bluuebunny/crossref_metadata_2025_split huggingface dataset:
Standardization of transthoracic impedance values for estimating heart failure and its utility
Authors: Nose D , Ibayashi Y , Arimura T , Matsui T , Matsuda Y , Otsuka T , Yasumoto K , Sugimoto M , Miura S 
Published: October 2024

Abstract

Background
The use of intrathoracic impedance for examining pleural effusion in heart failure patients has been explored previously. However, due to concerns about its accuracy and complexity, transthoracic impedance values have had limitations in quantifying extravascular lung water. Our dual objectives were to clarify the relationship between intrathoracic conditions and percutaneous transthoracic impedance values, and to establish a basic model for a new machine learning-based estimation system for assessing intrathoracic conditions in patients with heart failure.

Methods
First, we developed a live porcine congested lung model that induced pleural effusion by substantial fluid loading, and then longitudinally evaluated its correlation with percutaneous transthoracic impedance values. Second, we conducted multi-frequency bioelectrical impedance analysis to simultaneously collect electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers

Results
In the porcine model, pleural effusion correlated with a concurrent decrease in SpO2 and a gradual decrease in impedance. We indexed and generated features from the measured values and developed an intrathoracic estimation model based on electrical measurements and clinical findings using a decision tree-based machine learning approach. Among the 286 features collected per individual, the model used 16 features. Notably, the model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an AUC of 0.905 (95% CI: 0.870-0.940) in cross-validation, significantly outperforming the conventional frequency-based method.

Conclusions
Transthoracic impedance values, when indexed, reflect intrathoracic conditions and improve estimation. Our results highlighted the potential of machine learning and thoracic impedance measurement for accurate estimation of pleural effusion. This approach provides an effective means of assessing intrathoracic conditions.
Figure: The upper figure shows the relationship between impedance values and pleural effusion in the congested lung model. On these results, human data were collected to construct the estimation model. The lower illustrates the accuracy of the model and provides a comprehensive view of the system.
AI-based cluster analysis enables outcomes prediction among patients with increased LVM
Authors: Loutati R , Kolben Y , Luria D , Amir O , Biton Y 
Published: October 2024

Abstract

Background
The traditional classification of left ventricular hypertrophy (LVH) solely based on left ventricular geometry disregards associated comorbidities and clinical characteristics. We aimed to identify unique clinical phenotypes of LVH using unsupervised cluster analysis and explore their association with clinical outcomes.

Methods
From UK Biobank (UKBB) participants with available left ventricular mass (LVM) estimations, we identified those meeting CMR-based criteria for increased LVM: LVM index ≥ 72 g/m2 for males and LVM index ≥ 55 g/m2 for females. Baseline demographic, clinical, and laboratory data were collected. Using Ward's method, patients were clustered based on 27 variables. The primary outcome was a composite of all-cause mortality with heart failure (HF) admissions, ventricular arrhythmia, and atrial fibrillation (AF). Cox proportional hazard model and Kaplan-Meier survival analysis were applied.

Results
Among CMR-assessed UKBB participants, 4,255 individuals exhibited increased LVM, with an average age of 64 ± 7 years; 2,447 (58%) were females. Cluster analysis identified four distinct subgroups. Over a median 5-year follow-up (IQR: 4-6), 100 patients (2%) died, 118 (2.8%) were hospitalized due to HF, 29 (0.7%) were hospitalized due to VA, and 208 (5%) were hospitalized due to AF. Univariate Cox analysis revealed that compared to the 1st cluster (n=1,578), patients in the 2nd (n=1,296), 3rd (n=824) and 4th (n=557) clusters had 60%, 104%, and 164% increased risk of a major event, respectively (95% CI 1.2-2.16, p&lt;.001 for cluster 2; 95% CI 1.49-2.78, p&lt;.001 for cluster 3; 95% CI 1.92-3.62, p&lt;.001 for cluster 4). Each cluster manifested different phenotypes: cluster 2 comprised mainly overweight females, with the highest prevalence of chronic lung disease; cluster 3, predominantly composed of males, had the highest burden of comorbidities and cardiovascular risk factors; and cluster 4, mostly males, presented with the most abnormal cardiac measures, including left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV), and left ventricular ejection fraction (LVEF).

