bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving omnipolar electrogram reconstruction: an animal model study
Authors: Ruiperez-Campillo S , Castells F , Casado-Arroyo R , Merino J L , Millet J
Published: November 2023

Background

Cardiac arrhythmias are a significant cause of morbidity and mortality worldwide. The characterization of cardiac tissue's electrophysiological substrate is crucial for the diagnosis and treatment of these conditions. High-density (HD) electrode arrays, such as the Advisor™ HD Grid Mapping Catheter and Sensor Enabled™ (SE), are commonly used to examine cardiac tissue locally and create activation maps. However, the current method for reconstructing omnipolar EGMs, a novel technique claimed to be orientation-independent, has limitations. The enhancement of this reconstruction methodology has the potential to improve the accuracy and reliability of electrophysiological mapping. This, in turn, can have a positive impact on patients suffering from cardiac arrhythmias, ultimately leading to better clinical outcomes.

Purpose

We propose a novel reconstruction method to estimate omnipolar EGM that overcomes the angle dependence weakness of the current method to enhance the reconstruction of electrophysiological signals.

Methodology

Prior experiments were conducted on isolated rabbit hearts using a 128-electrode array with electrodes spaced 1mm apart over the ventricular epicardium. Four-electrode cliques were designed from these experiments, with different inter-electrode distances as depicted in Figure 1A, to reconstruct the current and novel omnipolar configurations. Metrics were designed to assess the quality of the electrogram signals, including the bipolar loop area and the ratio of the bipolar components of the omnipole (see fig. 2). The bipoles were calculated from unipolar electrograms, and the signal was corrected with a 45º offset in the cross. The propagation angle was then detected, and the local activation time (LAT) was estimated.

Results

In the bipole plots, it was observed that the morphology of the bipolar loop becomes distorted with an increase in the inter-electrode distance, becoming thinner for distances less than 2mm. There is a direct relation between the areas in the bipolar loop and the inter-electrode distance (see Figure 2A). Furthermore, the values of the area in the cross are always comparable to the best-performing triangular clique. For the ratio of bipoles, the relation is inversely related to distance (see Figure 2B). Similarly, the performance of the cross is comparable to the best-performing triangular omnipole.

Conclusion

This study highlights the importance of accurate reconstruction of omnipolar EGMs for the diagnosis and treatment of cardiac arrhythmias. To achieve precise reconstruction, inter-electrode distances smaller than 2mm are recommended. The proposed reconstruction method based on the cross configuration is more accurate and robust to wavefront propagation, provided that the recommended distance criterion is met. This study opens up new possibilities for the design of new devices and practices in the clinic.Figure 1Figure 2
Comparison of vest-recorded electrocardiogram versus conventional electrocardiogram concerning ST-deviation in acute coronary syndrome
Authors: Parale G , Parale C , Parale H , Kolhapure S , Gandhi V
Published: November 2023

Background and Aim

With the 12 lead ECG being the cornerstone in the diagnosis of ACS Development of a home-based, user-friendly recording of a 12 lead ECG will be a breakthrough in combating delay in diagnosing Acute Coronary Syndrome (ACS). This study evaluates the potential of a wearable vest 12-lead ECG (V-ECG) compared with the gold standard, conventional 12-lead ECGs (C-ECG) in diagnosing ACS.

Methodology

A prospective single-centre observational study conducted in a tertiary care centre. C-ECG and wearable V-ECG were recorded in hemodynamically stable adult patients and patients with normal ECG. The location of the electrodes in the vest included ten specific torso positions. Two independent physicians interpreted all the ECGs in a blinded fashion.

Results

Out of 241 patients, the vest fitted 236 patients (97.9%). Among 234 patients (72.5% males, 27.5% females; mean age 57.5 years), 154 had ACS. Three patients had an additional set of ECGs recorded, given their dynamic ECG changes during hospitalisation. The sensitivity and specificity of V-ECG, compared with the gold standard C-ECG for the diagnosis of ACS, were 98.7% ( CI 95.36-99.77) and 96.5% (CI 90.2-99.1), respectively. Among patients with STEMI, the sensitivity of V-ECG went up to 100% (CI 95.9-100). V-ECG has correctly identified anterior and inferior STEMI patients with 100% accuracy.

Conclusion

12 lead ECG recording by a wearable garment using novel torso positions is feasible in the vast majority, including both genders, and reasonably accurate for diagnosing ACS.

Highlights

Commercial availability of this vest might enable an otherwise lay person to record an ECG in an out-of-hospital setting (even at home), thus leading to an expedited diagnosis of ACS and saving precious time and lives.
Duration of left and right atrial activation wavefront assessed non-invasively is significantly higher in patients with atrial fibrillation than in controls
Authors: Parreira L , Carmo P , Nunes S , Zubarev S , Chmelevsky M , Marinheiro R , Mesquita D , Amador P , Ferreira A , Farinha J , Adragao P
Published: November 2023

Introduction

Ablation strategies in atrial fibrillation (AF) are currently a "one-size- fits- all" type of approach. Pulmonary vein isolation as the standard of care is not perfect, especially in the persistent forms, but none of the alternatives has shown better results. The dichotomic paroxysmal versus persistent AF classification in no longer acceptable. Characterization of the atrial substrate before ablation is needed. Electrocardiographic imaging (ECGI) is capable of non-invasively assess atrial activation in a single beat and in a panoramic fashion. The aim of this study was to evaluate the feasibility and accuracy of ECGI to assess the activation speed and identify possible targets for ablation.

