bluuebunny/crossref_metadata_2025_split huggingface dataset:
121 year after Einthoven: show me the normal ECG?
Authors: Van Dam P , Malinowski K , Proniewska K K 
Published: October 2024

Abstract

Background
The clinical electrocardiogram (ECG) is measured about 120 years. The interpretations of the ECG is an art mastered by only few. Rhythm and Interval analysis is relative reliable and well supported by the available ECG machines. However, the interpretation of the ECG waveform and where it starts to deviate from the normal waveforms is poorly defined for the clinical practice. To support the anatomical relationship of the ECG, the PathECG has been developed as part of the CineECG tool, expanding the view on the ECG. In this study normal ECG distributions were created from a public available database to enable a) visual comparison of the ‘normality’ of the ECG and PathECG and b) build a predictive model to classify an ECG as normal or abnormal.

Purpose
To create an objective and 3D visual comparison of an ECGs to "normal ECGs".

Methods
From the (Physionet) PTB-XL database (Germany) all verified ECGs with known gender were selected. From the these ECGs a median ventricular beat was automatically constructed and subsequently resampled to have a standardized number of samples (Figure: Normal ECGs on top of abnormal ECGs). These resampled ECGs were also converted to their equivalent PathECG. From the normal labeled ECGs (3124 female, 2283 male) distributions were created, representing the normal ECG for male, female or the combined group. With these distributions a percentage of ECG segments (QRS, ST, T wave) can be identified as being outside this distribution, and thus as potentially abnormal. A logistic regression model was fitted to determine the optimal outlier removal per segment for the ECG waveforms and the PathECG to classify and ECG as normal or abnormal per gender group. The performance was tested on the CPSC (Hong Kong) public available ECG database.

Results
The abnormal ECG data in both databases contained approximately 1/3 normal ECG, atrial and ventricular arrhythmias and 1/3 ischemia and conduction disorders. The performance (AUC) of the logistic regression model on the PTB-XL and CPSC data respectively was MALES: 87.5% - 81.5%, FEMALES: 86.7% - 83.7%, and COMBINED: 86.8%-82.9%. The visual overlay of the ECG significantly supports the identification of abnormal segments (Figure: Normal ECG distribution example).

Conclusions
The overlay of an ECG and PathECG on the normal distribution supports the identification and verification of abnormal ECGs. This method might increase the ability of less experienced ECG readers, like general practitioners, to identify patients that need to be transferred to a cardiologist. This method could also be used for the classification of P-waves, paediatric ECGs or ECGs in specific patient groups.Normal ECGs on top of abnormal ECGsNormal ECG distribution example
Prognostic assessment based on the trajectory of artificial intelligence-enabled electrocardiograms for age estimation
Authors: Hwang T , You S C , Kim S , Eom S , Boo D , Kim D , Kim T H , Uhm J S , Pak H N , Lee M H , Yu H T , Joung B 
Published: October 2024

Abstract

Background
Artificial Intelligence (AI) estimation of age using data from the 12-lead electrocardiogram (ECG) has demonstrated its utility as a biomarker in predicting the future risk of cardiac diseases and mortality. However, expected long-term outcomes based on temporal changes in the AI-ECG age gap remain unclear.

Purpose
This study aims to utilize the difference between ages predicted by AI-ECG and actual ages longitudinally for follow-up and to assess whether there are significant clinical differences in mortality among specific groups.

Methods
Among patients at a hospital who had not been used for the AI-ECG age prediction model (121,702 individuals, 522,261 ECGs), 6,685 individuals with 31,858 ECGs who had an index ECG and the subsequent ECG in the fifth year, with at least one intervening ECG were included for analysis. The absolute age gap, defined as the absolute difference between ECG-predicted and chronological age, was limited to ±10 for analysis. Latent class trajectory modeling was then utilized to classify groups based on the AI-ECG age gap.

Results
Data-driven analysis revealed four distinct trajectory groups based on age gap trajectories: Consistently Young ECG (27.2%), Reversed ECG Aging (35.3%), Accelerated ECG Aging (8.0%), and Consistently Old ECG (29.5%). Notably, the Accelerated ECG Aging group exhibited a significantly higher cumulative incidence of all-cause death (adjusted hazard ratio (HR) 1.66 (95% confidence interval (CI) 1.30-2.12, p-value &lt;0.001) and non-cardiac death (adjusted HR 1.83 (95% CI 1.38-2.43, p-value &lt;0.001) compared to the Consistently Young ECG group.

