bluuebunny/crossref_metadata_2025 huggingface dataset:
Identifying at-risk patients for atrial high-rate episodes by machine learning
Authors: Chen Y , Gerra L G , Gue Y G , Mcdowell G M , Boriani G B , Zheng Y L Z , Lip G Y H L
Published: October 2024

Background

Current evidence suggests that atrial high rate episodes (AHRE)/subclinical atrial fibrillation (AF) are associated with increasing risk of clinical AF, and subsequently, ischaemic stroke and other adverse cardiovascular events.

Purpose

To construct a high-performance model by machine learning (ML) for the prediction of AHRE detected by a cardiac implantable electronic device (CIED) capable of atrial sensing in a European cohort.

Methods

We utilized data from a prospective cohort of patients with CIED. AHRE was defined as an episode lasting at least 5 minutes with atrial rate ≥ 175 beats per minute. We divided the cohort into training and internal validation cohorts in 7:3 and used 5-fold cross-validation in the training cohort to assess model performance and prevent model overfitting. Pre-selected features were imported into eight common ML algorithms to predict AHRE, including light gradient boosting machine (LightGBM), random forest (RF), logistic regression, support vector machine, multiple perceptron, eXtreme gradient boosting machine (XGBoost), Gaussian naïve Bayes, and K nearest neighbours. The importance ranking of the features was interpreted by calculating the SHapley Additive exPlanationation (SHAP) value for each feature within the optimal model.

Results

A total of 500 consecutive patients with CIEDs (mean age 70.2±13.3; 39.2% female) were included. Of these, 164 patients (32.8%) were found to have AHRE. Among all ML models, RF was the best performing model, which obtained the highest AUC (0.738), recall (0.673) and F1-score (0.584) in the internal validation cohort. The results of SHAP showed that antiplatelet, statin, age, white blood cell count, and cardiomyopathy were the first five important features, indicating that the RF model had good interpretability and reliability.

Conclusion

Our study is the first to demonstrate in a European cohort the effectiveness and feasibility of ML algorithms in predicting AHRE in patients detected by CIEDs. The best RF models require further external validation to assess generalizability to other cohorts.Figure 1Figure 2
Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention
Authors: Knorr M S , Brederecke J , Bremer J P , Neyazi M , Blankenberg S , Doerr M , Vollmer M , Schnabel R B
Published: October 2024

Background

Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB).

Methods

We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests.

Results

The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75).

Discussion

ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.
Rhythm classifier: artificial intelligence for rhythm classification in insertable cardiac monitor
Authors: Majumder S , Sarkar S , Landman S , King K
Published: October 2024

Background

Though detections improved significantly, separating atrial tachycardia/atrial flutter (AT/AFL) from atrial fibrillations (AF) in insertable cardiac monitors (ICM) remains a challenge.

Objective

Using artificial intelligence (AI) algorithms to classify AF and AT/AFL in ICM detected episodes, which may reduce episode review burden.

Methods

Both raw EGM signal and the QRS diminished version of the raw signal were used to extract 14 AF and AT/AFL features from each episode. Extracted features were then represented as an ensemble of features and used to train and test the AI model. Modified ResNet18 network was used as the AI model. All episodes from a random selection of 80% of patients were used as training dataset, another 10% were used as validation, and 10% were used as test dataset, to evaluate model performance. AI model probability threshold was chosen based on validation set results. Episode detection sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristics (ROC) curve in the independent test dataset are reported.

Results

AF and AT/AFL episodes detected in 918 LINQ ICM patients (5,253 AF and 4,705 AT/AFL – 745 pts and 8,950 false detection episodes) were included. Training dataset consisted of 15,183 episodes (735 pts), validation set consisted of 1,901 episodes (91 pts), and test set consisted of 1,824 episodes (92 pts). At chosen threshold in validation set, AF and AT/AFL detection had sensitivity of 90% and 73.3%, respectively, and specificity of 93.2% and 95%, respectively, with AUC of 0.966. In test data, AF and AT/AFL obtained relative sensitivity of 84.7% and 85.7%, respectively, with specificity of 88% and 86.5%, respectively (Fig. A). With ROC-AUC of 0.956 (Fig. B), the relative diagnostic yield of AF and AT/AFL on test data was 95.6%.

Conclusion

The trained AI model provides a relative sensitivity of about 85% for AF vs. AT/AFL classification and reduces inappropriate ICM detections by 79%. Adding more episodes may improve the performance.
Preventing unnecessary echocardiograms in de novo patients referred to the cardiology outpatient clinic using electrocardiogram-based deep learning
Authors: Arends B , Vessies M , Van Amsterdam W , Teske A , Van Der Harst P , Van Osch D , Van Es R
Published: October 2024

Introduction

The growing demand on European echocardiography labs, driven by the rise in structural heart disease (SHD), is further complicated by a shortage of skilled sonographers, necessitating resource optimisation strategies. Electrocardiogram (ECG)-based deep learning models may assist in streamlining the echocardiography workflow.

