bluuebunny/crossref_metadata_2025_split huggingface dataset:
Usability of novel beat-by-beat ECG annotation software portal for generating large-scale machine learning training datasets
Authors: Navara R , Mchugh W , Pallod A , Raj K , Sinha A , Deshpande M , Wang Y , De S , Deleon R , Bolaji O , Asfaha D , Okoroanyanwu S , Paudel B , Manohar R , Patel K 
Published: May 2024

Abstract

Introduction
Despite the availability of numerous large publicly available ECG datasets, there is a distinct absence of specialized software to enable the beat-by-beat annotation and labeling, limiting the ability to generate high-quality machine learning training datasets. This study analyzed the usability of such a novel software interface. Through a survey of several cardiologists and clinic staff, several metrics were gauged, including user satisfaction, system and app simplicity, user interface, and effectiveness. The survey results gleaned insight into the users’ experiences, providing an understanding of the software’s user-friendliness and potential in developing a large-scale machine learning annotated ECG training dataset to advance cardiac health research.

Methods
3 cardiology physicians, and 9 cardiac telemetry unit staff were surveyed following use of the SafeBeat Annotation Software Platform to annotate a large publicly available dataset ECGs (n=10,646) beat-by-beat. The validated Post-Study System Usability Questionnaire (PSSUQ) and mobile Health App Usability Questionnaire (MAUQ) were used to assess the overall usability of the software interface. Results are reported as mean ± standard deviation.

Results
Users completed 10,646 ECG beat-by-beat annotations in 62 days, and averaged 164.0 ECGs per day. Physicians rated the software interface highly, with an overall mean PSSUQ of 6.4 ± 0.8 and MAUQ of 6.4 ± 0.7. Staff also rated the software interface highly, with an overall mean PSSUQ of 6.7 ± 0.6 and MAUQ of 6.8 ± 0.5. Overall 92% of the users reported that they strongly agreed they would use the application again and 83% strongly agreed that it was easy to learn how to use the system.

Conclusion
The usability analysis of this novel beat-by-beat ECG annotation software revealed that it was a highly effective tool for both cardiologists and clinic staff. Both the system and app were reported to be simple to learn, comfortable to use, and easily navigable. With most users responding that they could become productive quickly using this system, there is clear potential for it to be used to develop a large-scale machine learning dataset.Figure 1
Quantitative and qualitative assessment of left atrial cardiomyopathy as well as prediction of atrial fibrillation recurrences upon PVI using machine learning
Authors: Bock M , Tanacli R , Alogna A , Fassina L , Dagres N , Hindricks G , Gerds-Li J H , Hohendanner F 
Published: May 2024

Abstract

Introduction
Atrial fibrillation (AF) is one of the most common heart pathologies in daily clinical practice and confers significant mortality and morbidity. However, AF ablation often proves ineffective - even after successful pulmonary vein isolation (PVI). The prediction of PVI failure for the long-term treatment of AF as well as the improved assessment of the underlying substrate appears pivotal. We hypothesize, that ML can be applied to better characterize the substrate and recurrence in AF patients upon PVI.

Methods & Results
We retrospectively evaluated data from 205 patients that underwent AF ablation between 2019 and 2023, including high-density 3D electroanatomic maps (sinus rhythm), LA rotational angiography, invasive LA hemodynamic measurements (after transseptal passage), echocardiography, 12-lead ECG and laboratory testing. A neuronal net was trained on selected features extracted from those datasets using supervised learning and employed to predict measures of atrial cardiomyopathy and AF recurrence.
Patients were 66±1 years old and received first or second pulmonary vein isolation (PVI) for paroxysmal atrial fibrillation with 18% documented recurrences upon a mean follow-up of 23±1months. First, we extensively refined or developed tools for automated P-wave analysis from 12-lead ECGs as well as local LA voltage and volume quantification. Next, LA hemodynamics and volumes were assessed: Mean LA volume as derived from rotational angiography significantly correlated with LA volume index as obtained using echocardiography. Interestingly, LA size showed no correlation with NT-proBNP, LA low voltage or LA hemodynamics. In addition, global (25±2% of total LA area) and local (i.e. anterior, posterior, roof) LA voltage was determined and shown to be an independent predictor of AF recurrence. In our first unselected subset of 136 patients with 2nd PVI for atrial fibrillation, our ML models predicted LA fibrosis (i.e. low voltage regions; Utah stage) from 12-lead ECGs with an accuracy of 90%. Accuracy was 87% for the prediction of AF recurrences from LA pressure and low-voltage regions. In addition, the algorithm predicted AF recurrences from 12-lead SR ECGs obtained before PVI with an accuracy of 91%.

