bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin
Authors: Manninger M , Lercher I , Hermans A N L , Isaksen J L , Prassl A J , Zirlik A , Vernooy K , Chaldoupi S M , Luermans J , Ter Bekke R M A , Kanters J K , Plank G , Scherr D , Pock T , Linz D 
Published: May 2024

Abstract

Aims
To test, whether a neural network-based classifier could aid in distinguishing photoplethysmographic (PPG) pulse waveforms of ventricular from those of supraventricular origin.

Methods
Thirty patients undergoing invasive electrophysiological studies for documented narrow complex tachycardias between May and December 2021 at a university medical centre were included in the trial. PPG waveforms were recorded using a PPG wristband (Empatica E4) in parallel to 12-lead surface electrograms (ECGs) and intracardiac electrograms from diagnostic catheters. Pulse waves were then annotated to either supraventricular (sinus rhythm, atrial pacing) or ventricular origin (ventricular pacing) based on invasive electrogram recordings, ECGs and stimulation protocols. 21147 data sets were split into test, training and validation data sets and used to develop, train and validate a convolutional neural network (CNN) for classifying PPG waveforms according to their origin into sinus rhythm, atrial or ventricular pacing.

Results
Datasets were complete for 27 patients. 74% were female and median age was 53 (range 18, 78) years, median BMI was 27±5, 19% had arterial hypertension, 11% had diabetes and 15% had coronary artery disease. During the electrophysiological study, typical AV-nodal re-entrant tachycardias could be induced in 63% of patients, 15% had inducible atrial tachycardias and the other patients had no inducible tachyarrhythmias. Our CNN-based classifier was able to correctly predict the origins of pulse waves in 95±1% for sinus rhythm, 96±1% for atrial paced rhythms and 95±1% for ventricular rhythms.

Conclusion
A neural network-based classifier trained on ground truth PPG data obtained by using invasive EP measurements for annotation could accurately predict atrial or ventricular origin from PPG waveforms alone.Graphical abstract
Predictive machine learning model for mechanical dilatation in transvenous lead extraction procedures
Authors: De Lucia R , Micheli A , Parlato A , Podda M , Pedrelli L , Aliprandi F , Parollo M , Segreti L , Di Cori A , Canu A , Grifoni G , Barletta V , Viani S , Zucchelli G 
Published: May 2024

Abstract

Background
Transvenous lead extraction (TLE) remains a procedure that requires a high level of expertise, with a doubled risk of death and clinical failure when performed in low-volume centers compared to high-volume ones (1).

Purpose
The aim of this study was to create a machine learning (ML)-based risk stratification system for predicting the need for mechanical dilatation in patients undergoing TLE due to infection.

Methods
We designed a ML-based risk stratification system trained with data from our registry to predict the need for mechanical dilatation in patients undergoing TLE for infection. An extensive evaluation of 5 different ML models (k-nearest neighbors, support vector machine, decision tree, and decision tree ensembles, such as random forest and gradient boosting machine) was conducted to identify a classifier with the highest potential to correctly predict previously unseen patients.

Results
Data to train the model was extracted from our 25-year registry of patients undergoing TLE (June 1998 – March 2023), for a total of 491 patients (77.8% male; age 69.7 ± 12.8 years) and 938 leads (ICD 21.2%; pacing 78.8%; indwelling time 61 ± 60 months) removed with success in 100% of cases. Each patient was represented by a set of 21 attributes (14 clinical, 7 device-related). Manual traction (MT) was used in 27.5% of cases, and mechanical dilatation (MD) was employed in the remaining 72.5% of cases. 5-fold nested cross validation was used to estimate performances: in turn, 393 patients were used for training and model selection, and 98 patients were used for independent testing. According to the evaluation, Gradient Boosting Machine performed best, achieving test accuracy of 89% (+/- 2% std. dev.), test sensitivity of 95% (+/- 3% std. dev.), test specificity of 73% (+/- 8% std. dev.), test AUROC of 92% (+/- 1% std. dev.). A further interpretability analysis on the best performing decision tree was conducted, showing remarkable adherence between the internal decisions taken by the model to make predictions and the current clinical practice for TLE.