Purpose

This study sought to develop a deep learning model capable of identifying patients at very low risk of SHD in a population of de novo patients referred to the cardiology outpatient clinic.

Methods

We identified all adult patients who underwent a 12-lead ECG and echocardiogram at a Dutch academic teaching hospital between 2011 and August 2023. Echocardiogram outcomes were extracted from reports, including left ventricular systolic dysfunction (LVSD), left ventricular dilatation (LVD), aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR), from which a composite outcome label was derived. A cohort of patients newly referred to our cardiology outpatient clinic with a primary complaint of chest pain, dyspnea, palpitations, dizziness, a murmur, or an abnormal ECG was held out as a test set, while the remaining dataset was split patient-wise into training (90%) and validation (10%) sets. A convolutional neural network was trained using only the ECG tracing as input and the composite label as outcome.

Results

We included 92,210 ECG-echo pairs from 44,785 patients (19,717 female) with 25,543 (27.7%) abnormal ECG-echo pairs. Among 2724 newly referred patients in the test set, 210/1389 males (15.1%) and 201/1335 females (15.1%) were diagnosed with SHD. In the test set, the model for predicting composite outcome demonstrated an area-under-the-curve, negative predictive value, and specificity of 0.74 (95% confidence interval [CI] 0.71-0.79), 0.94 (95% CI 0.92-0.95), and 0.47 (95% CI 0.45-0.49), respectively.

Conclusion

This study demonstrates the feasibility of employing deep learning models based solely on electrocardiogram (ECG) data to identify patients at very low risk of structural heart disease (SHD). Further validation and prospective studies are warranted to confirm the generalizability and clinical utility of our findings in diverse patient populations and healthcare settings.
Barriers and facilitators for implementation of AI algorithms for ECG analysis and triage in patients with chest pain
Authors: Arends B , Mccormick J , Heus P , Van Der Harst P , Van Es R
Published: October 2024

Introduction

Numerous artificial intelligence (AI) algorithms have been developed to assist in clinical decision-making, with a focus on areas like electrocardiogram (ECG) analysis due to the signal complexity and the varying levels of expertise among healthcare professionals. However, ensuring acceptance of these algorithms by end-users is paramount for successful implementation.

Purpose

This study aimed to identify barriers and facilitators related to implementation of an AI algorithm for ECG analysis and triage in patients with chest pain.

Methods

A three-round modified electronic Delphi study was conducted among Dutch potential end-users of an ECG-AI algorithm, including physicians, nurses and ambulance professionals. During the first round, participants brainstormed on barriers and facilitators, which were subsequently mapped to the Consolidated Framework for Implementation Research (CFIR). In the following two rounds, participants reviewed the identified barriers and facilitators for relevance and ranked them according to their importance.

Results

Twenty-five out of the initial forty respondents completed all three rounds, resulting in a dropout rate of 38%. Four barriers and twelve facilitators were identified. The most important facilitator was "Faster recognition of subtle ECG abnormalities", while "Poor model performance" emerged as the most important barrier. The most frequently mentioned corresponding CFIR domains were the innovation and inner setting domains.

Conclusion

The identification of barriers and facilitators provides valuable insights into the acceptance and utilisation of ECG-AI algorithms by end-users. By considering the CFIR domains, including innovation and inner setting, this study offers a comprehensive understanding of the contextual factors that influence the successful implementation of AI algorithms in healthcare settings. Addressing these barriers and facilitators will be crucial to facilitating future implementation processes.Barriers and facilitators
External validation of a machine learning based classification algorithm for ambulatory heart rhythm diagnostics using smartphone-photoplethysmography - the SMARTBEATS algorithm study
Authors: Fernstad J , Svennberg E , Aberg P , Kemp Gudmundsdottir K , Engdahl J
Published: October 2024

Introduction

Smartphone-photoplethysmography (PPG) can be used for heart rhythm monitoring. According to current guidelines, ECG verification is required for a new diagnosis of atrial fibrillation (AF). Accurate machine learning (ML) based automatic algorithms have the potential to facilitate clinical applications generating large numbers of PPG recordings, by offloading the need for manual over-read.

Purpose

The aim of this study was to evaluate the diagnostic performance of an automatic ML-based algorithm for heart rhythm diagnostics using smartphone-PPG recorded by patients with AF in an unsupervised ambulatory setting.

Methods

Patients undergoing direct current cardioversion (DCCV) at a university hospital for treatment of AF or atrial flutter (AFL) were asked to perform one-minute heart rhythm recordings post-cardioversion at least twice daily for 30 days. All participants were provided with an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile).

The automatic algorithm uses support vector machines (SVM) to classify heart rhythm from smartphone-PPG recordings. The SVM classifier evaluated here was trained on ambulatory PPG recordings made by patients in a separate cardioversion cohort, ensuring complete independence between training and validation sets. PPG recordings in the validation cohort were analyzed by the automatic algorithm and diagnostic performance was calculated by comparing heart rhythm classification output from the SVM classifier to the heart rhythm diagnosis from the simultaneous ECG recordings. ECG recordings were interpreted independently by two cardiologists and were set as gold standard.

