bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of 18F-fluorodeoxyglucose positron emission tomography in the differential diagnosis of arrhythmogenic right ventricular cardiomyopathy and cardiac sarcoidosis
Authors: Costa S , Haug S M , Gasperetti A , Buechel R M , Kovacs B , Akdis D , Rossi V , Cimen T , Tanner F , Manka R , Ruschitzka F , Brunckhorst C , Duru F , Saguner A M 
Published: May 2024

Abstract

Introduction
Arrhythmogenic Cardiomyopathy (ACM) is defined as an arrhythmogenic heart muscle disorder not explained by ischemic, hypertensive, or valvular heart disease, which ranges from a systemic immunologic disorder such as cardiac sarcoidosis (CS), to a genetically determined one (arrhythmogenic right ventricular cardiomyopathy=ARVC). The aim of this study was to assess the utility of 18F-fluordeoxyglucose positron emission tomography /computer tomography (18F-FDG-PET/CT) in the differential diagnosis of ACM.

Methods
This study included 45 patients with a definite diagnosis of ARVC, in whom 18F-FDG-PET/CT was performed to screen for CS. Following a positive PET, diagnosis of CS was pursued according to the 2014 HRS consensus document. The patients were divided into two cohorts: the ARVC Cohort (ARVC-C) and the Cardiac Sarcoidosis Cohort (CS-C).

Results
All 45 patients received an 18F-FDG-PET/CT, of these 5 had a positive PET showing myocardial hypermetabolism. All 5 patients with a positive PET had a final diagnosis of histologically confirmed CS, as such the 18F-FDG-PET/CT had a negative predictive value (NPV) of 100%. All 45 patients also received genetic testing (cardiomyopathy panel). In the ARVC-C, 23 (58%) patients harbored a pathogenic (P)/likely pathogenic (LP) variant (40% in PKP2). In the CS-C, 1 (20%) patient with histologically confirmed CS harbored a LP DSG2 variant. Nine (23%) subjects in the ARVC-C presented with atrioventricular block (AVB) (grade I) and 7 (18%) with QRS fragmentation, whereas 5 (100%) patients in the CS-C presented with AVB (grade I and higher) and 4 (80%) with QRS fragmentation. On TTE, left ventricular (LV) involvement was less frequent in the ARVC-C (LVEF 52.2 +/- 8.7%; wall motion abnormalities in 19 patients (48%)), while LV-involvement in the CS-C was more frequent (LVEF 44.0 +/- 16.2%; wall motion abnormalities in 3 (60%). On cardiac magnetic resonance imaging (cMRI), late gadolinium enhancement was present in the ARVC-C in 14 (35%) in the RV and 15 (38%) in the LV, while it was present in 3 (60%) patients in the RV and 3 (60%) in the LV in the CS-C.

Conclusions
CS is an underdiagnosed clinical entity, which can be challenging to differentiate from ARVC. 18F-FDG-PET/CT has a high NPV to exclude CS in patients fulfilling definite ARVC criteria. Hence, we recommend to offer 18F-FDG-PET/CT to patients fulfilling definite ARVC criteria who present with AVB, QRS fragmentation, or biventricular involvement to screen for CS, especially to those who do not have an LP/P variant in the PKP2 gene.
Long-term risk assessment in athletes with ventricular arrhythmias: the key roles of arrhythmia morphology, complexity, and substrate
Authors: Compagnucci P , Casella M , Narducci M L , Conte E , Cammarano M , Pelargonio G , Andreini D , Palmieri V , Lo Russo G , Pontone G , Natale A , Tondo C , Crea F , Zeppilli P , Dello Russo A 
Published: May 2024

Abstract

Background
Ventricular arrhythmias (VAs) are an important source of concern among athletes.

Purpose
To determine the prognostic role of non-invasive and invasive diagnostic assessments among athletes with complex VAs.

Methods
We included 215 athletes (83% male; 28 [19-43] years) with complex VAs in a prospective, multi-center cohort study. At baseline, each athlete underwent a prespecified comprehensive diagnostic workup, which included cardiac magnetic resonance (CMR) in 202 subjects, and electrophysiology study (EPS)/electroanatomical mapping (EAM) in 190. The primary study endpoint was the occurrence of sudden death or sustained VAs during long-term follow-up.