Conclusion
ML models have enhanced the prediction of MD requirements in TLE procedures. This data may support the referral of patients to specialized and high-volume centers, thereby improving preoperative risk assessment.Feature importanceROC Curves
Performance of an online chat-based artificial intelligence interface for patient education on atrial fibrillation ablation
Authors: Han J , Baykaner T , Mittal S 
Published: May 2024

Abstract

Background
Chat-based artificial intelligence (AI) web interfaces that aim to mimic human conversation have increasing utilization in healthcare to help with simple tasks such as scheduling appointments, and even more complex tasks such as providing patient educational responses to COVID-19 questions as done by the World Health Organization.1 Chat-based AI has also been shown to provide accurate responses to cardiovascular disease prevention questions.2 Its ability to provide patient education for more complex treatments like atrial fibrillation (AF) ablation has not been explored.

Purpose
To evaluate the quality of a popular chat-based AI program’s answers to patient questions about AF ablation.

Methods
Twenty commonly asked questions ("prompts") regarding AF ablation were entered into ChatGPT (Chat Generative Pre-trained Transformer), a large language model-based AI program (Fig. 1). Prompts were written in plain language; technical terms were avoided except for "radiofrequency", "cryoablation" and "pulsed field ablation" (PFA). SMOG readability calculator was used to assess responses for difficulty and grade-level, as healthcare organizations recommend ≤ 8th-grade level complexity for patient information. Response content was graded by 3 experienced cardiac electrophysiologists as "reasonable", "missing important elements/some inaccuracies" or "misleading/inappropriate". Responses are presented in mean +/- standard deviation and percentages.

Results
Responses averaged 118±67 words (Fig. 1). Of 20 responses, 17 (85%) were deemed reasonable, 3 (15%) missing important elements/some inaccuracies and none inappropriate or misleading; 16 (80%) emphasized discussion of issues with the healthcare team (Fig. 2). Responses missing important elements/some inaccuracies were those about risks/complications of ablation [missing phrenic nerve palsy, atrioesophageal fistula (AEF), potential need for emergent cardiac surgery or pacemaker, death], concerning symptoms post-procedure (missing symptoms of hematoma, AEF, stroke), and that PFA is not yet approved for use in all regions. Average reading grade level of responses was 13.8 (college level or "professional"): 17 (85%) responses were 12th grade level, 11 (55%) were college-level or higher, and 6 (30%) were college-graduate level ("extremely difficult"). None were ≤ 8th grade level (Fig. 2).

Conclusions
A majority of ChatGPT responses to common patient questions about AF ablation had reasonable content quality that frequently emphasized the importance of discussion with the healthcare team. However, responses to more difficult questions regarding risks, symptoms of potential complications, or newer technology missed important details; more than half of responses required college-level reading skills. While use of Chat-AI for patient education on EP topics appears promising, patients should be advised to use caution. Further AI training to improve content and readability should be explored.
Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation
Authors: Bernardini A , Bindini L , Berteotti M , Antonucci E , Palareti G , Poli D , Frasconi P , Marcucci R 
Published: May 2024

Abstract

Background
Despite the availability of different risk scores, the accuracy of actual prediction tools for outcomes in patients with atrial fibrillation (AF) remains modest [1]. Although Machine Learning (ML) has been used to predict outcomes in the AF population, evidences about outcome prediction in a population entirely on oral anticoagulation is lacking. The aim of this study is to use ML to predict outcomes in anticoagulated patients with atrial fibrillation, processing data from the Italian AF START-2 Register.

Methods and aims
Different ML models were applied to predict all-cause death, cardiovascular (CV) death, major bleeding and stroke in anticoagulated patients with nonvalvular AF. Overall population, Vitamin K Antagonists (VKA) and Direct Oral Anticoagulants (DOACs) populations were considered for the analyses.