Results

The SVM classifier was trained on data from 153 patients with 11,749 PPG recordings, of which 5,873 (50.0%) were labelled as AF and 5,876 (50.0%) as sinus rhythm (SR), as assessed by manual reading. In the validation cohort, 280 patients, with a median age of 69.0 years (31% women), registered 18,005 simultaneous PPG and ECG recordings. Recordings interpreted as AFL on ECG (2.0%), as having insufficient quality on ECG (4.9%) or PPG (2.8%), and low certainty algorithmic classifications (1.2%) were excluded, leaving 16,057 PPG recordings. Included ECG recordings consisted of 71.2% interpreted as (SR) and 28.8% as AF. Algorithm classification of the PPG recordings in the validation cohort diagnosed AF (sensitivity) in 99.7% and SR (specificity) in 99.7% of the recordings, with an overall accuracy of 99.7%. F1-score was 99.4% and area under the ROC curve (AUC) was 0.999.

Conclusion

A machine learning based algorithm for automatic heart rhythm diagnostics showed excellent diagnostic performance for smartphone-PPG recordings in an unsupervised ambulatory setting, minimizing the need for ECG verification in cardioversion populations.
A novel digital twin strategy to enable the translation of evidence from randomized control trials to new populations
Authors: Thangaraj P , Vasisht Shankar S , Oikonomou E , Khera R
Published: October 2024

Background

Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. Between the SPRINT and ACCORD trials, only SPRINT found a significant reduction in major cardiovascular events (MACE) with intensive blood pressure control, but ACCORD did not, without clear evidence of whether this reflected differences in enrolled patients.

Purpose

To use a novel RCT digital twin-based approach to demonstrate the translation of an RCT to a different population, leveraging the differences in populations and treatment effects in SPRINT and ACCORD.

Methods

We developed a statistically informed Generative Adversarial Network (GAN) model that leverages relationships between covariates and outcomes using Directed Acyclic Graphs (DAGs). This model can generate a digital twin of an RCT (RCT-Twin) conditioned on covariate distributions from a second patient population while maintaining these relationships (Fig A). Using this, we generated the (i) RCT-Twin of SPRINT, which was conditioned on the 10 most disparate covariates drawn from ACCORD, and (ii) an RCT-Twin of ACCORD conditioned on SPRINT. To demonstrate treatment effect estimates of each RCT conditioned on the other RCT’s patient population, we evaluated the cardiovascular event-free survival of 10 iterations of these cross-trained RCT-Twins against the original trials.

Results

Using 9361 SPRINT participants and conditioning on 4733 ACCORD participants, we generated 10 ACCORD-conditioned SPRINT-Twin datasets. We confirmed that the RCT-Twins were consistently balanced between treatment and controls across all measured covariates (mean absolute standardized mean difference (MASMD) 0.019, SD 0.018), as expected for real RCTs. We also confirmed that covariates of the ACCORD-conditioned SPRINT-Twins were closer to ACCORD than SPRINT (MASMD 0.0082 vs 0.46, SD 0.016 vs 0.20), suggestive of successful conditioning. Most importantly, across iterations, ACCORD-conditioned SPRINT-Twin datasets reproduced the overall non-significant effect size seen in ACCORD (5-year MACE hazard ratio (95% confidence interval) of 0.88 (0.73-1.06) in ACCORD vs median 0.87 (0.68-1.13) in the ACCORD-conditioned SPRINT-Twins, Fig B), while the SPRINT-conditioned ACCORD-Twin datasets reproduced the significant effect size seen in SPRINT (0.75 (0.64-0.89) vs median 0.79 (0.72-0.86) in the SPRINT-conditioned ACCORD-Twins, Fig C).

Conclusions

Our novel conditioned digital twin approach simulates RCT-derived effects in different patient populations by translating these effects to the covariate distributions of the patients. This key methodological advance may enable the direct translation of RCT-derived effects into disparate patient populations and may enable causal inference in real-world settings.
Multiomic comparison of GLP1R analogues interaction networks
Authors: Wicik Z , Koutentakis M , Eyileten C , Nowak A , Motamedi P , Sourij H , Von Lewinski D , Postula M
Published: October 2024

Background

GLP1R analogs are drugs increasingly used in clinical practice in patients with type 2 diabetes, and obesity. Subsequent clinical trials indicate their beneficial effects, including cardioprotection, but the exact mechanisms of those actions are still not known.

Aim of the study: This study aims to understand how GLP1R analogs exert cardioprotective and therapeutic effects in patients with type 2 diabetes and obesity. Through analyzing GLP1R interaction networks and associated metabolomic and lipidomic data, we seek to uncover the molecular mechanisms underlying these effects and their potential therapeutic implications.