Methods

We studied 18 consecutive patients, 9 referred for ablation of paroxysmal AF and 9 without AF, referred for ablation of premature ventricular contractions (PVCs), that had an ECGI performed before ablation. No patient had structural heart disease and all patients had normal electrocardiographic evaluation in sinus rhythm (SR). The ECGI was performed in SR with the AMYCARD 01 system, based on body surface electrocardiograms of a maximum of 224 electrodes and computed tomography imaging data. Total duration of atrial activation, left atrial (LA), and right atrial (RA) activation duration, and presence of lines of conduction block (LOB) defined as the presence of a jump in the local activation time of 50 ms or more between contiguous points, were assessed. The results were compared in patients with and without AF.

Results

The results are depicted in the Table. Patients with AF were older. Total atrial activation duration, LA, and RA activation duration were longer in patients with AF and there was a trend towards a higher prevalence of LOB in the LA in patients with AF (Figure).

Discussion & Conclusion

Total atrial activation duration as well as LA and RA activation duration assessed with the ECGI were significantly prolonged in patients with AF. Presence of LOB in the LA showed a trend towards a higher prevalence in AF patients. These pilot study has proved that this technology is accurate in identifying a pathological substrate in AF patients. We intend to use ECGI in a subsequent study that will include patients with AF both in paroxysmal and persistent forms, in order to identify markers of disease as well as possible targets to perform a customized AF ablation.Comparison between AF and control groupsECGI showing local activation times
The performance of preliminary version of irregular heart rhythm notification algorithm of a consumer smartwatch for screening of atrial fibrillation
Authors: Lee J H , Cho Y , Oh I Y , Lee S , Kim M , Lee J , Lee W , Kwon S , Seo H , Han J , Seo J
Published: November 2023

Background

Smartwatch photoplethysmography (PPG) is a useful non-invasive tool for atrial fibrillation (AF) screening. However, the process of signal acquisition is vulnerable to variable factors such as motion, respiration, and unstable sensor contact which may lead to false positive results. Thus, an optimal signal acquisition and processing algorithm is needed to balance the inherent trade-off between sensitivity and specificity for smartwatch PPG to be used for AF screening in daily life.

Purpose

This study aimed to evaluate the efficacy of the preliminary version of the Irregular Heart Rhythm Notification (IHRN) feature of a consumer smartwatch for AF screening.

Methods

We obtained time-synchronized PPG and ECG signals using smartwatches and wearable ECG patches in 49 patients with paroxysmal and persistent AF in a free-living setting for up to 7 days. The IHRN algorithm selectively schedules PPG tachogram data acquisition at specific time interval, classifies heart rhythm for each interval, and integrates the classification results to determine whether to alert the user of their irregular heart rhythm. We tested the diagnostic performance of the IHRN algorithm for identifying AF episodes lasting 30 minutes or more.

Results

We obtained a total of 364,260 minutes of ECG recording and time-synchronized 27,524 minutes of PPG signal in 49 AF patients. Out of the PPG signals collected, 19,341 minutes (70.3%) were considered valid for the analysis. On the ECG analysis, 754 AF episodes (≥30 seconds) were identified. Longer-lasting AF episodes had a higher likelihood of being captured by PPG and consequently classified as AF by the algorithm (Figure 1). AF episodes lasting over 30 minutes were identified in 28 out of 49 patients. Regarding the subjective-level analysis, the sensitivity and specificity of IHRN for AF ≥ 30 minutes were 82.1% and 100.0%, respectively. In tachogram-level analysis, the positive predictive value was 98.7%.

Conclusions

The preliminary version of the IHRN algorithm of the smartwatch demonstrated excellent performance in identifying AF in a free-living setting. Further large-scale research is needed to determine whether this algorithm is useful for AF screening in a real-world setting.Figure 1
Combining hypothesis-driven ECG indices with machine learning improves ventricular arrhythmic risk prediction in a low-risk population
Authors: Ramirez J , Martinez J P , Van Duijvenboden S , Tinker A , Lambiase P , Orini M , Munroe P B , Miguel A
Published: November 2023

Background

Life-threatening ventricular arrhythmias (LTVA) are a leading cause of mortality, but early identification of high-risk individuals remains a major challenge. An ECG index, TMV (T-wave morphology variations), predicts LTVA in 51,794 individuals without known cardiovascular disease from the UK Biobank (UKB) within a 10-year follow up (cohort 1), with an area under the ROC curve (AUC) of 0.558 and a hazard ratio (HR) of 1.57. Machine learning on the ECG has shown a high accuracy in detecting patients with cardiovascular disease, but its LTVA predictive value in a low-risk population and comparisons with hypothesis-driven risk markers, like TMV, is unknown.

Purpose

We tested the LTVA predictive value of a score combining a multi-layer convolutional neural network (CNN) model and TMV in individuals without known cardiovascular disease.

Methods

Using UKB we split the individuals from cohort 1 into training (90%) and internal test (10%) sets (Figure 1). In the training set, we applied 10-fold cross validation to train a multi-layer CNN with an attention layer, where the input was a 15-second ECG at rest (lead I), and the output was occurrence (or not) of LTVA within a 10-year follow-up. We used 10-second ECG data (lead I) from an independent cohort of 32,209 individuals without cardiovascular disease from UKB (cohort 2) as an external test set (median follow-up was 3.4 years), where we also calculated TMV. We next combined them into a score (weighted by their relative contribution to LTVA in a logistic regression). The AUC, and Cox regression HRs were calculated to estimate the performance of the CNN, TMV and the combined score.