Results
11078 AF patients (male n=6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0-2.6]. Patients on VKA were 5135 (46.4%), while 5943 (53.6%) were on DOACs. During the follow-up, 785 patients died, of which 169 (21.6%) due to CV causes; 240 major bleeding events were recorded, and 50 strokes occurred. Using Multi-Gate Mixture of Experts (MmoE), a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respectively, for the prediction of all-cause death and CV death in the overall population. The best ML model outperformed CHA2DSVA2SC and HAS-BLED for all-cause death prediction (p&lt;0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients compared to HAS-BLED score (0.711 vs. 0.586, p&lt;0.001). Body mass index, age, glomerular filtration rate, platelet count and hemoglobin levels resulted the most important variables for ML prediction.

Conclusions
In patients with AF, ML models showed good discriminative ability to predict all-cause death, regardless of the type of anticoagulation strategy, and major bleeding on DOAC therapy, outperforming CHA2DS2VASC and the HAS-BLED scores for risk prediction in these populations. Anemia, platelet count, and BMI emerged as new potential risk predictors in anticoagulated AF patients. The applications of ML prediction models in clinical practice could improve the risk assessment and subsequent management of anticoagulated patients with AF.Graphical AbstractFeature importance for Bleeding in DOACs
Comparison of QT interval readings between smartwatch ECG combined with artificial intelligence and 12-lead ECG in subjects hospitalized for antiarrhythmic drug initiation and follow-up
Authors: Lefebvre B , Lehmann H I , Tordjman D , Henry C , Singh J P 
Published: May 2024

Abstract

Background/Introduction
Class III antiarrhythmic drugs (AADs) are effective for the maintenance of sinus rhythm (SR) in subjects with atrial fibrillation (AF). However, several require initial hospitalization for QT interval monitoring using the 12-lead ECG.

Purpose
The purpose of this study was to evaluate the feasibility of using a smartwatch single lead ECG combined with a deep learning algorithm for routine QT interval determination in the context of AAD initiation.

Methods
A deep learning model was developed using smartwatch and Holter ECG recordings to estimate corrected QT intervals. To evaluate the performance of this model, we carried out a prospective study on subjects hospitalized for initiation of sotalol and/or dofetilide. Subjects recorded near simultaneous (maximal 2 minutes apart) smartwatch ECGs (Apple Watch Series 6) and 12-lead ECG (Mac 5500 HD ECG or GE Carescape B450) right before and 2-3 hours after drug administration twice a day for 2-3 days. Blinded double annotation followed by adjudication of disagreement provided a reference heart rhythm (NSR or AF) and QTc interval. The tangent method was used for QTc interval determination. Bland-Altman analysis was performed to assess the agreement between QTc measures.

Results
Fifty subjects (mean age 67.4±10.9 years, 64% male) were enrolled in the study from February 2022 to October 2022. Out of 386 monitoring visits, 321 near simultaneous pairs of ECGs were collected with a mean delay of +15.0±41.1s for the smartwatch ECG compared to the 12-lead ECG. Overall, a mean QTcF reference value of 419.9±34.0 ms was observed (435.2±38.4 ms for QTcB, r=0.83 between annotators[BL2] ). Among the 139 pairs with 12-lead ECG found in NSR, we observed a mean QTcF difference of +19.7 ms and limits of agreements equal to -21.5 ms and 60.8 ms, with AI-facilitated measurement. In comparison, a mean QTcF difference of +22.5 ms and limits of agreements equal to -23.8 ms and 68.8 ms were observed for manual readings of smartwatch ECGs instead.
Conclusion(s): Our findings suggest that a smartwatch ECG deep learning approach can estimate the QT interval with a fair agreement compared to standard 12-lead monitoring during AAD initiation. This measurement method appears to be at least as accurate as manual measurements on smartwatch ECGs. With further validation, the smartwatch-based automatic detection of QT interval prolongation, could be used for a faster, safe, and extended outpatient QT interval monitoring.Bland-Altman plots
Remote magnetic navigation for catheter ablation of complex arrhythmias in congenital heart disease: an initial experience using a transnational telerobotic approach
Authors: Griffiths J , Shi R , Ailoaei S , Wright P , Hunnybun D , O'connor M , Kempny A , Vlachou C , Pickworth T , Keogh B , Wong T 
Published: May 2024

Abstract

Background
The field of congenital electrophysiology (EP) is still in its relative infacncy with a limited number of electrophysiologists capable of performing catheter ablation in patients with congenital heart disease (CHD). As a result, patients or physicians are often required to travel great distances to performed specialised ablation procedures, something made near impossible during the COVID-19 pandemic.