Methods

We performed an integrative analysis of four GLP1Ri interaction networks and associated metabolomic and lipidomic datasets. Interactomics revealed 130 common genes among semaglutide, exenatide, tirzapeptide, and retatrutide, primarily associated with diabetes-related processes, including obesity and hyperglycemia. Noteworthy findings encompassed ontological terms related to cardiovascular diseases (CVDs), such as hypertension, calcium regulation in cardiac cells, and amino acid accumulation-induced mTOR activation. Additionally, enrichment in gene sets linked to longevity, less recognized terms like fatty liver disease, chemokine signaling, and sirtuin interactome, were observed.

Results

Specific processes for tirzepatide included behavior-related terms and hydrochloric acid secretion, while retatrutide exhibited associations with fibrosarcoma, thinking and speaking disturbances, and adipogenesis signaling. Shared processes like primary ciliary dyskinesia differed in implicated genes. Metabolomics identified shared metabolites associated with diverse pathways, including galactose metabolism and nitric oxide-related pathways. Lipidomics highlighted enriched phenotypes and pathways, such as arachidonic acid metabolism and cholesterol biosynthesis.

Conclusion

Integration of interaction networks with metabolomic and lipidomic data revealed top interactors and impacted metabolites, shedding light on the intricate molecular landscape of GLP1Ri. These findings contribute valuable insights into the potential therapeutic implications and broader health considerations of GLP1R drugsfig1fig2
Association between warfarin and direct oral anticoagulants users and risk of mortality: cohort studies in England and Hong Kong
Authors: Wang Z , Matthewman J , Tazare J , Cheung K S , Chui C S L , Chan E W , Bhaskaran K , Smeeth L , Wong I C K , Douglas I J , Wong A
Published: October 2024

Background

The evidence of the association between warfarin and risk of mortality compared with direct oral anticoagulants (DOACs) has been mixed in current literature.

Purpose

To investigate the association between all-cause mortality and warfarin, compared with DOACs.

Methods

Population-based cohort studies were conducted using the data from the Hong Kong Clinical Data Analysis and Reporting System (CDARS) and United Kingdom Clinical Practice Research Datalink (CPRD) Aurum. People with atrial fibrillation aged ≥18 years who initiated warfarin or a DOAC during the study period (1st Jan 2014 - 31st Dec 2019) were identified. Follow-up started from the first date of oral anticoagulant prescription and ended at earliest of the date of death, the day before drug switching (between warfarin and DOACs), transferring out of the practice (CPRD Aurum only), last data collection date for the practice (CPRD Aurum only) or the end of study period. We estimated propensity score (PS) based on the list of potential confounders including demographics, co-morbidities, co-medications and polypharmacy. Cox proportional hazards regression model with PS-weighting was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs).

Results

Among 227,190 new oral anticoagulant users, 54,367 (23.93%) died. The median follow-up was 2.96 and 2.45 years in CPRD and CDARS, respectively. Compared with DOAC use, a lower hazard of all-cause mortality was found in warfarin use (PS-weighted hazard ratio [HR]=0.94, 95% confidence interval [CI]=0.89-0.99) in CPRD; while a higher hazard was observed in warfarin users (PS-weighted HR=1.31, 95% CI=1.24-1.39) in CDARS, versus DOAC users. Consistent results were seen in both databases when stratified warfarin users by Time in Therapeutic Range (TTR). We observed a lower hazard of all-cause mortality in warfarin users with TTR≥65% (PS-weighted HR=0.90, 95% CI=0.85-0.94 in CPRD; 0.87, 0.79-0.96 in CDARS) and increased hazard in warfarin users with TTR<65% (PS-weighted HR=1.37, 95% CI=1.25-1.49 in CPRD; 1.57, 1.48-1.66 in CDARS), versus DOAC users.

Conclusions

The hazard of all-cause mortality associated with warfarin compared with DOACs were found to be conflicting across databases. However, both databases consistently showed that an increased hazard of all-cause mortality was only found in warfarin users with TTR<65% but not in those with TTR≥65%, suggesting that the differences in hazard of all-cause mortality associated with warfarin compared with DOAC, in part may depend on anticoagulation control in warfarin users. We recommend that warfarin users with poor INR control warrant closer clinical attention and could be considered for a switch to DOACs.
Comprehensive cardiac interval analysis in the WEAR-TECH study cohort by comparing the Apple Watch Series 6 against a simultaneous 12-lead ECG
Authors: Schramm D , Varini R , Briosa E Gala A , Banerjee A , Betts T
Published: October 2024

Background

Wearable devices capable of single-lead electrocardiograms (ECGs) are becoming increasingly prevalent. Exploring their potential for screening conditions beyond atrial fibrillation presents a compelling research opportunity.

Objective

This study evaluates the agreement between the Apple Watch Series 6 (APW) and simultaneous 12-lead ECG (12L) recordings in measuring PR, QRS, RR, QT, and QTc intervals in a cohort of National Health Service (NHS) cardiology patients.