Results

In cohort 1, 217 subjects had a LTVA during the follow-up period. In the internal test set, the AUC of the CNN was 0.707. In the external test set (60 LTVA events during the follow-up period), the CNN’s prediction and TMV led to AUCs of 0.610 and 0.604, respectively. Interestingly, the CNN’s prediction and TMV were not correlated (ρ = 0.005), and the combined score led to an AUC of 0.627. We set a threshold at the score value that maximised sensitivity and specificity, and survival analyses showed a HR of 3.007 (P < 0.0001) for individuals in the score+ (score > threshold) versus those in the score- (score < threshold) group, after adjusting for age and gender (Figure 2). The continuous score also predicted LTVA independently from age and gender.

Conclusions

A CNN-based model predicts LTVA in a low-risk population independently from TMV, a strong ECG risk predictor derived from knowledge on the underlying electrophysiology. When TMV and the CNN are combined into a score, the LTVA risk stratification improves, independently from age and gender. Our findings support the combination of hypothesis-based risk markers and CNN approaches to optimise LTVA prediction in a low-risk population.Study designSurvival curves
Reducing the burden of inconclusive smart-device single-lead ECG tracings via a novel atificial intelligence algorithm
Authors: Weidlich S , Mannhart D , Kennedy A , Doggart P , Serban T , Knecht S , Du Fay De Lavallaz J , Kuehne M , Sticherling C , Badertscher P
Published: November 2023

Introduction

Multiple smart-devices capable of automatically detecting atrial fibrillation (AF) based on single-lead electrocardiograms (SL-ECG) are presently available. The rate of inconclusive tracings by the manufacturers’ algorithms is currently too high to be clinically useful. Reliable artificial intelligence (AI) algorithms may be valuable for managing the large amount of data created by smart-devices.

Method

This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart-device agnostic AI-based algorithm for detecting AF from four different commercially available smart-devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12-lead ECG and four smart-device SL-ECGs. Single-lead ECGs were exported as PDF files and converted into Scalable Vector Graphics (SVG) images for signal extraction. The novel AI-algorithm was compared with each manufacturer’s algorithm.

Results

We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI-algorithm vs. manufacturer algorithm were: 88% vs. 81% (p=0.34) and 97% vs. 77% (p<0.001) for the AliveCor KardiaMobile, 86% vs. 81% (p=0.45) and 95% vs. 83% (p<0.001) for the Apple Watch 6, 91% vs. 67% (p<0.01) and 94% vs. 82% (p<0.001) for the Fitbit Sense, 86% vs. 82% (p=0.63), and 94% vs. 80% (p<0.001) for the Samsung Galaxy Watch3, respectively. (Figure 1) In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart-devices using the AI-based algorithm compared to manufacturer’s algorithms (14-17%) p<0.001.

Conclusion

A novel AI-algorithm reduced the rate of inconclusive SL-ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers’ algorithms.Central Illustration
Artificial intelligence-assisted examination of cardiac health status through electrocardiogram
Authors: Sung D Y , Sung S H , Huang J T , Huang W M
Published: November 2023

Background

Cardiac diseases are the main causes of death. Echocardiogram is the standard testing tool of heart diseases but it is inconvenient and expensive. This study aims at developing an artificial intelligence (AI)-assisted method to interpret electrocardiograms (ECG) for cardiac health status.

Method

A total of 70757 subjects undergoing concomitant ECG and echocardiography were enrolled. ECG was analyzed by using Philips DXL algorithm to obtain 512 parameters, and a healthy heart is defined by normal cardiac chamber size, myocardial thickness, systolic function, diastolic function, and valvular function. A multi-layer perceptron (MLP) and Long short term memory (LSTM) were developed with hold-out validation (training dataset 80% and testing dataset 20%). Three categories of input data are made based on the percentage of healthy data of 30.6%. Death of the participants were confirmed via linking database to the National Death Registry A Kaplan-Meier survival curve analysis was conducted in the testing dataset stratified by AI-determined cardiac health status.

Results

The testing results of the proposed methods on an independent dataset are shown in Table 1. It is interesting that using MLP has similar performance as the LSTM model. Furthermore, the 30-layer MLP model has a F1 value of 83.8% and the 50-layer MLP model has a F1 value of 83.8, while the F1 value of 2-layer LSTM_0.2 is 85.2% and 2-layer LSTM_0.2 is 84.6%. In addition, AI-determined healthy hearts were associated with better long-term survival.

Conclusion

An AI-assisted analysis on ECG can act as a preliminary examination of cardiac diseases to facilitate the arrangement of echocardiograms for early detections.
The exploratory clinical trial of AI-enabled Holter electrocardiogram for diagnosis of paroxysmal atrial fibrillation from sinus rhythm waveforms
Authors: Tamura Y , Taniguchi H , Iwasawa J , Watanabe E , Ogawa S , Takechi M , Takata T , Taniguchi T
Published: November 2023

Backgrounds

Machine learning (ML) models using convolutional neural networks (CNNs) have been successfully applied to arrhythmia detection and prediction. The current study aimed to develop a CNN-based AI algorithm for the prediction of paroxysmal atrial fibrillation (PAF) patients during sinus rhythm waveforms from Holter electrocardiograms.