Conclusions
Unsupervised cluster analysis identified four subgroups among patients with increased LVM. These subgroups bear notable relevance to overall mortality risk and clinical outcomes. This phenotypic classification holds promise in offering valuable insights regarding clinical course and outcomes in LVH patients.
Accuracy of the Apple Watch in detecting atrial fibrillation among patients undergoing 24-hour Holter monitoring
Authors: Inocian E , Junia A , Cordovez M G , Katada K , Paday L M W 
Published: October 2024

Abstract

Background
As direct-to-consumer wearable devices like the Apple Watch become more common, an increasing number of patients arrive at clinics with reports of irregular rhythms captured on their devices.

Purpose
This study sought to assess the accuracy of a commonly used smart wearable device (Apple Watch) in detecting atrial fibrillation.

Methods
We enrolled 140 patients referred for 24-hour Holter monitoring in a Cardiovascular Center. Following thorough disclosure, participants were instructed to wear an Apple Watch provided by the researchers in addition to the Holter monitor to facilitate comparison. The Apple Watch was selected due to its widespread usage among patients. Participants were instructed to record an ECG using the Apple Watch hourly, or more frequently while awake. The objective of the study was to evaluate the sensitivity and specificity of the irregular rhythm notification and ECG-based detection features of the Apple Watch in comparison to the conventional 24-hour Holter monitoring for atrial fibrillation detection.

Results
The irregular rhythm notification feature of the Apple Watch exhibited a low sensitivity of 21.4% but achieved a high specificity of 100% in detecting atrial fibrillation. Meanwhile, the ECG-based detection feature of the Apple Watch analyzed from 1,295 ECGs captured by the participants when compared in real-time to their ECG rhythms recorded by the Holter monitor, revealed a high level of agreement with a sensitivity of 100% and a specificity of 99.1%.

Conclusion
The low sensitivity of the irregular notification feature of the Apple watch in detecting atrial fibrillation cautions against relying solely on it to rule out atrial fibrillation as the cause of a patient’s symptoms. Nevertheless, the high concordance of manually taken Apple watch ECGs positions the device as a reliable tool for detecting atrial fibrillation through its ECG-based detection feature.
Low conduction velocity is a key factor for localisation of reentrant drivers for atrial fibrillation
Authors: Ogbomo-Harmitt S , King A , Aslanidi O 
Published: October 2024

Abstract

Background
Atrial Fibrillation (AF) is a prevalent cardiac arrhythmia globally, associated with heightened risks of stroke, dementia, and heart failure. Catheter ablation, the sole curative therapy for AF, yields suboptimal success rates for persistent AF cases. Understanding the mechanisms and dynamics of AF remains challenging, hindering treatment advancements. Novel ablation strategies aimed at targeting substrates harbouring AF reentrant drivers (RD), such as low-voltage ablation and rotor-based ablation, have yet to demonstrate superiority over the gold standard ablation strategy – pulmonary vein isolation.

Purpose
The purpose of the study is to integrate in-silico 3D left atrial (LA) simulations and explainable artificial intelligence (AI) to pinpoint key factors for RD localisation in persistent AF, aiming to improve the mechanistic understanding of AF and help develop a more effective ablation strategy.

Methods
3D LA models were derived from magnetic resonance images of 41 patients and combined with atrial cell electrophysiology models. Persistent AF was initiated in each patient-specific LA model with fast pacing, and five functional features (action potential duration (APD), conduction velocity (CV), wavelength, APD spatial gradient, CV spatial gradient, and wavelength spatial gradient) and three structural features (fibrotic density, fibrotic entropy, and image intensity ratio) were evaluated. A random forest AI model was trained to classify phase singularities corresponding to the RDs (PS-RD) in five distinct tissue classes (total LA tissue, healthy tissue, fibrotic border zone, dense fibrotic tissue, and PS-RD tissue) based on the functional and structural features. Shapley additive explanations (SHAP) analysis was then used to find the most influential feature in the AI model’s decision process.