Methods

We analysed data from the WEAR-TECH study, which included 400 simultaneous recordings from the APW and 12L ECGs. We included patients with ECGs in sinus rhythm and excluded patients with poor-quality ECGs. No exclusions were made based on underlying cardiac conditions. Measurements of the best three consecutive waveforms in leads I, II, and V5 were performed using EP Calipers (EP Studio) by a single observer. Leads I and II were compared for PR, QRS, and RR intervals, while leads I, II, and V5 were compared for QT and QTc intervals. QTc was calculated using Bazett’s formula. Differences between APW and 12L ECGs were assessed using linear mixed effects regression models with random intercepts and type II Wald chi-square tests. Clinical agreement was measured by averaging the waveforms from 12L leads and the APW and calculating the difference between the means.

Results

Out of 324 paired ECGs from 162 patients, 8 were excluded due to missing or uninterpretable data. The median age was 64 (IQR: 54 - 74) years, with 117 (75%) being male. PR, QRS, and RR intervals from the APW agreed best with 12L lead II, and QT and QTc intervals from APW agreed best with 12L lead V5, with no significant difference found in any of these comparisons. PR and RR intervals significantly differed between APW and 12L lead I, with the APW tending to overestimate relative to the 12L. Additionally, significant differences were found in 12L leads I and II for QT and QTc, with APW also tending to overestimate these intervals (see Table 1). Clinical agreement between the APW and all 12L leads was a minimum of 21% for a <10ms difference, 45% for a <20ms difference, and 71% for a <40ms difference (see Figure 1).

Conclusions

The Apple Watch Series 6 demonstrates promising potential for initial outpatient monitoring of PR, QRS, RR, QT, and QTc intervals with reasonable agreement to 12L ECG. This is especially true in lead II for PR, QRS, and RR intervals and lead V5 for QT and QTc intervals. Further reductions in variance between APW and 12L ECG measurements could enhance clinical decision-making accuracy.Table 1:The p-values of APW vs 12L leadFigure 1:Agreement of APW to 12L leads
Meta-Heart Team: enhancing multidisciplinary heart teams through virtual reality in the metaverse
Authors: Skalidis I , Koliastasis L , Apostolos A , Mylonas K , Antiochos P , Maurizi N , Arangalage D
Published: October 2024

Introduction

Cardiovascular Multidisciplinary Heart Teams (MDHTs) are integral for patient management, but persistent challenges, including limited subspecialty expertise and time constraints, necessitate innovative approaches. This study explores the viability of conducting international Meta-Heart Team sessions utilizing virtual reality (VR) within the metaverse, aiming to address these challenges.

Methods

The Meta-Heart Team, comprising participants from diverse medical specialties and countries, engaged in VR sessions facilitated by VR headsets and web browsers. The immersive Aimedis metaverse platform recreated a three-dimensional hospital setting, complete with a dedicated Meta-Heart Team room featuring large screens displaying patient data such as coronary angiograms and echocardiograms. A 0-10 scale questionnaire evaluated participant experiences, covering technical quality, immersion, willingness to participate again, and overall satisfaction.

Results

The virtual environment effectively facilitated discussions on valvular heart disease cases among 10 participants, dedicating 15 minutes to each case. Technical stability was robust (90%), with only one participant experiencing internet disconnection. Positive participant feedback highlighted high ratings for technical quality (8.0±1.8) and engagement, a good immersive experience (8.0±1.3), willingness to participate again (9.2±0.8), and confidence in treatment decisions (80%). Despite a sense of digital fatigue reported by a few participants, the majority expressed that the VR session saved time compared to traditional meetings. Privacy concerns were acknowledged by 80% of participants.

Discussion

The virtual environment demonstrated potential in augmenting MDHT collaboration, providing advantages in global accessibility, multidimensional analysis, and adaptability. Participants recognized limitations in non-verbal communication and interpersonal connections but underscored the adaptability and effectiveness of the virtual setting. Technical challenges, including a single internet disconnection, emphasized the importance of a robust technical infrastructure. Privacy issues, digital fatigue, and potential exclusions were highlighted, prompting consideration of ethical guidelines and ongoing technological advancements.

Conclusion

Leveraging VR in the metaverse for MDHT sessions is feasible and offers substantial benefits. Despite challenges such as technical issues and privacy concerns, the Meta-Heart Team complements traditional interactions, ushering in a new era in collaborative decision-making while demanding ongoing attention to technological and ethical considerations.Two presenters explaining the processThe Metaverse-based Heart Team room
Effectiveness of telephone clinics for assessment of new onset chest pain - a retrospective single-centre observational study
Authors: Faizur Rahman M , Singh J K , Batcha S R , Iqbal J
Published: October 2024

Background

The COVID-19 pandemic led to a significant shift in the way clinical consultations are conducted with a rapid transition towards remote consultations (teleclinics). Although face-to-face (F2F) clinics are being re-introduced in the post-pandemic era, a significant proportion of clinics continue to run remotely. Despite the inability to physically examine the patient, remote clinics have notable advantages such as increased accessibility and flexibility for both the clinician and the patient.

Purpose

As the healthcare model moves increasingly towards a hybrid system where both F2F and teleclinics coexist, we sought to analyse the differences in practice between the two groups.