Methods

A ML model was developed by using 2-leads from Holter ECG waveforms divided into slots of 30 seconds each. As training data, we prepared > 300000 slots with sinus rhythm and they were subsequently converted to image data for application to the CNN-based AF detection algorithm. The performance metrics obtained for the ML model for PAF detection were as follows: sensitivity, 85.2 %; specificity, 64.0 % and AUC for PAF prediction was 0.84. To validate the performance of the ML model, multicenter clinical trial was conducted. The clinical trial was conducted at three independent centers and included 24 patients with PAF and 20 patients in true sinus rhythm. Five factors were matched between the PAF and sinus groups: age, gender, hypertension under treatment, diabetes under treatment, and smoking. Waveform data from validation datasets to Grad-CAM were applied to obtain explainable AI (XAI) images, which allowed for a heat map over the waveform to be produced.

Results

For the primary endpoint, the estimated sensitivity and specificity [95% CI] were 91.7% [73.0-99.0%] and 65.0% [40.8-84.6%], respectively (Table 1). The sensitivity and specificity of the secondary endpoints using 10 time-independent slots were the same as for the primary endpoint. Regarding safety endpoints, no adverse events attributable to the test device itself were observed. Based on the regions of interest highlighted via Grad-CAM, the most contributing featured regions to predict PAF were indicated by areas on P waves and QT area (Figure 1). Featured regions to rule out the existence of PAF indicated areas just on QRS waves were also present. It was therefore determined that the proposed CNN-based ML model focuses on the P-wave area and atrial repolarization area in the case of PAF.

Conclusion

The study suggested that 2-lead Holter ECG with 30 second slot could detect PAF patients from their sinus rhythm waveforms and the performance was more effectively than using 12-lead ECGs. The results of this clinical trial are expected to contribute to the screening of patients with PAF that could not be detected by a single time Holter ECG.Table 1Figure 1
Validation of electrocardiographic artificial intelligence model for outcome prediction in patients with heart failure
Authors: Cho Y , Yoon M , Kim J , Lee J H , Oh I Y , Park J J , Lee C J , Kang S M , Choi D J
Published: November 2023

Introduction

Although several valuable biomarkers have been introduced for heart failure (HF) patients, their utilization in routine clinical practice is often constrained by cost and limited availability. Electrocardiogram (ECG) is an essential and cost-effective tool for evaluating cardiovascular diseases. We tested artificial intelligence (AI) algorithm analyzing a printed ECG image for the outcome prediction in patients with acute HF.

Methods

Two tertiary centers prospectively enrolled 1,254 patients with acute HF syndrome. Baseline ECG images were analyzed using a deep learning system called Quantitative ECG (QCG™), which features a CNN-based binary classifier trained to detect several urgent clinical conditions including shock, cardiac arrest, myocardial infarction, and HF, in addition to rhythm diagnosis.

Results

Patients with reduced left ventricular ejection fraction (LVEF, <40%) had significantly higher QCG scores for LV dysfunction (QCG-LVdys) (0.80 ± 0.20 vs. 0.38 ± 0.27, P < 0.001), and the AUC of QCG-LVdys for low LVEF was 0.884. Fifty-three patients (4.2%) resulted in in-hospital cardiac death (IHCD), and QCG score for critical events (QCG- Critical) was significantly higher in these patients than in survivors (0.57 ± 0.23 vs. 0.29 ± 0.20, P < 0.001). QCG-Critical was an independent predictor for IHCD after adjustment for age, sex, comorbidities, etiology/type of HF, atrial fibrillation, and QRS widening (adjusted OR = 1.68 [95% CI, 1.47 – 1.92], P <0.001, per 0.1). After further adjustments for echocardiographic LVEF and NTproBNP, QCG-Critical was still significantly correlated with IHCD (adjusted OR = 1.59 [95% CI, 1.36 – 1.87], P <0.001, per 0.1). The AUC of QCG-Critical for IHCD was 0.821 which was higher than the AUC of echocardiographic LVEF (P<0.001) or NTproBNP (P=0.07) (Fig 1A). The AUC of the multivariate model with QCG-Critical and other covariates was 0.866 (Fig 1B). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) showed higher all-cause mortality rate compared to those with low QCG-Critical scores (<0.25) (adjusted HR = 2.69 [2.14 – 3.38], P < 0.001) (Fig 2).

Conclusion

Predicting outcomes in patients with acute HF appeared feasible using the newly developed Quantitative ECG (QCG) scores. These results suggest the possibility that QCG may serve as a novel biomarker for HF patients.ROC curves for In-hospital cardiac deathSurvival curves according to QCG scores
ECG-based deep learning for detecting epicardial coronary occlusion in acute myocardial infarction
Authors: Herman R , Meyers H P , Smith S W , Demolder A , Bertolone D T , Leone A , Bermpeis K , Viscusi M M , Boza V , Kresnakova V , Martonak M , Perl L , Hatala R , Bartunek J , Barbato E
Published: November 2023

Background

One third of Non-ST-elevation myocardial infarction (NSTEMI) patients have occlusion myocardial infarction (OMI) associated with poor short- and long-term outcomes due to delayed invasive management.

Purpose

We sought to develop and externally validate a versatile artificial intelligence (AI)-model detecting OMI on single standard 12-lead electrocardiograms (ECGs) and compare its performance to existing state-of-the-art criteria.

Methods

An AI model was developed using 18,616 ECGs from 10,692 unique contacts (22.9% OMI) of 10,543 patients (age 66±14 years, 65.9% males) with acute coronary syndrome (ACS) originating from an international database and a tertiary care center. This AI model was tested on a holdout set of 3,254 ECGs from 2,263 unique contacts (20% OMI) of 2,222 patients (age 62±14 years, 67% males) and compared with STEMI criteria and annotations of ECG experts in detecting OMI on 12-lead ECGs using sensitivity, specificity, and predictive values.

