the best overall classifier (accuracy = 0.82, sensitivity = 0.76, and specificity = 0.87). Clinical and HRV features can be used to predict rhythm outcome using an ensemble classifier which would enable a more effective pre-ablation patient triage that could reduce the economic and personal burden of the procedure by increasing the success rate of first catheter ablation.

Atrial fibrillation detection using photoplethysmographic signal: the effect of the observation window.

A correct and early diagnosis of cardiac arrhythmias could improve patients' quality of life. The aim of this study is to classify the cardiac rhythm (atrial fibrillation, AF, or normal sinus rhythm NSR) from the photoplethysmographic (PPG) signal and assess the effect of the observation window length. Simulated signals are generated with a PPG simulator previously proposed. The different window lengths taken into account are 20, 30, 40, 50, 100, 150, 200, 250 and 300 beats. After systolic peak detection algorithm, 10 features are computed on the inter-systolic interval series, assessing variability and irregularity of the series. Then, feature selection was performed (using the sequential forward floating search algorithm) which identified two variability parameters (Mean and rMSSD) as the best selection. Finally, the classification by linear support vector machine was performed. Using only two features, accuracy was very high for all the analyzed observation window lengths, going from 0.913±0.055 for length equal to 20 to 0.995±0.011 for length equal to 300 beats.Clinical relevance These preliminary results show that short PPG signals (20 beats) can be used to correctly detect AF.