Objective
We aimed to explore the feasibility and safety of a telerobotic approach to catheter ablation where the remote operator was situated approximately 1200km away from the patient and local team.

Methods
A 3D-printed 'phantom' heart was used in a pilot experiment in which no significant difference was seen between the local and remote operators in electroanatomic (EAM) mapping of the phantom heart using remote magnetic navigation (RMN). Catheter ablation was subsequently performed in patients with severe CHD using a transnational telerobotic approach to catheter ablation where the remote operator was able to view the dedicated console with live fluoroscopy, EAM and EP signals, as well as control the mapping and ablation catheters from 1200km away. As a safety precaution, radiofrequency (RF) could only be delivered by the local team.

Results
In total, 10 procedures were performed in 9 patients with severe CHD (mean age 38) between April and December 2021. All patients underwent an atrial ablation and one additional non-CHD patient underwent denervation of the pulmonary arteries. Median procedure duration was 335 (minutes) (IWR 230-349) with 11.5 minutes (QRS 3-15.4) of fluoroscopy exposure and 752 (IQR 674-905) seconds of RF delivery. There were no acute procedural complications. At one-year follow-up, one patient died as a result of heart failure and there were two arrhythmia recurrences.

Conclusions
Telerobotic catheter ablation is feasible and effective and creates the possibility of exciting new approaches to remote ablation and its use in the tele-proctoring of international fellows.
From ECG to broadband ECG, focused on the ventricular activation
Authors: Jurak P , Viscor I , Halamek J , Sokup L , Plesinger F , Smisek R , Vondra V , Panovsky R , Matejkova M , Nguyen U C , Prinzen F W , Curila K , Leinveber P 
Published: May 2024

Abstract

Background
An ultra-high-frequency ECG (UHF-ECG) technique, recently introduced, provides ventricular activation patterns and identifies electrical dyssynchrony. UHF-ECG interpretation assumes that the ECG amplitude decreases rapidly at higher frequencies with increasing distance from the source. Based on this property, nearby sources can be distinguished. Unfortunately, this assumption has never been experimentally proven.

Purpose
The study aims to show whether there is a decrease in ECG amplitude with distance from the source and whether this decrease is frequency-dependent.

Methods
UHF-ECG resting recordings were acquired in 106 subjects. We used a standard electrode setup for a 12-lead ECG. In addition, we recorded UHF-ECG from six extended precordial electrodes. With the help of magnetic resonance images, we measured the distance of all precordial leads to the ventricular geometrical center. For each lead, we determined the amplitude of frequency components by computing the area of the averaged amplitude envelopes in the region of the QRS complex. We analyzed five frequency bands: LF (0.2-20 Hz), MF (20-80 Hz), HF (80-300 Hz), UHF1 (300-500 Hz), and UHF2 (800-1000 Hz).

Results
The left panel in the figure shows the relative amplitude decrease in five frequency bands with relative distance from the depolarization source. The decay coefficient in the LF band is only 0.56 [0.84, 0.29] median [percentile 25,75] and gradually grows up to 1.23 [1.40, 0.98] in the UHF2 band, p&lt;0.005. It indicates that the amplitude of the ECG signal from distant areas of the ventricles in the LF band drops approximately 50 percent of the original value, and the amplitude in the UHF2 band drops to only 20 percent of the original value.
The right panel demonstrates an amplitude decrease with distance and frequency in the background of heart geometry. The circle's area corresponds to the relative signal intensity recorded in a particular body surface electrode V1, V4, and V6 from RV, LV, and septal ventricular segments. The circle marked in red has a size of 1 and is at the smallest distance from the electrode. The right panel includes the LF band (regular ECG) and the UHF2 band of 800-1000 Hz.