Methods

This single-centre retrospective analysis of patients referred to specialist cardiology services with NOCP compared face-to-face (F2F) consultations with teleclinic consultations. 70 patients were randomly selected in each category from a list of patients with NOCP that were referred to a tertiary cardiac centre from January to December 2021. Teleclinic appointments were conducted via telephone while F2F clinics had the patients visit the hospital for their appointments.

Results

The main findings are tabulated in Table 1. The samples for the two categories were evenly matched. Teleclinic appointments had a significantly reduced waiting time compared to F2F clinic (4 vs 6 weeks, p= 0.0009). As expected, the teleclinic group had reduced access to examination findings such as blood pressure, heart rate, and ECG. There was no significant difference in an initial working diagnosis of typical angina between the F2F group and the teleclinic group (30% and 25.7% respectively, p=0.706). However, patients in the F2F group were more likely to have anti-anginal therapy commenced compared to teleclinic group (50% vs 27.1% respectively, p=0.0089) following initial clinic review. Also, they were more likely to undergo an invasive coronary angiogram (30% vs 12.9% in F2F and teleclinic groups respectively, p=0.0224). However, this did not lead to a significant increase in PCIs performed in the F2F group. More patients had non-invasive tests requested in the teleclinic group compared to the F2F clinic group (62.9% vs 50% respectively, p=0.17) although this was not significant the median number of total investigations requested were the same in both the group.

Conclusion

Telephone consultations appear to be a reasonable alternative to F2F consultations with reduced waiting times and no significant impact on investigations ordered. However, lack of key examination findings may have hampered prescription of anti-anginal medications. Provisions such as readily available access to GP health care records on examination findings along with a streamlined remote prescription system can help overcome reluctance to prescribe antianginals. Further research is needed to assess clinical outcomes of teleclinic consultations.Table 1
Hypertensive myocardial disease screening in low-income settings using smartphone-based technologies: a prospective multicenter trial
Authors: Maurizi N , Skaldis I , Fumagalli C , Ochooa E , Tamba M , Olivotto I
Published: October 2024

Background

Mobile health technologies are revolutionizing cardiovascular (CV) medicine. However, more than 80% of heart disease related deaths occur in low-income settings, that are unable to take advantage of such technologies for healthcare awareness and prevention.

Purpose

To assess the feasibility of a pilot telemedicine-based CV screening campaign with D-Heart, a validated low-cost 8-lead smartphone portable ECG, coupled with blood-pressure (BP) monitoring in three regions of Africa.

Methods

A total of 950 patients (60%) men, age 29±21) were enrolled at 5 African rural dispensaries, 2 in Kenya, 2 in Senegal and at a hospital (Uganda). ECGs recording were obtained by community health workers with D- Heart smartphone ECG; BP was measured with a validated smartphone BP recorder. Global burden of ECG abnormalities was defined by a semi-quantitative score based on the sum of 9 criteria, identifying four classes of increasing severity. ECGs that were moderately and severely abnormal and from patients with severe hypertension, were sent for consultation via smartphone to D-Heart tele-cardiology centre: an advice on how to manage the patient was sent back to the community health worker’s smartphone within 15 minutes. If referral was needed, patients were sent to the local hospital.

Results

Mean BMI was 26.2 ± 8.5 (men 22.3±1.5 vs women 27.2±1.1, p<0.01). Active/recent history of smoking and alcohol intake were 327 (45%, 298 men) and 232/590 (40%, 198 men) respectively. A total of 142 (15%) patients had been previously diagnosed with diabetes, 199(25%) had known hypertension of whom 95/199 on treatment), whereas 43(4%) had a previous myocardial infarction. A total of 487 (51%) patients never had their blood pressure measured, whereas 798(91%) never received an ECG. Mean SBP/DBP were 138±31/71±12 mmHg respectively. Patients with normal BP were 304(32%), whereas 380(40%) were mildly-moderately hypertensive (SBP/DBP 148±7/91±5 mmHg) and 130 (29%) with severe hypertension (SBP/DBP 162±8/99±6 mmHg). D-Heart ECGs tracings were respectively classified as: normal: 361(38%); mildly abnormal: 332(35%); moderately abnormal: 199 (21%) and severely abnormal 58(6%). Most common ECG abnormality was a positive Romhilt-Estes index in 361, 38%. For 174(18%) patients smartphone tele-report advised referral to the local hospital, but only 121 69%) presented to the visit: 9 (8%) were found to have normal hearts, 57(47%) with hypertensive heart disease, 35 (29%) with rheumatic heart disease, 20 (16%) with HFrEF. Visit time was 8±2 minutes; cost of screening per patient was 1.10€: 0,30€ for consumable electrodes, 0.80€ for the community health worker visit time.