Results

The OMI AI model achieved an AUROC of 0.941 (95% CI: 0.926, 0.954) in identifying the primary outcome of OMI on 12-lead ECGs in the holdout set [Figure 1A], yielding robust performance across genders and age subgroups (ranging from 0.907 to 0.951 AUROC) [Figure 1B]. Sensitivity in detecting OMI was significantly higher for OMI AI model compared to STEMI criteria (82.6% [95% CI: 78.9%, 86.1%] vs. 34.4% [95% CI: 30.0%, 38.8%]) and statistically equal compared to ECG experts 75.9% [95% CI: 71.9%, 80%] in identifying OMI [Table 1]. The OMI AI Model showed statistically superior performance compared to STEMI criteria and equal (non-inferior) performance to ECG experts when evaluated using industry-standard metrics.

Conclusions

This sizeable validation of an AI ECG model demonstrates the ability of AI to detect acute OMI and suggests a potential in improving ACS patient triage and need for immediate revascularization.AI Model Performance on Holdout SetBenchmark Comparison
Development of a machine learning model using 12-lead ECG to improve acute dianosis of pulmonary embolism
Authors: Silva B V , Marques J , Nobre Menezes M , L Oliveira A , J Pinto F
Published: November 2023

Introduction

Pulmonary embolism (PE) is a life-threatening condition. Given the lack of specificity in symptoms and clinical decision rules, diagnostic uncertainty in PE remains high and in most of the cases requires confirmation by computed tomography pulmonary angiogram (CTPA). This could be critical to decide fibrinolysis indication in hemodynamic unstable patients (pts) with PE suspicion in out-of-hospital setting or if CTPA is not immediately available.

The implementation of artificial intelligence (AI) in medical diagnosis has attracted major attention last years. Electrocardiography (ECG) signals and patterns can be detected by AI networks with precision. The purpose of this study was to develop an AI model for predicting PE using 12-lead ECG.

Methods

We extracted 1014 ECGs of pts admitted to emergency department who underwent CTPA due to PE suspicion: 911 ECGs were used for development of the AI model (derivation cohort) and 103 ECGs were used for testing the PE prediction model (validation cohort). An AI algorithm based on an ensemble neural network was developed using 12-lead ECG signal.

The first endpoint was the diagnosis of PE. To evaluate the performance of the AI model, we compare the performance of AI model against the recommend clinical prediction rules for PE based on clinical probability and D-dimer measurement (Wells and Geneva criteria combined with fixed and age-adjusted D-dimer cut-off, YEARS and PEGeD algorithms).

Results

On validation cohort, AI model achieves greater specificity to detect PE than the commonly used clinical prediction rules (p<0.001) -Table 1. The AI model shown a specificity of 100%, which is particularly relevant in the context of fibrinolysis decision. Although the sensitivity of the AI model is lower (p=0.001), the biggest gain of this model is to provide security to the physician to stablish a definite diagnosis. Globally, the AI model performed significantly better than all the other models (AUC 0.75, p<0.001), which had nearly no discriminative power.

The incidence of typical PE ECG features (S1Q3T3, right bundle branch block and V1-V3 T wave inversion) was similar in pts with and without PE, meaning that AI model provide information beyond these findings and can improve PE prediction.

ECGs were included regardless of cardiac rhythm (including pacing), so these findings can be generalized to all pts. Model performance is similar across gender.

Conclusion

In this study we developed and validated a deep learning-based AI algorithm for PE detection using a 12-lead ECG which demonstrated high performance.
Detection of left ventricular contractile dysfunction in patients with premature ventricular contractions using electrocardiogram trained using a deep learning model
Authors: Kowlgi G , Dai Q , Greason G , Woelber T , Wight J , Noseworthy P A , Friedman P A , Asirvatham S J , Attia I Z
Published: November 2023

Background

Premature ventricular contractions (PVCs) are the most common arrhythmia affecting the ventricle. Frequent PVCs are associated with the development of PVC-cardiomyopathy (CM). It is known that the PVC burden has a direct correlation to the severity of left ventricular (LV) dysfunction. However, only 5% of patients with frequent PVCs (>10,000 per day) have associated PVC-CM. Thus, selecting patients for therapy options with drugs or catheter ablation is challenging, especially, when patients are asymptomatic with the PVCs.

Purpose

To train a deep learning model to predict the probability of low LV ejection fraction (LVEF) in patients with PVCs on a 12-lead electrocardiogram (ECG).

Methods

Using 69,440 paired 12-lead ECGs (containing at least one PVC), and echocardiogram data, including the left ventricular ejection fraction at Mayo Clinic, we trained a convolutional neural network to identify patients with ventricular dysfunction, defined as ejection fraction ≤35%, using the ECG data alone. ECGs were acquired at a sampling rate of 500 Hz using a Marquette ECG machine (GE Healthcare) and stored using the MUSE data management system for later retrieval. Comprehensive 2-D or 3-D and Doppler echocardiography were available for all patients. We trained a convolutional neural network using the Keras framework with TensorFlow (Google). The ECG-echocardiogram pairs were divided into a 60:20:20 training:validation:test split.

Results

The mean age of the patients used to train the model was 69.9 ± 13.0 years, and 65.2 percent of patients were male. The mean LVEF for this population was 52 ± 14.9 %, and the proportion of patients with low LVEF was 16.8 percent. The model yielded values for area under the curve, sensitivity, specificity, and accuracy of 0.897, 0.830, 0.813, and 0.816, respectively (Figure 1). A representative example of model prediction of normal LVEF and low LVEF is shown in Figure 2.