Conclusion
Artificial neural networks allow to quantitatively and qualitatively predict measures of atrial cardiomyopathy and atrial fibrillation recurrence probability upon complete PVI with high accuracy.Figures
Cost-effectiveness of atrial fibrillation detection with insertable cardiac monitors in large-artery or small-vessel disease stroke in the United Kingdom
Authors: Witte K K , Schwamm L H , Bernstein R A , Reynolds M R , Rose D Z , Lip G Y , Ozturk E , Villinger J , Rosemas S C , Ziegler P D , Yaghi S 
Published: May 2024

Abstract

Background
The randomised STROKE-AF trial demonstrated clinical effectiveness of continuous cardiac monitoring to detect atrial fibrillation (AF) with insertable cardiac monitors (ICMs) for patients with ischemic stroke attributed to large-artery or small-vessel disease (LAD/SVD). ICMs were ten-fold more effective at detecting AF compared to the standard of care (SoC). However, cost-effectiveness of this monitoring strategy is unknown.

Purpose
The aim of this economic evaluation was to assess the cost-effectiveness of long-term continuous cardiac monitoring via ICMs, for detection of AF (and, in event of AF diagnosis, subsequent anticoagulation), in patients with ischemic stroke attributed to large-or-small vessel disease compared to the SoC of conventional follow up in the United Kingdom.

Methods
We constructed a Markov model with a cycle length of three months and a lifetime horizon to conduct the cost-effectiveness analysis, from the National Health Service (NHS) perspective. Costs and health-related benefits were discounted at an annual rate of 3.5%. We expressed outcomes in terms of quality-adjusted life-years (QALYs). Patient characteristics in the analysis and detection probabilities were based on the STROKE-AF trial. Detection of AF resulted in a switch in treatment from antiplatelet to direct oral anticoagulant (DOAC). Risk of cerebrovascular events were dependent on a patient’s AF detection status and subsequent treatment. Scenario, deterministic, and probabilistic sensitivity analyses were conducted to test the robustness of the base-case incremental cost-effectiveness ratio (ICER) and account for uncertainties in the model. We stratified patients into risk groups based on CHA2DS2-VASc scores and risk of AF detection.

Results
The base case incremental cost-effectiveness ratio of the implantation of an ICM was £15,485 per QALY, substantially under the NICE willingness-to-pay threshold of £20,000-£30,000 per QALY. ICM implantations resulted in 0.139 QALYs gained per patient at an incremental cost of £2,147. There were 0.054 fewer stroke events per patient in the ICM arm versus SoC. Results were robust under sensitivity analysis.

Conclusion
ICMs were cost-effective compared to SoC for detection of AF in patients with LAD/SVD stroke in the United Kingdom driven largely by more stroke events avoided. Subgroup analysis showed that ICMs were more cost-effective in patients with higher CHA2DS2-VASc scores and those with a higher individual risk of AF.Markov Model SchematicSummary ICER Results and Events avoided
Association between leukocyte telomere length and heart failure hospitalization based on the presence of the atrial fibrillation
Authors: Lee Y , Yang P S , Jang E S , Kim D , Yu H T , Kim T H , Uhm J S , Sung J H , Pak H N , Lee M H , Joung B 
Published: May 2024