Results
During a median follow-up of 6.2 (4.4-8.1) years, 16 (7%) athletes had a primary outcome event, including one sudden cardiac death. In univariable Cox models, sustained VAs at presentation (p&lt;0.001), uncommon VA morphology(p=0.004), syncope(p=0.005), abnormal ECG (p=0.003), VAs induction/persistence at exercise testing(p=0.021), late gadolinium enhancement (LGE;p=0.006), low-voltage areas (p=0.003) and late potentials (p&lt;0.001) at EAM, and sustained VA induction by EPS (p&lt;0.001) were all associated with increased risk of primary outcome events. The association of late potentials and EPS inducibility with primary outcome events was confirmed after controlling for all non-invasive predictors at univariable analysis, and the incorporation of findings of invasive tests allowed improved prediction of primary outcome events over clinical/non-invasive findings in isolation (log-likelihood ratio for nested models, p=0.043). Younger age (p=0.032), being a professional athlete (p=0.006), and absence of LGE (p=0.029) were independently associated with higher odds of uninterrupted competitive sports practice until last follow-up.

Conclusions
A comprehensive diagnostic assessment integrating clinical/ECG findings with CMR and EPS/EAM in selected cases may provide important prognostic information among athletes with complex VAs.
Twelve-lead electrocardiography-based artificial intelligence predicts the upcoming future of patients with heart failure with mildly reduced ejection fraction
Authors: Baek Y , Kim D Y , Lee S W , Lee D H , Lee S C , Jang J H , Shin S H , Choi W I , Kim D H 
Published: May 2024

Abstract

Background
Heart failure with mildly reduced ejection fraction (HFmrEF) has emerged as the predominant subtype of heart failure (HF), but there is a paucity of data for identifying it using artificial intelligence (AI) based electrocardiogram (ECG).

Purpose
This study aimed to develop artificial intelligence (AI)-ECG to identify and predict the prognosis of patients with HFmrEF.

Methods
We collected 104,336 12-lead electrocardiography (ECG) datasets from April 2009 to December 2021 in a tertiary center. The AI-ECG encompasses a novel model that combines an automatic labelling preprocessing method with a transformer architecture incorporating a triplet loss for HFmrEF analysis.

Results
The receiver operating characteristic analyses revealed that the area under the curve of AI-ECG for identifying all types of HF were acceptable (0.87, 95% confidence interval [CI]: 0.86-0.89), but that for identifying those with HFmrEF was relatively lower (0.83, 95% CI: 0.78-0.86) than those with HF with reduced ejection fraction (HFrEF) (0.89, 95% CI: 0.85-0.82) and those with normal ejection fraction (EF) (0.87, 95% CI: 0.85-0.89). The analysis of ECG features showed significant increases in QRS duration (p=0.001), QT interval (p=0.045), and corrected QT interval (p=0.041) with increasing ‘Severity by Euclidean distance’. Following the predictability analysis with another group of 1,134 patients for improvements of follow-up EF in HFmrEF, the patients were grouped into three clusters based on the AI- Euclidean distance; Cluster 1 had the most severe cases and poorer outcomes than Clusters 2 (p&lt;0.001) and 3 (p&lt;0.001).

Conclusion
AI-ECG presents an innovative approach for the prognostic stratification of cardiac contractility in patients with HFmrEF. By using AI-ECG, patients with HFmrEF will be able to predict upcoming disease progression.
Artificial Intelligence-based Electrocardiogram prediction for duration of Atrial Fibrillation
Authors: Park Y , Kim G K , Joo S , Chang M , Lee Y 
Published: May 2024

Abstract

Introduction
Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with poor clinical outcomes, including stroke, acute coronary events, and heart failure. Recent studies have shown that early rhythm treatment of new-onset atrial fibrillation improves the patient's prognosis. However, atrial fibrillation is often asymptomatic, and it is difficult to determine its duration accurately. Recently, AI-bsed ECG has been studied for various cardiovascular diseases.

Hypothesis
We sought to develop and validate a predictive model of the ECG for the duration of atrial fibrillation.

Methods
All patients aged 18 years older with at least one ECG were included in the study. Only patients with sinus rhythm with ECG prior to atrial fibrillation with ECG were selected. An ECG within 1 year from AF was first documented in ECG was defined as new onset AF. After dividing our datasets into training (and test sets, we developed an end-to-end deep neural network to predict for the duration of AF. Performance evaluation was conducted using various metrics, including AUROC, AUPRC, sensitivity, specificity, F1 score.

Results
The dataset consisted of 83,525 ECGs from 16,193 patients from two hospitals. The AUROC for discriminating old AF and new-onset AF is 0.8186 (0.8181 - 0.8190) on internal validation set and 0.7967 (0.7966 - 0.7969) on external validation set. Sensitivity, Specificity, and F1 score are 0.7126((0.7118-0.7134), 0.7697 (0.7693-0.7701) and 0.5751 (0.5745-0.5757) on internal validation set and 0.7309 (0.7307-0.7311), 0.7225 (0.7224-0.7227) and 0.6354 (0.6352-0.6356)on external validation set.