Conclusion

Using large-scale data, we have trained a highly accurate convolutional neural network with excellent predictive accuracy for LV systolic dysfunction using the surface ECG. Such a tool would be invaluable for cardiologists when risk-stratifying patients with frequent PVCs, especially those that are asymptomatic. Patients at risk will benefit from early referral to electrophysiologists for drug therapy and catheter ablation to mitigate the risk of PVC-CM.Figure 1Figure 2
Disease trajectories following myocardial infarction: insights from process mining of 145 million hospitalisation episodes
Authors: Hayward C , Batty J , Westhead D R , Johnson O , Gale C P , Wu J , Hall M
Published: November 2023

The number of survivors of MI has increased dramatically in recent decades, yet knowledge of post-myocardial infarction (MI) disease risk to date is limited. We investigated temporally ordered sequences of all conditions following MI in nationwide electronic health record data through the novel application of process mining. Here, we present, for the first time, the observed sequences of diseases that individuals accrue post MI.

We conducted a national retrospective cohort study of all hospitalisations (145,670,448 episodes, 34,083,204 individuals) admitted to NHS hospitals in England (1st January 2008-31st January 2017, final follow up 27th March 2017). Through process mining we identified trajectories of all major disease diagnoses following MI and compared their adjusted relative risk (RR) and all-cause mortality hazard ratios (HR) to a risk-set matched non-MI control cohort using flexible parametric survival models.

Among a total of 375,669 (1·1%) MI patients (872,450 episodes) and 1,878,345 (5·5%) matched non-MI patients (4,712,654 episodes), we identified 28,740 unique disease trajectories. The accrual of multiple circulatory diagnoses was more common amongst MI (RR 4·37, 95% CI 4·00-4·77) and conferred an increased risk of death (HR 1·32, 1·14-1·54) compared with matched controls.

Further, trajectories featuring neuro-psychiatric diagnoses (including anxiety and depression) in conjunction with circulatory disorders, were markedly more common and had increased mortality post MI (HR ranging from 1·12 to 1·74) compared with non-MI individuals.

Pathways involving both circulatory and digestive diseases appeared more frequently in the MI cohort versus matched controls (RR 4·03 [3·87-4·20] to 4·77 [4·28-5·32]). A number of trajectories including disorders of the eye/adnexa were also associated with an increased risk of death compared with matched controls (HR: 1·32, 1·11-1·57), with those eye/adnexa disorders consisting mainly of cataracts (77·6%).

This study quantifies the relative risk and mortality burden associated with disease trajectories following MI from a nationwide hospitalisation cohort for the first time. These data show the significant ongoing cardiovascular disease burden following MI, and crucially highlight the excess accrual of neuro-psychiatric diagnoses conferring increased mortality compared with non-MI individuals. These data provide a clear opportunity for early intervention targets, such as increased focus on the psychological and behavioural pathways for survivors of MI, to mitigate ongoing adverse disease trajectories, multimorbidity and premature mortality. Further understanding of the factors contributing to these trajectories is required to facilitate identification of patients in the early post-MI setting who would benefit most from closer follow-up and prompt intervention.Trajectories: primary diseasesTrajectories: primary+secondary diseases
Simulating atrial fibrillation patterns and screening strategies using virtual patient-level models
Authors: Cai M , Barrios-Espinosa C , Rienstra M , Crijns H , Schotten U , Heijman J
Published: November 2023

Introduction

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality. However, early detection and treatment of AF are hampered by highly variable occurrence and duration of AF episodes, heterogenous patterns of AF progression, and poor symptom-rhythm correlation present in many patients.

Purpose

To develop a computational patient-level model for simulating individual AF episodes, AF progression, and associated clinical outcomes, and to compare the effectiveness of systematic or symptom-based 1-minute electrocardiography (ECG) or 14-day Holter screening using this model.

Methods

We developed a Markov model with 7 different rhythm and/or clinical states (sinus rhythm (SR), asymptomatic and symptomatic AF, each with or without previous stroke, and death) and age/sex-dependent transition probabilities estimated based on clinical data (Figure 1A). Additionally, transitions between SR and AF states were modulated by AF-related remodeling, which itself was controlled by the time in AF and age.

Results

The virtual patient-level model enabled the simulation of changes in rhythm status and clinical outcomes during an entire lifetime with minute-level resolution (Figure 1B). Age/sex-specific AF prevalence / incidence, the proportion of symptomatic episodes and mortality in 4000 virtual patients were in good agreement with clinical data (Figure 1C). We employed the perfect knowledge about rhythm status at any moment in time provided by the model to evaluate systematic and symptom-based 1-minute ECG and 14-day Holter screening for AF in this population. In total, 2372 virtual patients >65 years developed AF. Systematic yearly 1-minute ECG recordings only identified 58 (2.4%) patients. In contrast, 14-day Holter-monitoring detected 18% of AF patients over 65 with a median of 532 days earlier than their clinical AF diagnosis, and identified 11.8% of AF patients who were either completely asymptomatic or only had symptomatic episodes <3 hours. Short-term (2-week) screening after a first symptom had a very low yield, which increased with increasing screening frequency and duration of follow-up (Figure 2). The majority of the remaining AF patients could be diagnosed when their symptoms lasted ≥3 hours, providing time for a traditional clinical diagnosis.