Abstract
Leukocyte telomere length (LTL) is a well-established biomarker linked to cardiovascular disease, with shorter LTL associated with increased severity of heart failure (HF) outcomes. It is also well known that atrial fibrillation (AF) and HF are closely related and often coexist. This study explores the relationship between LTL and hospitalization due to HF, considering both AF and non-AF patients within the UK Biobank cohort. Retrospective analysis of the UK Biobank dataset (n=451,552, mean age 57.0±8.1 years, 45.8% male) excluded individuals with pre-existing HF (n=12,162) and extreme/no LTL data (n=36,829). LTL was quantified using multiplex quantitative polymerase chain reaction. Among 445,895 participants without AF at enrollment, 2,851 were hospitalized for HF (mean age 63.1±5.8, 63.3% male), while 313 with AF (mean age 64.3±5.3, 68.4% male) experienced HF hospitalization. Shorter LTL correlated significantly with HF hospitalization in both non-AF (826.8±117.7 vs 797.9±115.5, P &lt; 0.001) and AF groups (804.8±116.1 vs 789.2±115.9, P &lt; 0.001). Categorized into quartiles, shorter LTL consistently associated with higher HF hospitalization rates in both non-AF (P for trend &lt; 0.001) and AF groups (P for trend = 0.022). Adjusting for age, sex, and cardiovascular risk factors, longer LTL correlated with reduced HF risk (HR, 0.87; 95% CI, 0.77-0.98; P = 0.017), independent of AF presence (P for interaction=0.629). This study establishes an association between shorter LTL and increased HF hospitalization rates, regardless of AF presence. These findings underscore the significance of LTL as a potential prognostic biomarker for HF, emphasizing its relevance in both AF and non-AF populations
Senolysis ameliorates increased susceptibility to ventricular arrhythmias in prediabetes
Authors: Angendohr S , Zoehner C , Duplessis A , Amin E , Polzin A , Dannenberg L , Schmitt J , Srivastava T , Bejinariu A , Rana O , Spieker M , Kloecker N , Kelm M , Gerdes N , Lang A 
Published: May 2024

Abstract

Background
Obesity and associated prediabetes are major risk factors for cardiovascular disease that promote cellular senescence and predispose to ventricular arrhythmias (VA). In consequence, targeting cellular senescence may represent a feasible therapeutical approach that can decrease VA susceptibility in aged hearts. The aim of the present study was to characterize the impact of senolysis on glucose control, left ventricular function (LVF) and VA susceptibility in a prediabetic mouse model.

Methods
Ten – 12 week-old C57Bl/6J mice were fed a high-fat diet over a period of 12 weeks to generate diet-induced-obesity (DIO). Afterwards, mice were treated with dasatinib (5 mg/kg) and quercetin (50 mg/kg) (DQ) or vehicle (10 % DMSO) by oral gavage three times in 7 days. Cardiac senescence was assessed by measuring Cdkn1a mRNA expression levels. Glucose control was monitored by glucose tolerance testing. LVF was evaluated by echocardiography and myocardial contractility was assessed by sarcomere length measurements in paced single cardiomyocytes. VA susceptibility testing was performed by programmed ventricle stimulation combined with an epicardial fluorescence imaging of the LV anterior wall in isolated hearts. VA susceptibility was quantified by an established scoring system.

Results
Cardiac senescence, as assessed by expression of Cdkn1a mRNA (encoding p21), was increased in DIO mice (P=0.0004, Figure 1A), whereas DQ reduced cardiac senescence (P&lt;0.0001, Figure 1A). Glucose control was impaired in DIO mice (P&lt;0.0001) and could be partially restored by DQ (P=0.003). LVF was ameliorated by DQ in DIO mice (P=0.018). In agreement, contractility in single cardiomyocytes was improved (Amplitude of sarcomere contraction: P=0.0003, figure 1B; speed of sarcomere contraction: P=0.046). VA susceptibility was increased in DIO mice (number of inducible VAs: P=0.001, figure 1C; VA score: P=0.0002; VT inducibility: P=0.001) whereas DQ limited VA susceptibility (number of induced VAs: P=0.004, Figure 1C; VA score: P=0.004; VT inducibility: P=0.025) in isolated hearts.

Conclusions
Our findings demonstrate that senolysis ameliorates glucose tolerance and LVF while reducing VA susceptibility in obesity and prediabetes. In consequence, senolysis may offer a perspective therapeutical approach for obese patients at risk for VAs. Further experiments will address underlying molecular mechanisms to identify potential therapeutical targets.
Rest and stress aortic flow analysis by CMR in MyoFit46
Authors: Varma A , Webber M W , Captur G C 
Published: May 2024

Abstract

Background
Aortic stiffness is a strong predictor of future cardiovascular events and all-cause mortality. Aortic stiffness has never been measured during adenosine stress perfusion before, and may provide valuable relationship about this relationship.

Aim
To investigate if there are differences between aortic stiffness and conditions of rest and stress perfusion, in an older age-homogenous population-based cohort.