Conclusions

D-Heart ECG screening combined with smartphone BP measurement proved feasible and cost-effective. This should encourage to develop and extend low-cost/high-technology community-based CV screening programs in low-income settings.
Remote monitoring of patients with chronic heart failure class II-III (NYHA) using the mobile application 'M-cardio' in residents of Kyrgyzstan
Authors: Rustambekova A , Abbasova M A , Noruzbaeva A M
Published: October 2024

Background

This study aimed to assess the impact of remote monitoring through the "M-cardio" mobile application on patients with chronic heart failure (CHF) of NYHA class II-III, particularly those of ischemic etiology, residing in Kyrgyzstan.

Methods

A total of 244 patients with NYHA class II-III heart failure due to ischemic etiology were included in the study. During the inpatient phase, patients underwent training on self-monitoring and self-help strategies for heart failure. The intervention group received additional training on using the "M-cardio" mobile application. Patients were randomized into two groups: 1) intervention group with remote monitoring using the mobile application (n=127), and 2) control group under standard outpatient observation at their residence (n=117). The intervention group used the mobile application with a developed algorithm for clinical indicators, allowing real-time assessment of the patient's condition. The observation period extended for 12 months.

Results

The study evaluated changes in quality of life (MLHFQ), self-care ability (EHFSBS), and physical activity tolerance (6MWT). Initially, there were no significant differences between the groups. However, improvements in quality of life and self-care ability in the intervention group were evident at 6 months and persisted up to 12 months (44.8 ± 6.7 points and 56.5 ± 7.9 points, p < 0.01, in the intervention and control groups, respectively). Significant differences were also observed in physical activity tolerance after 12 months (6MWT) - 319 ± 79 meters in the intervention group and 233 ± 64 meters in the control group (p < 0.001). Moreover, there was a notable decrease in the proportion of patients discontinuing essential medications in the intervention group compared to the control group, particularly in ACE inhibitors/ARBs and beta-blockers.

Conclusions

The preliminary results indicate that the "M-cardio" mobile application for remote monitoring significantly improved the quality of life, self-care capabilities, and functional status of heart failure patients. The application demonstrated effectiveness in reducing recurrent hospitalizations and enhancing medication adherence, suggesting its potential for improving long-term outcomes and survival.
Impact of SLGT2 inhibitors treatment initiation on the reduction of atrial and ventricular arrhythmias in patients with implanted cardiac defibrillator
Authors: Minguito Carazo C , Rodriguez Manero M , Garcia Seara J , Martinez Sande J L , Fernandez Lopez X A , Garcia Rodeja F , Sanchez Munoz E , Rodriguez Santamarta M , Tundidor Sanz E , Fernandez Vazquez F , Gonzalez Juanatey J R
Published: October 2024

Introduction

Sodium-glucose cotransporter type 2 inhibitors (SGLT2i) have been associated with improved prognosis in patients with heart failure; however, their impact on atrial arrhythmic (AA) and ventricular arrhythmic (VA) events is not fully understood.

Methods

Retrospective multicentric study of heart failure patients with implantable cardiac defibrillator device (ICD) from 2015 to 2020 with or without cardiac resynchronization therapy (CRT) receiving SGLT2 inhibitors. Device-registered arrhythmic events were analyzed and compared in two time periods for each patient: one year before and one year after starting SGLT2i. Relevant VA were defined as the occurrence of any sustained VT (SVT) (>30 seconds), ventricular fibrillation (VF), or appropriate therapy (antitachycardia pacing or shock). All VA included all the relevant VA and the occurrence of non-sustained ventricular tachycardia (NSVT). AA included atrial fibrillation (AF) burden, and episodes of more than 24 hour of AF.

Results

A total of 195 patients (66.8 [61.3-73.1] years, 18.5% women) were included. A reduction was observed in the percentage of patients with any VA (pre: 52.3% vs post: 30.3%; P < 0.001) and clinically relevant VA (excluding NSVT) (pre: 21.5% vs post 8.7%; P < 0.001) in the post-SGLT2i period. There was also a reduction in the number of episodes per patient/year of NSVT (pre: 2 (1-5) vs post: 1 (0-2); P < .001), and SVT (pre: 1 (1-3) vs post: 0 (0-2); p = 0.046). This protective effect was also observed after controlling confusion factors (Any VA: OR 0.35 (0,24-0,50); p<0.001 and relevant VA: OR 0.30 (0,17-0,52); p<0.001). However, no differences were observed in the prevalence of AA (24.7% vs 18.8%; P = 0.117) or the burden of atrial fibrillation (pre: 0% (0-0.1) vs post 0% (0-0); p = 0.097).

Conclusions

Following the initiation of SGLT2i treatment, a reduction in the percentage of patients with relevant VA was observed. However, this effect was not observed for AA.Figure 1Figure 2
Effect of participant baseline characteristics on adherence to self-monitoring of diet, physical activity and weight in a 12-month weight loss mHealth trial
Authors: Burke L O R A E , Bizhanova Z B , Cheng J , Conroy M B , Beatrice B , Kariuki J K , Sereika S M
Published: October 2024

Background

Self-monitoring (SM) of lifestyle behaviors is the centerpiece of standard behavioral treatment (SBT) for weight loss as SM increases one’s awareness of their behaviors. Extensive evidence demonstrates a significant relationship between adherence to SM and successful weight loss outcomes.