Conclusion

Our novel patient-level AF model can simulate individual AF patterns and clinical outcomes such as stroke and death in large cohorts of virtual patients. The model highlights the variability of AF episodes that makes effective early detection of asymptomatic AF with short-term monitoring challenging, and provides a tool for future studies into AF progression, screening and prevention of AF-related stroke and mortality.Figure 1Figure 2
Relationship between pre-procedural non-ischemic ST-segment depression and the clinical outcomes after catheter ablation in persistent atrial fibrillation patients
Authors: Yano M , Egami Y , Kawanami S , Sugae H , Ukita K , Kawamura A , Nakamura H , Yasumoto K , Tsuda M , Okamoto N , Matsunaga-Lee Y , Nishino M , Tanouchi J
Published: November 2023

Background

ST-segment depression suggests the presence of coronary artery disease (CAD) during sinus rhythm, but the clinical significance, including the outcomes after catheter ablation (CA), in atrial fibrillation (AF) patients remains unknown.

Purpose

The aim of this study was to investigate the relationship between non-ischemic ST-segment depression during AF rhythm before CA and late arrhythmia recurrence in persistent AF (PerAF) patients.

Methods

The present study included PerAF patients from the Osaka Rosai Atrial Fibrillation ablation (ORAF) registry who underwent an initial ablation and had no history of CAD. We assigned the patients based on the presence of ST-segment depression before CA and evaluated the impact of relevant factors on ST-segment depression and the relationship between ST-segment depression, including leads locations (anterior leads, inferior leads, and lateral leads) or depression type (upsloping, horizontal and downsloping) or the degree of ST-segment depression and late recurrence of AF (LRAF).

Results

This study population included a total of 551 patients of which 189 had ST-segment depression. The median follow-up duration was 397 days and LRAF occurred in 195 patients. By multiple regression analysis, diabetes mellitus, hemoglobin, brain natriuretic peptide, left ventricular ejection fraction and left atrial diameter were significant determinants of ST-segment depression before CA. Kaplan-Meier analysis demonstrated that the patients with ST-segment depression had a significantly greater risk of LRAF than those without (p<0.001). Multivariate Cox proportional hazards analysis showed ST-segment depression was independently and significantly associated with a higher risk of LRAF (p<0.001). The patients with ST-segment depression≥0.15mV had a significantly higher risk of LRAF than those with ST-segment depression≥0.15mV (p<0.001). No significant differences among the ST-segment depression leads locations and ST-segment depression type were observed.

Conclusion

Non-ischemic ST-segment depression during AF rhythm was significantly associated with LRAF post CA in PerAF patients.
Effect of telemedicine on health-related quality of life in patients with heart failure: insights from the iCOR randomised controlled trial
Authors: Pons Riverola A , Yun S , Jose N , Calero E , Ruiz M , Alcoberro L , Jimenez S , Moliner P , Ramos R , Ras M , Verdu J M , Garay A , Corbella M , Enjuanes C , Comin-Colet J
Published: November 2023

Background/introduction

Heart failure (HF) can negatively affect health-related quality of life (HRQoL) in HF patients by reducing their independence and limiting their daily activities. Patient-reported outcome measures (PROMs), such as HRQoL, are useful indicators to health status and have good correlation with prognosis. In the past years the addition of telemedicine (TM) to HF programmes has emerged as a promising strategy for the management of chronic HF patients. However, there is little information about the impact of TM on HRQoL.

Purpose

To determine the effect of a TM-based intervention on HRQoL of high-risk chronic HF patients compared to usual care (UC).

Methods

We performed a sub-analysis of the "Insuficiència Cardíaca Optimització Remota" (iCOR) study focusing on HRQoL. iCOR was a single centre, randomised, controlled trial, designed to evaluate a TM intervention added to an existing nurse-based, hospital/primary care multidisciplinary, integrated programme for high-risk chronic HF patients. Participants were randomised to TM or UC after hospital discharge ("vulnerable-phase") and were followed for 6 months. The primary study endpoint for the current analysis was the delta change of HRQoL after 6 months of follow-up, assessed using Minnesota Living with Heart failure Questionnaire (MLHFQ). Impaired HRQoL was considered when patient scored > 55 points. To assess the change in HRQoL between the 2 groups, a repeated-measures ANOVA analysis was performed, adjusted for baseline HRQoL.

Results

178 patients with HF were analysed (81 TM vs 97 UC). The median age of the patients was 74±11 years, 41% were female and 57% had preserved ejection fraction.

As shown in figure 1, HRQoL was poor at baseline (median score of 55±20 points in TM vs 59±19 points in UC) and was not different between both groups (p-value > 0.05). Although both groups showed an improvement in HRQoL after 6 months of follow-up (p-value 0.001 for time effect), HRQoL improved significantly more in the TM-based intervention group (from 55±20 to 14±15, mean Delta change= -37.9±26.4) than in UC group (from 59±19 to 23±21, mean delta change = -29.7±29.1, p-value 0.008) (Figure 1). We designed several multivariate linear regression models exploring the determinants of HRQoL at baseline and the determinants of change in HRQoL over time. As shown in Table 1, HRQoL at baseline, cognitive function (MMSE [mini-mental state examination] and Pfeiffer), affective status and randomisation arm were the only independent predictors of improvement of HRQoL over time.