Conclusion
Our deep learning model can be used to predict atrial fibrillation's duration. Additional studies are ongoing to understand the relative importance of ECG features.
Clinical validation of an artificial intelligence algorithm offering automated corrected QT-interval measurements from a single lead electrocardiogram
Authors: Mannhart D , Kennedy A , Weidlich S , Hennings E , Doggart P , Mc Connell E , Isenegger C , Serban T , Du Fay De Lavallaz J , Knecht S , Osswald S , Kuehne M , Sticherling C , Badertscher P 
Published: May 2024

Abstract

Introduction
A novel smartwatch (Withings Scanwatch, SW) offers automated analysis of the QTc based on single-lead ECG recordings. Prior clinical validation studies showed insufficient accuracy when compared to manual measurements based on a 12-lead ECG.

Purpose
We hypothesized that a novel AI-based algorithm (PulseAI) is capable of measuring QTc-interval based on PDF-exports from a single-lead ECG with improved accuracy compared to the manufacturer’s automated QTc measurements.

Methods
In this prospective, observational study, consecutive patients underwent a nearly simultaneously 12-lead ECG and a single-lead ECG. Two blinded, independent cardiologists manually interpreted the QT-interval in leads I and II of the 12-lead ECG using the tangent-method, which served as the gold standard. QTc was calculated using Bazett’s formula. QTc measurements were compared using the Bland-Altman method.

Results
A total of 317 patients (48% female, mean age 63 ± 17 years) were enrolled. QTc-intervals could be automatically calculated by the SW and by the novel AI algorithm in 175 patients (55%). Diagnostic accuracy of SW-ECG for detection of QTc-intervals ≥ 450 ms as quantified by the area under the curve was 0.85 for the manufacturer and 0.9 for novel AI algorithm. In 21 patients (12%) QTc was ≥450ms. The Bland-Altman analysis resulted in a bias of 21 ms [95% limit of agreement (LoA) -48 to 89 ms] for the manufacturer algorithm and 2 ms [95% LoA -35 to 39 ms] for the novel AI algorithm, respectively, when compared with manual QTc-measurement based on the 12-lead ECG.

Conclusion
A novel AI algorithm outperformed the manufacturer algorithm for automated QTc-measurements based on PDF-exported single-lead ECGs.Central Illustration
Polygenic risk score for Atrial Fibrillation as a predictor for the incidence of Sick Sinus Syndrome: A comprehensive analysis from the UK biobank
Authors: Kim H J , Yang P S , Kim D H , Sung J H , Jang E S , Yu H T , Kim T H , Pak H N , Lee M H , Jung B Y 
Published: May 2024

Abstract

Background
Sick sinus syndrome (SSS) and atrial fibrillation (AF) often coexist, demonstrating a bidirectional relationship. The polygenic risk score (PRS) represents the cumulative risk of disease onset based on variations at multiple genetic loci and is known as a predictor for disease incidence. However, the PRS for SSS remains under-investigated. This study aims to evaluate whether the PRS for AF can predict the incidence of SSS in the general population.

Methods
The UK biobank, comprising of about 500,000 participants aged 40 to 70, was established across 22 centres in the UK between 2006 and 2010. After excluding missing values, prior AF history, and valvular heart disease, a total of 101,654 participants without AF (median age, 56.0 [interquartile range (IQR), 49.0–63.0] years; 44,851 [44.1%] male) were analyzed. Participants were divided into quartiles according to a validated PRS for AF. Throughout the observation period, the incidence of SSS event was documented.

Results
During a follow-up period of 11.7 (IQR 11.0-12.5) years, a total of 157 cases of Sick sinus syndrome were identified. In the new-onset SSS group, age, PRS for AF, BMI, and waist measurements were significantly higher. Additionally, there was a statistically significant increase in the proportions of male sex, hypertension, previous MI, hypothyroidism, dyslipidemia, and ESRD/CKD. The Kaplan-Meier analysis demonstrated an association between the highest quartile and an increased incidence of SSS compared to the other quartiles. (Log-rank P &lt; 0.001) The Cox regression model revealed that a PRS for AF was associated with an increased risk of incidence of sick sinus syndrome (aHR 1.29, 95% CI 1.10-1.51, p-value 0.002). Notably, the discriminative power of our model was substantial, yielding a C-index of 0.746.