Methods
203 participants from the longitudinal 1946 Medical Research Council National Survey of Health and Development British birth cohort were included. Cardiovascular Magnetic Resonance (CMR) imaging was performed at rest and during adenosine stress perfusion. 3 established and validated biomarkers of aortic stiffness were reported - pulse wave velocity, distensibility and beta stiffness, at rest and stress perfusion. All variables were non-normally distributed, therefore the Wilcoxon signed-rank test was used to look for significant differences between rest and stress.

Results
Aortic stiffness increased significantly during adenosine stress perfusion, shown by all biomarkers of aortic stiffness used: pulse wave velocity (p = &lt;0.001), distensibility (p = &lt;0.001), and beta stiffness (p = &lt;0.001). However, the aortas of participants with a higher resting aortic stiffness at baseline were less dynamic (little if any aortic stiffness change with stress) when compared to those with low resting aortic stiffness.

Conclusion
It is feasible to measure aortic stiffness during adenosine stress perfusion. In healthy aortas, aortic stiffness increases in the acute phase of adenosine stress suggesting a new potential mechanism for the body response to adenosine – peripheral chemoreceptor reflex activation. Stiffer arteries at baseline are less modifiable and stress aortic flow CMR has the potential to identify these persons permitting early targeted therapy. Change in aortic stiffness, measured by stress aortic flow CMR is therefore a potential non-invasive biomarker of aortic health.
The impact of comorbidities and substance use on heart failure events and major ventricular arrhythmias in phospholamban p.(Arg14del) positive individuals
Authors: Van Der Heide M Y C , Verstraelen T E , Mahmoud B , Van Drie E , De Brouwer R , Proost V M , Houweling A C , Dickhoff C , Germans T , Te Riele A S J M , Van Spaendonck-Zwarts K Y , Cox G P J , Van Tintelen J P , Zwinderman A H , Wilde A A M 
Published: May 2024

Abstract

Background
Patients with the phospholamban (PLN) p.(Arg14del) variant can develop a severe cardiomyopathy, characterized by progressive heart failure (HF) and major ventricular arrhythmias (VA). However, there is a marked variability in disease expression among affected individuals. There is still a considerable gap of knowledge regarding the influence of comorbidities and substance use on disease progression.

Purpose
The aim of this study is to investigate whether specific comorbidities and substance use are associated with HF events or major VA events in PLN p.(Arg14del) positive individuals.

Method
Logistic regression analysis was conducted was conducted in a retrospective cohort of 880 PLN p.(Arg14del) positive individuals (N=847 without heart failure at baseline, N=810 without major arrhythmia at baseline and N=780 without both). Comorbidities and substance use at baseline were assessed in univariable and multivariable models with respect to HF events, major VA events, and a composite endpoint of these two referred to as ‘severe phenotype’. Heart failure was defined as heart failure hospitalization, left ventricle assist device implantation, heart transplantation or HF-related death. Major VA was defined as sustained VA, appropriate implantable cardioverter defibrillator intervention, or (aborted) sudden cardiac death.

Results
Of all tested comorbidities, only renal dysfunction defined as an eGFR (CKD-EPI) &lt;60 ml/min per 1.73 m² was significantly and independently associated with all endpoints (p&lt;0.001). In the substance use analysis, prior history of smoking was associated with heart failure and a severe phenotype (p&lt;0.002 and p&lt;0.021), however actively smoking was not associated with both endpoints.

Conclusion
Renal dysfunction and a history of smoking were the only factors identified to be associated with worse outcomes in PLN p.(Arg14del) positive individuals. Whether renal dysfunction signifies a risk factor or an early manifestation of disease has to be further elucidated.
Machine learning for development of ECG-based prediction models for atrial fibrillation, thromboembolism and mortality in patients with heart failure
Authors: Huang T H , Ahlgrim C A , Mueller-Edendorn B M E , Pilia N P , Eichenlaub M E , Allgeier J A , Keyl C K , Neumann F J N , Trenk D T , Westermann D W , Arentz T A , Jadidi A J 
Published: May 2024

Abstract

Background
Heart failure (HF) is a worldwide epidemic affecting more than 64 million people. Given its massive burden both socially and economically, early identification of individuals at risk for complications is important.

Purpose
To test the predictive performance of non-invasive models from 12-lead digitalized electrocardiography (ECG) and cycle ergometer for adverse outcome in individuals with heart failure.