Conclusions

The addition of TM to a standard HF programme is effective in improving HRQoL in high risk patients with chronic HF regardless cognitive function, affective status or frailty.HRQoL status changeDeterminants of HRQoL
Digital health in the EP lab: Initial experience using remote access and technical support for complex ablation procedures
Authors: Feher M , Heeger C , Kirstein B , Phan L , Hatahet S , Eitel C , Schmidtke C , Kuck K H , Vogler J , Tilz R
Published: November 2023

Aims/Objectives

Three-D electroanatomical mapping (EAM) systems are a prerequisite for treatment of complex arrhythmias. Until date, the EAM system is operated by qualified staff or a field technical engineer (FTE) from the control room.

Novel technology allows for remote access (RA) of the EAM from anywhere in the world. RA increases the flexibility of the EP lab, reduces travel time for the FTE and overcomes hospital access limitations e. g. during the COVID-19 pandemic.

We report on the safety and feasibility of remote operation of the EAM for complex ablation procedures.

Methods

RA mapping was achieved by combining the EAM software with an integrated audiovisual solution for remote support. Communication between the operator and the remote support was achieved using internet-based headphones in the RA group. The 3D EAM system was controlled exclusively from home office. In the conventional group (CG), EAM was exclusively controlled from the control room in the EP lab. Ablation procedures were performed as per institutional standard.

Results

We investigated 50 RA assisted consecutive electrophysiological procedures from 09/2022 to 02/2023 in our University Heart Center. We compared a group of patients undergoing ablation procedures with RA assistance (28/50 (56%) patients were male, median age: 66.5 yrs [IQR 56, 76], BMI: 25.2 kg/m2 [IQR 22, 30]) with a control group (31/50 (62%) male, median age: 66.5 yrs [IQR 54, 78], BMI 28.2 kg/m2 [IQR 24, 30], p > 0.05 for all). Ablation procedures were matched in both groups: 20 pulmonary vein isolations (3 paroxysmal, 17 persistent atrial fibrillation ), 4 cavotricuspidal isthmus ablations, 13 ablations of atrial tachycardia, 1 premature ventricular complex ablation, 3 of ventricular tachycardia, 2 AV reentry tachycardia and 7 AV-node reentry tachycardia cases. In the RA group, the 3D EAM system was controlled exclusively from home office in all patients. In one patient, the internet based headphone connection was instable. Therefore, the communication was established via a conventional smartphone. No other technical issue was noted. No switch to in house support was required. There were no major complications observed.

Procedural data (total procedure time median 100 min [IQR 76, 120] vs. control group 86 min [IQR 60, 110], total fluoroscopy time (median 9.1 min [IQR 6, 13] vs. control group 8.7 min [IQR 5, 12], p > 0.05) were comparable to previous procedures without RA.

Due to RA travel time could be reduced by 139 hr in total in comparison to the control group.

Conclusions

RA for EAM of any arrhythmia is feasible and safe in this single center study. Procedural data were comparable to previous procedures. In the future, this new solution might have a great impact on facilitating EP procedures.Table procedural data
Performance of a multisensor implantable defibrillator algorithm for HF monitoring in the presence of atrial fibrillation
Authors: Boriani G , Bertini M , Manzo M , Calo L , Santini L , Savarese G , Dello Russo A , Santobuono V E , Lavalle C , Viscusi M , Amellone C , Calvanese R , Santoro A , Imberti J , D'onofrio A
Published: November 2023

Background

Some implantable cardioverter defibrillators (ICD) are equipped with diagnostic algorithms that combine data from multiple sensors. The HeartLogic Index was shown to accurately stratify patients at risk of heart failure (HF) events. However, there are no data on its performance in the presence of atrial fibrillation (AF). We evaluated and compared the performance of the algorithm during sinus rhythm and during long-lasting AF episodes.

Methods

The feature was activated in 568 ICD patients from 26 centers. We analyzed stored data to identify periods of ≥30 consecutive days with atrial high-rate episode (AHRE) burden <1hour/day, and periods with AHRE burden ≥20hours/day. Then, we identified patients who met both conditions during their follow-up (AHRE group, N=53), to allow pairwise comparison of periods. For control purposes, we identified patients with AHRE burden <1h during the entire follow-up and we implemented a 2:1 propensity score matching versus the AHRE group (Matched non-AHRE group, N=106).

Results

In the AHRE group, the rate of HeartLogic alerts was 1.2 (95%CI:1.0-1.5) /patient-year during periods with AHRE burden <1hour/day and 2.0 (95%CI:1.5-2.6) /patient-year during AHRE burden ≥20hours/day periods (p=0.004). Significantly higher values of the combined index were recorded during periods with AHRE burden ≥20 hours/day (in Figure the average combined index – Day 0: alert day). The rate of HF hospitalizations was 0.34 (95%CI:0.15-0.69) /patient-year during IN-alert periods and 0.06 (95%CI:0.02-0.14) /patient-year during OUT-of-alert periods (p<0.001). The IN/OUT-of-alert state incidence rate ratio of HF hospitalizations was 8.59 (95%CI:1.67-55.31) during periods with AHRE burden <1hour/day and 2.70 (95%CI:1.01-28.33) during AHRE burden ≥20hours/day periods. In the Matched non-AHRE group, the rate of HF hospitalizations was 0.29 (95%CI:0.12-0.60) /patient-year during IN-alert periods, and 0.04 (95%CI:0.02-0.08) /patient-year during OUT-of-alert periods (p<0.001). The incidence rate ratio was 7.11 (95% CI: 2.19-22.44).

Conclusions

Patients seemed more exposed to alerts during periods of AF. The ability of the algorithm to identify increased risk of HF events was confirmed during AF, despite a lower IN/OUT-of-alert incidence rate ratio when compared to non-AF periods and non-AF patients.