Conclusion
The results show significant differences in ECG signal decay between frequency bands and the ability to localize nearby sources using UHF-ECG. Verifying the principle of frequency dependence of ECG properties opens possibilities for a more accurate description of activation patterns. Combining multiple frequency bands (broadband ECG) has the potential to distinguish the activation of near and distant sources.Different decrease in ECG amplitude
Effect of atrial extrasystoles in electrophysiological properties in heart failure patients
Authors: Pereira Ferraz A , Pool L , Van Schie M , Ms De Groot N 
Published: May 2024

Abstract

Background
Patients with heart failure (HF) most likely have an extensive arrhythmogenic substrate which increases vulnerability to atrial fibrillation (AF). Conduction disorders underlying this increased AF vulnerability -hidden during sinus rhythm- may be unmasked by spontaneous atrial extrasystole (AES).

Objective
The aim of this study is to investigate the impact of AES on potential arrhythmogenic electrophysiological properties in patients with HF and AES.

Methods
Intraoperative epicardial mapping of the right and left atrium (RA, LA), Bachmann’s bundle (BB) and pulmonary vein area (PVA) was performed in 132 patients (10 with HF and 122 without HF). Differences in unipolar potential morphologies and conduction disorders during SR and spontaneous AES were determined.

Results
A median of 1 (1–3) AES per patient were included. In patients with HF, AES were associated with lower median potential voltages (1.94mV versus 4.02mV, P&lt;0.001), more low voltage areas (10.98% versus 4.97%, P=0.004) and a higher number of short double potentials (28.5% versus 13.0% of short double potentials, P&lt;0.001) compared to patients without HF. Comparing differences (AES - SR) in parameters between patients with and without HF, there was a larger increase in long double potentials (+5.32% versus +0.31%, P&lt;0.001), fractionated potential (+4.18% versus +0.49%, P=0.001), conduction block (+3.39% versus +0.63%, P=0.002) and continuous conduction delay and block (+6.72% versus +1.74%, P=0.001) in HF patients.

Conclusions
In HF patients, AES cause lower unipolar potentials voltages and more conduction disorders than in patients without HF. Differences in arrhythmogenic properties between sinus rhythm and atrial extrasystoles are more pronounced in HF patients. These results suggest that HF patients have hidden arrhythmogenic substrates that can be uncovered by AES and may explain the increased AF vulnerability.
Increased circulating BMP10 levels are associated with late postoperative atrial fibrillation after cardiac surgery and left atrial endomysial fibrosis
Authors: Winters J , Kawczynski M J , Gilbers M D , Isaacs A , Zeemering S , Maesen B , Verheule S , Schotten U 
Published: May 2024

Abstract

Background
Serum bone morphogenetic protein 10 (BMP10) levels are a marker for history of atrial fibrillation (AF) and for major adverse cardiovascular events in AF-patients, including stroke, recurrence after catheter ablation, and mortality. The predictive value of BMP10 in patients undergoing cardiac surgery and association with morphological properties of atrial tissue are unknown.

Objective
To study the correlation between BMP10 levels and pre-operative clinical traits, occurrence of early and late postoperative atrial fibrillation (POAF), and atrial fibrosis in patients undergoing cardiac surgery.

Methods
Patients with and without preoperative AF history undergoing first cardiac surgery were included (RACE V, n=147). Pre-operative blood biomarkers were analyzed, left (n=114) and right (n=125) atrial appendage biopsies were histologically assessed following WGA-staining, and post-operative rhythm was monitored continuously with implantable loop recorders (n=134, 2.5yrs).

Results
Multiple logistic regression indicated that BMP10 accurately predicts a history of persistent AF (OR=1.24, 95%CI: 1.10-1.40, p=4.32e-4), similar to NT-pro-BNP. BMP10 levels were associated with increased late-POAF90 occurrence after adjusting for age, sex, AF history, and early-POAF occurrence (HR=1.07 [per 0.1ng/ml increase], 95%CI: 1.00-1.14, p=0.04). Left atrial endomysial fibrosis (St.β=0.22, p=0.04), but not overall fibrosis (St. β= , p=), was associated with circulating BMP10, after adjusting for age, sex, AF history, reduced LVF, and valvular surgery indication.

Conclusion
Increased BMP10 levels were associated with persistent AF-history, increased late-POAF incidence, and LAA endomysial fibrosis in a diverse population of patients undergoing cardiac surgery.