Conclusion
Among 4 distinct trajectories based on the changes in the AI-ECG age gap, Accelerated ECG Aging was associated with a heightened risk of all-cause mortality. Additional in-depth analysis of factors associated with Accelerated ECG Aging is warranted.
Development of an automated process for monitoring of amiodarone treatment
Authors: Sjoland H , Landahl J , Tammelin K , Lindgren M , Rosengren A , Papachrysos N , Filipsson Nystrom H , Johansson B I 
Published: October 2024

Abstract

Background
Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to risk of side effects during long-term administration. Robotic process automation (RPA) represents the use of software robots to automate repetitive and routine tasks typically performed by humans.

Purpose
We aimed to develop an RPA using a diagnostic classification algorithm and clinical support system for follow-up of amiodarone treatment.

Methods
Based on international guidelines and clinical specialist advisory we constructed an RPA which was studied in a test environment in parallel with routine amiodarone treatment. Expert advice was integrated through a series of collaborative workshops, ensuring alignment of the algorithm with current best practices in thyroid and liver disease management. The RPA provided recommendations on the time interval to next laboratory testing and suggestions for patient management to the physician, who was serving as a human-in-the-loop responsible for clinical decisions.

Results
After iterative improvements the RPA was evaluated against physician orders (n=390 paired orders) without technical failures. The recommended time interval to next follow-up was (mean ± SD) 4.5 ± 2.4 vs. 3.1 ± 1.4 months for RPA vs. physician (p&lt;0.0001). While normal laboratory findings resulted in recommended follow-up in 6 months by the RPA (72.2%) this was only ordered in 9.9% by the physician (Figure). Physicians’ orders clustered between 3-4 months (58.5%). All patients diagnosed with new side effects (n=12) were detected and communicated correctly by the RPA, whereas only 8 were correctly detected and responded to by the physician (Table).

Conclusions
An automated process constitutes a reliable alternative to the current fully manual management of amiodarone follow-up. It may reduce manual labour, frequency of laboratory testing and detect side effects with increased precision, thereby reducing costs and enhancing patient value. While the RPA shows promise, further research is needed to explore its adaptability to other treatment scenarios.
Validation of chest-belt ECG evaluation using artificial intelligence
Authors: Skala T , Vacha J , Vicha M , Klimes D , Taborsky M 
Published: October 2024

Abstract

Background
Simple, cheap wearables for long-term ECG monitoring with automatic analysis of all measurements using a bespoke artificial intelligence (AI) are scarce. Annotated single-lead ECG data from a chest-belt are missing.

Methods
We have developed a complex software environment to acquire raw ECG data from the Polar H10 chest-belt, and a bespoke, original AI to detect artifacts, QRS complexes and the presence of atrial fibrillation (AF) from chest-belt recordings. Original manually annotated ECG recordings from real patients from cardiology departments using the chest-belt and a public MIT-BIH database as reference were used for validation.

Results
QRS determination by AI (version Q4/23) in evaluating MIT-BIH database of ECG samples had a 97.67 % sensitivity and a 97.73 % positive predictive value. AF in MIT-BIH database was detected by AI with 90.31 % sensitivity and 92.09 % positive predictive value.
To assess AI accuracy in ECG from a chest-belt, 150 consecutive measurements using Polar H10 chest-belt (215 hours of ECG recordings in total) from individual patients were included in the analysis. Patients were not limited in any physical activity during measurements.
7.15 % of the length of all recordings (15.4 hours) was assessed as noise by AI and excluded from the analysis. AF lasting longer than 30 seconds was present in 14 of 150 (9.3 %) manually evaluated recordings.
QRS determination by AI in evaluating manually annotated chest-belt data had 99.76 % sensitivity and 99.74 % positive predictive value. AF in manually annotated chest-belt data was detected by AI with 92.86 % sensitivity and 100 % positive predictive value.