Results
Simulations of the patient-specific LA models revealed that RDs were typically localised in regions with the lowest atrial CV, which often coincided with patches of dense fibrotic tissue. In a five-fold cross-validation, the random forest AI model achieved high accuracy in classifying PS-RD regions: a mean ROC AUC of 0.99 ± 0.010, a mean F1 score of 0.86 ± 0.034, a mean recall of 0.95 ± 0.058, and a mean precision score of 0.79 ± 0.085. The further SHAP analysis revealed that low CV was the key determinant in classifying the PS-RD regions.

Conclusion
Our study combined AF computational modelling and explainable AI to identify the key factors in RD localisation. Both the computational and AI models concluded that low CV was the most important determinant in localising RDs. These findings provide mechanistic evidence for the efficacy of ablation strategies that target low CV atrial areas for persistent AF patients.
To monitor or not after detected AF, this is a question: report from the consumer-led screening programme of long-term extension mAFA cohort
Authors: Guo Y 
Published: October 2024

Abstract

Background
The consumer-led screening for atrial fibrillation (AF) is evolving with the widespread availability of wearables. Nonetheless, what the long-term impact on the individual's behavior of rhythm monitoring after detected AF is unclear.

Objective
To investigate the long-term impact on health behavior for the individuals using wearables for cardiac rhythm.

Methods
In the mobile technology supported screening and integrated care in atrial fibrillation (mobile atrial fibrillation application, mAFA programme), the general population freely decided to enter the screening programm with wristbands or watches across China. At least 14-day monitoring was proposed, the individuals with detected AF can consider to receive the mAFA App for AF care. The adherence of rhythm monitoring after detected AF with wearables was calculated, while the multinomial logistics regression model was utilised to analyze the independent factors associated with the adherence.

Results
There were 4337172 individuals entered into AF screening programme using wearables between Oct, 2018 and Feb 14, 2024. Among them, 22224 individuals (0.5%, 22224/4337172, mean age, SD, 55, 16 years old, male 82.4%) were monitored with AF episodes. There were 21223 individuals remaining the wearables use for rhythm monitoring after first AF identification. The time (days, median, interquartile ) using wearables for monitoring the pulse rhythm were 465 (130, 889) days. The time to first identified AF (days, median, interquartile) were 16 (1, 176) days. The continuing monitoring time since first identified AF (days, median, interquartile) were 290 (54, 668) days.
The adherence of cardiac rhythm monitoring after detected AF with wearables (Figure): 96.0% individuals continued the rhythm monitoring using wearables during 30 days, 58.8% in 2 years, and 35.7% in 3 years and over.
In the adjusted multinomial logistics regression model, the palpitation, hypertension, obstructive sleep apnea, heart failure, age and mAFA use were independently associated with the adherence for the individuals remaining the wearables use for rhythm monitoring after detected AF (all p&lt;0.05).

Conclusions
The general population at risk were likely to receive long-term rhythm monitoring after detected AF, and the mAFA use, with the self-care support, was an independent factor for the continuing monitoring.
Traditional dual-chamber right ventricular pacing versus conduction system pacing in the US medicare population
Authors: Vijayaraman P , Longacre C , Kron J , Subzposh F , Zimmerman P , Butler K , Crossley G H , Ellenbogen K A 
Published: October 2024

Abstract

Introduction
Conduction system pacing (CSP) has emerged as an alternative therapy to traditional right ventricular (RV) pacing. However, the majority of CSP studies reflect small cohorts or single-center experience.

Purpose
This analysis aimed to compare CSP to dual chamber (DC) RV pacing in a large, population-based cohort utilizing data from the Micra Coverage with Evidence Development (CED) study.

Methods
Medicare administrative claims data were used to identify patients implanted with a DC RV pacemaker. Lead placement data from Medtronic’s device registration system was used to identify patients with treated with CSP (N=6,197) using a 3830 catheter-delivered lead or DC RV (non-3830 lead, non-CSP placement) (N=16,989) at the same centers. CSP patients were stratified into left bundle branch area pacing (LBBAP) (N=4,738) and His-bundle pacing (HBP) (N=1,459). Incident heart failure hospitalizations (iHFH), all-cause mortality, complication rates and reinterventions at 6 months were analyzed.