Purpose

Examine baseline characteristics of participants that are associated with higher adherence to SM of diet, physical activity (PA), and weight in a 12-month mHealth weight loss trial.

Methods

SMARTER was a single-site, 2-arm randomized controlled trial. We randomized 502 adults with a body mass index (BMI) 27-43 kg/m2 to diet, weight, and PA SM plus tailored feedback messages (SM+FB, n=251) or SM alone (n=251). We provided the SM+FB group feedback messages via the SMARTER app on diet daily, PA every other day and weight weekly. The study provided a wearable PA tracker and smart scale for weight and utilized a smartphone app for dietary SM. Adherence of dietary SM was defined as the % of days with ≥50% of calorie goal recorded and PA SM was % of days with ≥500 steps. Weight SM was the % of days with weight based on the scale record. Linear mixed modeling was applied to assess the monthly percent adherence to SM of diet, PA, and weight over a 12-month period. Our analysis incorporated time effects in months and polynomials of time, treatment group assignment (SM+FB vs. SM), baseline variables of interest (e.g., BMI, age, sex, race, education years, income), and the interactions between treatment and time effects with other variables. For parsimony, non-significant effects were removed.

Results

The sample (N=502) was 82% white (n=414), 79% female (n=399), and had a mean age of 45.0±14.4 years and BMI of 33.7±4.0 kg/m2. Over 12 months, percent adherence to SM of diet, PA, and weight declined non-linearly. Treatment groups did not demonstrate significant differences. The impact of treatment on the monthly decline in adherence to diet SM was curvilinear and varied significantly over 12 months. Older age was associated with greater adherence to the SM of diet (b=0.72, 95% CI: 0.55-0.88, P < .0001), PA (b=0.50, 95% CI: 0.35-0.66, P < .0001), and weight (b=0.71, 95% CI: 0.57-0.85, P < .0001). Conversely, higher baseline BMI was associated with lower adherence to SM of diet (b=-1.14, 95% CI: -1.73 to -0.55, P < .0001) and PA (b=-0.79, 95% CI: -1.35 to -0.22, P= .006). Other baseline variables were not significantly related to adherence.

Conclusion

The findings suggest that individuals with a higher BMI at baseline may need additional enhancement strategies to improve their engagement with the digital tools for SM lifestyle which could lead to improved weight outcomes.
N-acetyl-beta-D-glucosaminidase and kidney injury molecule 1: detection of potential tubular injury in atrial fibrillation
Authors: Lang F , Schlossbauer M , Staudner S , Leininger S , Vogel M , Lehn P , Burckhard R , Zimmermann C , Hupf J , Hubauer U , Maier L , Jungbauer C
Published: October 2024

Introduction

Acute heart disorders are often associated with an already present atrial fibrillation and cardiorenal syndrome in the emergency department (ED). This study focused on evaluating the potential cardiorenal biomarkers N-acetyl-β-D-glucosaminidase (NAG) and kidney injury molecule 1 (KIM) in a real-world chest pain cohort according to sinus rhythm (SR) and atrial fibrillation (AFib).

Methods

The urinary concentrations of the tubular markers NAG and KIM were measured in 601 samples (mean age = 61.1 y, SD = 16.9 y) of patients presenting with acute chest pain in the ED of a university hospital. 533 patients presented with SR in the initial ECG upon hospital admission contrary to 68 patients presenting with AFib.

The participants were divided into 4 groups based on their diagnosis for hospitalization, Acute coronary syndrome (ACS), acute decompensated heart failure (ADHF), non-ischemic chest pain without congestion (NICP) and chest-wall-syndrome (CWS). The CWS group only included healthy patients presenting with SR.

Results

Significantly increased NAG and KIM concentrations were identified among the patients presenting with AFib compared to those presenting with SR (each p < 0.001, AUCNAG = 0.730, AUCKIM = 0.634). Furthermore, ACS patients showed significantly increased tubular biomarker concentrations when presenting with AFib compared to SR (each p < 0.05). Similarly, for NICP patients significantly elevated NAG-concentrations were measured (p < 0.001), opposite to KIM (p = n. s.). Regarding ADHF, the concentration of NAG did not significantly differ between AFib and SR. However, KIM concentrations were significantly reduced in AFib. Both, NAG and KIM showed significantly higher concentrations in ACS or ADHF compared to CWS patients (p < 0.001).

Regarding multivariate linear binary regression, NAG was an independent predictor of AFib (p < 0.001), contrary to KIM, blood NT-proBNP and high-sensitivity troponin T, blood creatinine (each p = n. s.).

Conclusion

Both, NAG and to a lesser extent KIM correlate with AFib. This finding implies significantly higher tubular injury in acute heart diseases among individuals beating in AFib compared to those in SR. Hence, AFib may have a high impact on renal function and the cardiorenal syndrome in ACS-patients.ROC-Curve comparing AFib and SR overallNAG in AFib and SR according to disease