Methods
598 Patients scheduled for right heart catheterization due to unexplained dyspnea were screened. All ECG recorded at admission were further analysed using the software "ECG-Precision-Analysis V-1-2023" to measure the duration of amplified p-wave (APWD at 175mm/sec, 80mm/mV) and to confirm the diagnosis of inter-atrial block. Ergometer was performed in a supine position with increasing intensity adapted to individual capacity (25 or 50W). The maximum physical capacity (Watt-max) was determined. Follow-up (FU) was scheduled at 12-,36-, 60- and 120-month after discharge with the primary endpoint as MACCE (a composite of new onset of AF, ischemic stroke and peripheral embolism) in at baseline event-free patients and the secondary endpoint as all-cause mortality in the total cohort. Three machine learning (ML) algorithms (COX regression, BSR and LASSO regression) were used to select candidate variables and establish predictive models for MACCE and all-cause mortality. Model evaluation was performed in terms of discriminatory power using C-statistics for each endpoint.

Results
Prediction models developed using machine learning (ML) algorithms (COX regression, BSR and LASSO regression) identified Watt-max and/or APWD as core components for MACCE and mortality prediction . APWD and Watt-max were key components of ML models for prediction of thromboembolic risk and AF (MACCE) and reached accurate diagnostic performances (C-statistics up to 0.74), moreover, another model consisted of APWD and aIAB (advanced inter-atrial block) was established to serve as a reference to ML models with moderate performance for MACCE (mean C-statistics &lt;0.70). All ML models for prediction of all-cause mortality included Watt-max and achieved C-statistics &gt; 0.75.

Conclusions
In patients with heart failure, the combination of APWD, aIAB and maximum excercice capacity allow to identify patients at risk for AF and stroke, whereas mortality is predominantly predicted by maximum exercise capacity but not APWD.Kaplan-Meier curves for MACCE/MortalitytimeROC curves for MACCE/Mortality
Preliminary implantation experience in Europe with a novel insertable cardiac monitor
Authors: Fareh S , Diamante A , Nardi S , Luzzi G , Scarano M , Sangiuolo R , Silvetti M S , Porcelli D , Argenziano L , Zanotto G , Charles P , Montoy M , Valsecchi S , Magnocavallo M 
Published: May 2024

Abstract

Background
The LUX-Dx is a new insertable cardiac monitor (ICM) available in the European market since October 2022.

Purpose
To describe the initial experience of LUX-Dx implantation in Europe and report feedbacks from operators and patients.

Methods
The system includes an incision tool and a single-piece insertion tool that is pre-loaded with the small (1.2 cm3) ICM. The implantation procedure is straightforward and comprises the incision, the creation of the device pocket, the insertion of the ICM, the verification of sensing and the incision closure. Following implantation, patients are provided with a mobile device preloaded with the App which connects to their ICM and transmits data daily, or as needed, to the management system. We analyzed data and feedbacks collected at 20 European centers at the time of implantation and before patient discharge.

Results
A total of 175 implantation procedures were performed in 20 European centers. The most frequent indications for ICM implantation were syncope (n = 119, 68%) and cryptogenic stroke (n=15, 9%). Most procedures (157, 90%) were performed in electrophysiology laboratories by expert operators. The procedure was performed under local anesthesia in 169 (97%) patients, and systemic or local antibiotics were used before the procedure in 96 (55%) cases. A surface ECG mapping was performed before 65 (37%) insertions. All ICMs were implanted in the left parasternal region, with an orientation of 45° in 159 (91%) patients. Device repositioning was required after verification of sensing in 6 (3%) patients. The ICM was successfully implanted in all patients and no procedural complications were reported; the median time from skin incision to suture was 4 min (25th-75th percentiles 2-7). The mean R-wave amplitude was 0.38±0.34 mV at implantation and 0.42±0.38 mV before patient discharge (on average after 1 day). The P-wave visibility, i.e., the ratio between the number of clearly identifiable P-waves and the number of heart cycles during 10 sec ECG with a regular 1:1 conduction, was 92±21% at implantation and 93±18% before discharge. Survey questions on the overall operator experience with the implantation procedure are reported in Figure 1 and those on the patient experience are reported in Figure 2.

Conclusions
Implantation of the LUX-Dx seems fast and uncomplicated; operator and patient feedbacks are positive. Post-implantation sensing values, i.e. R-wave sensing and P-wave visibility, are good.Figure 1Figure 2