Conclusion
A chest-belt can be used to reliably detect QRS complexes and the presence of atrial fibrillation with high accuracy when using a bespoke artificial intelligence for the evaluation. Commonly available devices capable of long ECG measurements could be used for patient-empowered screening for important arrhythmias.Atrial fibrillation on chest-belt ECG
Physical activity habits among members of the Athens Medical Society in Greece
Authors: Lampsas S , Marinos G , Lamprinos D , Gialamas I , Pantelidis P , Goliopoulou A , Gounaridi M I , Pililis S , Anastasiou A , Theofilis P , Kalogera S , Chatziralli I , Lambadiari V , Siasos G , Oikonomou E 
Published: October 2024

Abstract

Introduction
Benefits of regular physical activity and exercise are well documented. Healthcare workers play a crucial role in counseling and appropriately referring their patients to adopt physical activity. However, doctors’ exercise habits range between different medical specialties and health service infrastructure.

Purpose
In this study, we assess physicians’ exercise performance according to the recommendations of 2020 ESC Guidelines.

Methods
The population of this cross-sectional study comprised 742 physicians of the Athens Medical Society (AMS), Greece. The study instrument was a self-administered questionnaire mailed in October 2023 to investigate physicians’ exercise habits, baseline characteristics of population, specialty, and health service infrastructure [Greek National Healthcare System (GNHS) or Private Practice (PP)]. European Society of Cardiology (ESC) Guidelines recommend at least 150 min/week of moderate-intensity exercise in all healthy adults. Subjects were divided in two groups according to their compliance to ESC Guidelines: 1. Moderate-intensity endurance exercise training per week ≥ 150 min (Group A); 2. Moderate-intensity endurance exercise training per week &lt;150 min (Group B).

Results
The recommended exercise training per week goal is achieved by the majority of AMS members (53.4%) with an estimated mean exercise endurance training time of 240 ± 285 minutes. Between Group A and Group B there was no difference in age (50±12years vs. 50±13years, p=0.73), whereas there was significant difference in male sex prevalence (58% vs. 43%, p &lt;0.001), in Body Mass Index (24.7 ± 3.8 kg/m2 vs. 26.2 ± 4.7 kg/m2, p &lt;0.001), in Smoking habits (17.4% vs. 23.5%, p=0.04), in diabetes mellitus incidence (6.6% vs. 11.8%, p= 0.01) and in the use of activity monitoring wearables (45.2% vs. 29.8%, p&lt;0.001). Interestingly, there was significant difference between GNHS and PP doctors representation in Group A and B with more doctors in the PP categorized in the Group A (p=0.008) (Figure 1). Importantly, logistic regression analysis revealed that even after adjustment for the aforementioned confounders there was significant under-representation of the GNHS in the Group A or otherwise doctors in the GNHS have 33% decrease odds of achieving the recommended exercise training per week (Odds Ratio: 0.676, 95%CI 0.484, 0.943, p=0.02) (Table 1).

Conclusion
A significant proportion of the AMS members follows the recommended ESC guidelines for minimum exercise training per week. Several factors affect their adherence to exercise time recommendation. Interestingly, AMS doctors working on the GNHS independently of other factors are less adherent to exercise recommendations. These findings emphasize the need to strengthen prevention strategies to AMS.
Effect of COVID-19 pandemic on the management of hypertension and the prevalence of lifestyle disease
Authors: Isogaki T , Takase H , Kawakatsu N , Hayashi K , Kin F , Dohi Y 
Published: October 2024

Abstract

Background/Introduction
Behavior restrictions after the advent of COVID-19 dramatically changed our lifestyle, which may have affected self-management of metabolic factors in individuals.

Purpose
We examined possible changes in the prevalence and management of lifestyle-related diseases in the general population.

Methods
We enrolled 2582 consecutive participants aged ≥20 years (male=6984, 49.5±11.9 year-old) who visited our hospital for their annual physical check-up at least five times between 2017 and 2022. Participants were divided into 2 groups (with [n=739] or without antihypertensive medication [n=1843]) and changes in parameters that are closely related with lifestyle were examined before (2017-2019) and after (2020-2022) the pandemic of COVID-19. Hypertension was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or the use of antihypertensive medications. Diabetes mellitus was defined as fasting plasma glucose ≥126 mg/dL, HbA1c ≥ 6.5% or the use of antidiabetic medications. Dyslipidemia was defined as LDL-cholesterol ≥140 mg/dL, HDL-cholesterol &lt;40 mg/dL, triglyceride ≥150 mg/dL, or the use of antidyslipidemic medications. Hyperuricemia was defined as uric acid &gt;7.0 mg/dL for men, &gt;6.0 mg/dL for women or the use of anti-hyperuricemic medications. Metabolic syndrome was defined based on Japanese diagnostic criteria (2005).