Results
CSP (LBBAP and HBP) patients with a 3830 catheter-delivered lead experienced significantly lower rates of iHFH (HR: 0.70, P=0.02) and all-cause mortality at 6 months compared with DC RV patients (HR: 0.66, P&lt;.0001) (figure). There was no difference in complications (HR: 0.89, P=0.06) or need for reintervention (HR: 0.83, P=0.08) with LBBAP compared to DC RV, though HBP patients experienced significantly higher rates of complications (HR: 1.41, P=0.001) compared to LBBAP.

Conclusion
Patients treated with CSP with a 3830 catheter-delivered lead experienced significant all-cause mortality and HFH benefits compared to DC RV pacing. LBBAP had lower complications compared to HBP. These real-world results in a large cohort of Medicare patients align with findings in small clinical studies demonstrating the benefits of LBBAP. Future research will focus on understanding patient selection for CSP.Figure
Echo triaging works. How to use international guidelines
Authors: Kipourou K , Jimeno M , Keskin Meric B , Chester R , Barron A J , Rahman Haley S , Baltabaeva A 
Published: October 2024

Abstract

Introduction
Transthoracic echocardiography (TTE) is the first-choice non-invasive imaging modality for the assessment of cardiac structure and function due to its wide availability, speed, cost-effectiveness and reproducibility. Therefore, there is exponential increase in demand in TTE across all specialties in medicine and surgery. Hence, it is important to carefully triage patients according to current international guidelines.

Purpose
To reduce inappropriate TTE scans by using guideline driven indications in triaging echo scans in the UK tertiary hospital.

Methods
Strict list of guidelines driven indications adapted to local practice was used for triaging echo requests which included both inpatient and outpatient TTEs. The 4 objectives: appropriateness, completeness of requests as well as impact of performed studies on the follow-up and patient management plan were assessed before and after implementation of new triaging tool. To ensure similar working and staff environment was sustained we studied the pattern of referrals in the same month of the year- September. The triage process was conducted by accredited physiologists.
The same month was chosen to reflect true change of practice. Change in patient management and follow up were evaluated for a minimum duration of 6-months.

Results
Overall there were 1112 TTEs performed in September 2022 and 1081 - in September 2023. Prospective TTE referral triaging began in March 2023. We have studied and analyzed above criteria retrospectively. In 2022 there were 18% inappropriate requests; 37% of requests lacked sufficient clinical information and 10% of studies did not lead to change in management plan according to clinical letters. There was also high proportion of inappropriate requests for inpatients (9%). Within 6 months of implementation there was a significant improvement in the appropriateness (82% vs. 99%, p&lt;0.001), with increase in supply of adequate clinical information (63.4% vs. 86.1%, p&lt;0.001). However, there was no significant change using TTE study results in changing patient management (10%). All inpatients had 100% appropriate requests.

Conclusion
The implementation of triage criteria resulted in a significant improvement in TTE referrals: both in appropriateness and completeness of requests. However, some clinicians did not use echo in patient management plan. Future research on lack of use of TTE results in clinical practice might help to utilize the limited echo capacity to full.
The impact of daily step count with mobile health apps on clinical outcomes among general population
Authors: Takeji Y , Nomura A , Takamura M , Tabuchi T 
Published: October 2024

Abstract

Background/Introduction
Numerous studies have investigated the correlation between daily step count and the reduction of all-cause mortality and cardiovascular events. However, the optimal daily step count for the general population remains controversial.

Purpose
This study aims to assess cardiovascular risk based on individual step counts using a large cohort that includes step count data from mHealth Apps (Kencom), health checkups databases, and Japanese insurance claims databases.

Methods
We analyzed data from 85,204 users registered with Kencom between January 2016 and September 2021. The mean step count during one year before and after baseline data collection was used to define each individual's step count. We divided participants into low (N=45,539) and high (N=39,665) step count groups based on their mean step counts. Additionally, we categorized users into five groups based on their step counts (&lt;4000; 4000-5999; 6000-7999; 8000-9999; and ≥10000) to evaluate cardiovascular events. The primary outcome was the occurrence of cardiovascular events, including myocardial infarction, stroke, angina pectoris, heart failure, and atrial fibrillation, as classified by the International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). The cumulative incidence was estimated over 5 years.