Results
The main results are shown in Table. The levels of all parameters were worse in participants with than without anti-hypertensive medication. The prevalence of hypertension, diabetes, dyslipidemia, hyperuricemia and metabolic syndrome which was higher in subjects with than without anti-hypertensive medication increased after the COVID-19 pandemic. Although the most of parameters were deteriorated after the pandemic, only the management of blood pressure in subjects under antihypertensive medication was improved.

Conclusion
Behavior restrictions during the pandemic might have unfavorable impact on the prevalence of lifestyle-related diseases. Although the control of blood pressure was not worsened, total management was not excellent during the pandemic in hypertensive individuals under medication.Characteristics of study participants
Nationwide trends in major cardiovascular risk factors in England in the last 2 decades: Health Survey of England 2003-2019
Authors: Gupta A , Graham C , Capel M , Steckelmacher J , Prasher J 
Published: October 2024

Abstract
Cardiovascular disease (CVD) accounts for a quarter of deaths annually in the UK, with more than 7.6 million people living with the disease. Hypertension, diabetes, and obesity are among the most prevalent modifiable risk factors for CVD, and the strategies to reduce the incidence and prevalence of these CV risk factors are deemed the most effective to reduce this immense toll. In the UK, over the last few decades, several health policy decisions and pathways have been developed to reduce this toll.
We used data from 11 nationally representative, cross-sectional surveys from the Health Survey of England between 2003 and 2019 to evaluate trends in the prevalence of these CV risk factors and whether there has been any population-wide improvement in them. These surveys have cumulative information on 94,100 adults, and we applied sample weights, accounting for oversampling and nonresponse. We used "period definitions and cut-offs" to define those with diabetes. For example, for undiagnosed diabetes, we used glycated haemoglobin cut-off equivalent of 7% or more for years before 2011 and the current diagnostic threshold of &gt; 6.5% thereafter. This was done to facilitate and compare the trends after accounting for changes in medical practice and understanding across this period. Similarly, hypertension was defined as doctor-diagnosed hypertension or in its absence regular use of antihypertensive medications or blood pressure ≥ 140/90mmHg (period diagnostic threshold remains unchanged during). Python and STATA were used to conduct analyses, and Join Point regression software was used to develop and compare trends.
In England, from 2003-2019, mean population BMI consistently increased over this period, rising by 0.8kg/m2 from 27.6 to 28.8 kg/m2. Obesity prevalence (BMI ≥30kg/m2) increased by 5.4% from 22.6% to 28.0%. Concurrently, mean glycated haemoglobin increased by 0.36% from 6.18% to 6.54%, surpassing the current diabetes diagnostic threshold. Diabetes period prevalence surged from 4.2% to 11.6%, a trend consistent when the current diagnostic threshold was alternatively applied across all periods. Conversely, doctor-diagnosed and total hypertension (including those who are unaware) prevalence exhibited a slight decrease (1.4% and 3%, respectively), mirrored by reductions in population-wide mean systolic and diastolic blood pressure by 4.6mmHg and 1.9mmHg.
These results suggest that, with the total population in England standing at 56.3 million in 2019, an estimated 15.8 million adults were within the obese BMI range, 6.5 million had diabetes and 17.6 million had hypertension. The obesity epidemic is likely fuelling the increase in diabetes prevalence. These findings underscore the importance of prioritising obesity reduction in public health initiatives, aiming to subsequently curb diabetes prevalence. Hypertension still remains a major CV risk factor, affecting almost one third of the adult population.
Effects of mineralocorticoid receptor antagonists on new-onset or recurrent atrial fibrillation: a Bayesian and frequentist network meta-analysis of randomized trials
Authors: Karakasis P , Dimitrios D , Popovic D , Pamporis K , Theofilis P , Nasoufidou A , Stachteas P , Samaras A , Tzikas A , Giannakoulas G , Stavropoulos G , Kassimis G , Karamitsos T , Fragakis N 
Published: October 2024

Abstract

Aims
Clinical and translational research suggests that mineralocorticoid receptor antagonists (MRAs) may prevent atrial fibrosis and electrical remodeling associated with atrial fibrillation (AF). This study aimed to consolidate existing evidence from randomized controlled trials (RCTs) evaluating the effect of MRAs on incident or recurrent AF.