Results
The high step count group was generally younger and predominantly male. No significant differences in cardiovascular risk factors such as hypertension, dyslipidemia, diabetes mellitus, and current smoking status were found between the two groups. Stratifying baseline characteristics by step count revealed significant differences among the five groups, with the exception of sex. The cumulative incidence of cardiovascular events was significantly lower in the high step count group compared to the low step count group (6.5% vs. 7.5%, log-rank P=0.005, Figure 1). After adjusting for confounding factors, the risk reduction of the high step count group remained significant (HR: 0.92, 95% CI: 0.87-0.98, P=0.005). Among the five step count groups, the incidence of primary outcome significantly differed, with only Group 5 (≥10000 steps) showing a significant reduction in the risk of cardiovascular events compared to Group 1 (&lt;4000 steps) after adjustment (HR: 0.87, 95% CI: 0.77-0.99, P=0.03). Subgroup analyses based on sex indicated that the risk reduction was significant in males in Group 5 but not in females.

Conclusion
The risk of cardiovascular events was higher in the low step count group compared to the high step count group. After adjusting for confounders, only the highest step count group (≥10,000 steps/day) showed a significant reduction in cardiovascular event risk compared to the lowest step count group.
Comparison between smartphone 6-lead ECGs and 12-lead ECGs in athletes
Authors: Davis A , Orchard J W , Mcghie D , Driscoll T , Puranik R , Orchard J J 
Published: October 2024

Abstract

Introduction
Athletes occasionally experience transient arrythmias that are difficult to capture using traditional methods. Smartphone electrocardiogram (ECG) technology presents an innovative method of capturing transient arrythmias. A 6-lead (6L) smartphone device that measures leads I, II, III, aVR, aVL, and aVF, has been shown to be accurate in some populations but has limited evidence in athletes. A previous pilot study comparing 6L with 12L readings in athletes showed good correlations.

Purpose
To assess the accuracy of the 6L device in athletes by comparing the 6L with a 12-lead (12L) ECG in a larger sample size under real-world conditions.

Methods
From 2020-2023, athletes (n=217, mean age 18.0±3.8 years, 40% female) had a resting supine 12L ECG as part of cardiac screening and a seated 30s 6L reading within 2 hours of the 12L. Manual measurements of RR, QT, PR intervals, and QRS duration in the 6L and 12L ECGs were completed by a cardiologist using EPS Digital Calipers in lead II. A subset (n=30) was measured by 4 expert cardiologists and the mean was taken. Each measurement was calculated as an average of at least 6 consecutive beats in the 6L and 12L. Bland-Altman analysis was used to assess the quantitative agreement between heart rate (HR), QTc interval (Bazett), PR interval and QRS duration in the 6L and 12L. ECGs were analysed for agreement of QRS axis (normal, left and right axis deviation defined by the International Criteria for Athlete ECG Interpretation[1]).

Results
The 6L readings for HR were 5.0±11.0bpm faster than the 12L; 6L readings for other measurements were shorter than the 12L. QTc had the largest difference, with the 6L on average 14.2±27.4ms shorter than the 12L reading. For QTc, 5.5% of readings were &gt;60ms different, mostly due to baseline interference in the 6L traces. PR intervals were 13.9±16.3ms shorter in the 6L than the 12L. QRS duration measurements were similar, with 6L readings being 0.8±10.2ms shorter than the 12L reading. There was 96.8% agreement on QRS axis classification between 6L and 12L.

Conclusion
The 6L readings had fair agreement with the 12L ECG, although the standard deviations were higher than measured in the pilot study, suggesting less consistent accuracy of the 6L. Most 6L measures (excluding HR) were slightly shorter than the 12L, consistent with studies in other populations. QRS axis categories were very similar. Given the change from supine to seated, it is unsurprising that HR was higher in the 6L. Our previous work showed good correlation between 6L and 12L readings and excellent utility of the portable 6L device for detecting transient arrhythmias in athletes. This larger study confirms most of those findings but adds caution that baseline interference is not uncommon in real-world 6L readings, meaning that QTc may be difficult to accurately measure.