Methods
Medline, Cochrane Library and Scopus were searched until February 12, 2024. Triple-independent study selection, data extraction and quality assessment were performed. Evidence was pooled using both pairwise and Bayesian and frequentist network meta-analyses.

Results
Twenty-three RCTs (13,358 participants) were identified. Based on the pairwise random effects meta-analysis, MRAs were associated with a significant reduction in AF events compared to placebo or usual care (risk ratio {RR}= 0.75; 95% confidence interval {CI}= [0.66, 0.87]; P&lt; 0.001; I2= 3%). This protective effect was robust both for new-onset and recurrent AF episodes (subgroup p-value= 0.69), while the baseline HF status was not a significant effect modifier (subgroup p-value= 0.58). MRAs demonstrated a significantly higher reduction in AF events for patients with chronic renal disease compared to placebo (RR= 0.78; 95% CI= [0.62, 0.98]; P= 0.03; I2= 0%). The network meta-analyses revealed that only spironolactone was associated with a significant reduction in AF events (Bayesian RR= 0.76; 95% CI= [0.65, 0.89]; P&lt; 0.001; level of evidence moderate; SUCRA 0.731), while eplerenone and finerenone showed a neutral effect.

Conclusion
MRAs confer a significant benefit in terms of reducing incident or recurrent AF episodes, irrespective of HF status. In this context, spironolactone may be preferable compared to eplerenone or finerenone.
Investigating N6-methyladenosin RNA modification as a mediator of microRNA mechanical regulation in the heart
Authors: Martino F , Anwar M , Nicastro L , Ben-Aicha S , Terracciano C , Emanueli C 
Published: October 2024

Abstract

Background
The heart is exposed to mechanical forces and continuously adapts to its environment in a process known as cardiac remodeling. Excessive mechanical load is a primary driver of pathological cardiac remodeling, leading to heart failure (HF), a prominent cause of morbidity and mortality worldwide. A set of microRNAs (miRs), termed mechano-miRs, has been identified to respond to mechanical forces and contribute to heart pathophysiology and HF.
N6-methyladenosine (m6A) RNA modification is emerging as a novel regulatory layer impacting gene expression and occurring both in coding and non-coding RNAs. In this context, the N6-adenosine-methyltransferase METTL-3 regulates the processing of miR primary transcripts (pri-miRs) to functional miRs, promoting angiogenesis and cardiac repair after myocardial infarction. On the other side, dysregulation of m6A due to a defect in FTO, a m6A demethylase, has been implicated in HF. However, the role of m6A RNA modifications in heart mechanical responses and mechano-miR regulation remains unexplored.

Purpose
This project aims to: 1) investigate the impact of mechanical overload on the expression of m6A regulatory machinery components; 2) unveil the role of m6A-mechano-miRs axis in mechanically induced responses driving pathological cardiac remodeling leading to heart failure.

Methods and Results
Using living myocardial slices (LMS) technology, we modulate mechanical load in a multicellular environment while maintaining physiological behavior. Human LMS, obtained from the left ventricle of human donor non-failing hearts (NHS Blood and Transplant INOAR program, IRAS project ID: 189069) and rat LMS are subjected to two degrees of mechanical preload: physiological sarcomere length (SL) (SL = 2.2 μm) and overload (SL = 2.4 μm). This enables us to mimic the effects of mechanical stress on the heart during HF (volume overload model). LMS are maintained for 48 hours under electrical stimulation in circulating oxygenated media at 37°C.
Mechanically overloaded LMS exhibit decreased contractility, accompanied by an increase in global m6A levels compared to physiological controls. Consistent with this finding, the analysis of the expression of m6A machinery reveals a downregulation of m6A demethylases (FTO and ALKBH5) in overloaded LMS.
We identified putative cardiac mechano-miRs by small-RNA sequencing in both human and rat LMS and next predicted potential m6A sites on their primary transcripts via SRAMP (sequence-based RNA adenosine methylation site predictor).

Conclusions
Mechanical overload affects global m6A levels and the expression of m6A demethylases in the heart. Ongoing analysis of the m6A profile on the identified mechano-miR primary transcripts and mRNA targets holds promise for unveiling novel mechanically regulated m6A events, providing insights for transformative therapeutic strategies.