Conclusion
In our study, the PRS for AF effectively predicted the incidence of SSS. In the absence of a dedicated PRS for SSS, the AF PRS may offer insights into developing one for SSS.
The right atrium affects in silico arrhythmia vulnerability in both atria
Authors: Martinez Diaz P , Sanchez J , Fitzen N A , Ravens U , Doessel O , Loewe A 
Published: May 2024

Abstract

Introduction
The role of the right atrium (RA) in atrial fibrillation (AF) has long been overlooked. Computer models of the atria can aid in assessing how the RA influences arrhythmia vulnerability and in studying the role of RA drivers in the induction of AF, both aspects challenging to assess in living patients. It remains unclear if incorporating the RA influences the reentry inducibility of the model. As personalized ablation strategies rely on non-inducibility criteria, the adequacy of left atrium (LA)-only models for developing such ablation tools is uncertain.

Aim
To evaluate the effect of incorporating the RA in 3D patient-specific computer models on arrhythmia vulnerability.

Methods
Imaging data from 8 subjects were obtained to generate patient-specific computer models. We created 2 models for each subject: a monoatrial with only the LA and a biatrial with both the RA and LA. We considered 3 different states of substrate remodeling: healthy (H), mild (M), and severe (S). The Courtemanche cellular model was modified from control conditions to a setup representing AF-induced remodeling [1] with 0%, 50%, and 100% changes for H, M, and S, respectively. Conduction velocity was set to 1.2, 1.0, and 0.8m/s for each remodeling state. Fibrosis extent corresponded to Utah 2 (5-20%) and Utah 4 (&gt;35%) stages [2] for M and S, while the H state was modeled without fibrosis. Arrhythmia vulnerability was assessed by virtual S1S2 pacing from different points separated by 2cm using openCARP [3]. A point was classified as inducing arrhythmia if reentry was maintained for at least 1s. The vulnerability ratio was defined as the number of inducing points divided by the number of stimulation points. The mean tachycardia cycle length (TCL) was assessed at the stimulation site. We compared LA vulnerability ratios in monoatrial and biatrial models.

Results
Incorporating the RA increased the mean LA vulnerability ratio by 115.8% (0.19±0.13 to 0.41±0.22, p=0.033) in state M and 29.0% in state S (0.31±0.14 to 0.40±0.15, p=0.219) as illustrated in Figure 1. No arrhythmia was induced in the H models. RA inclusion increased the TCL of LA reentries by 5.5% (186.9±13.3ms to 197.2±18.3ms, p=0.006) in scenario M and decreased it by 7.2% (224.3±27.6ms to 208.2±34.8ms, p=0.010) in scenario S. RA inclusion increased LA inducibility revealing 5.5±3.0 new points per patient in the LA for the biatrial model, which did not induce reentry in the monoatrial model.

Conclusions
LA arrhythmia vulnerability in a biatrial model differs from that in a monoatrial model. Incorporating the RA in patient-specific computational models unmasked potential inducing points in the LA. The RA had a substrate-dependent effect on reentry dynamics, altering the TCL of LA-induced reentries. Our results provide evidence for an important role of the RA in the maintenance and induction of arrhythmia in patient-specific computational models, thus suggesting the use of biatrial models.
Practical use case of natural language processing for comprehensive atrial fibrillation cohort characterization from electronic health records in Poland
Authors: Maciejewski C , Ozieranski K O , Basza M B , Barwiolek A B , Ciurla M C , Bozym A B , Krajsman M J K , Lodzinski P L , Opolski G O , Grabowski M G , Cacko A C , Balsam P B 
Published: May 2024

Abstract

Background
Electronic health records (EHR) contain data valuable for clinical research but mostly in textual format. Natural language processing (NLP) is a computational technique that allows text analysis.

Purpose
To demonstrate a practical use case of NLP in Polish EHR for a large retrospective study cohort characterization and compare it to a standard, manual retrieval by a human.

Material and methods
The dataset consisting of anonymized discharge documentation of 10314 patients from the cardiology tertiary care department in Poland was analyzed for inclusion in the registry of patients with oral anticoagulation for atrial fibrillation (AF). Extensive clinical characteristics regarding: 73 concomitant diseases, medications from 21 classes, and 15 numeric echocardiography parameters were collected manually and through NLP.

Results
There were 2624 and 2601 patients fulfilling study inclusion criteria according to human and NLP-based approaches, respectively, reflecting 99.5% accuracy of NLP in identifying correct study participants. The calculated CHA2DS2VASc and HASBLED scores based on both methods did not differ (human vs NLP; median, IQR, p-value): 3 (2-4) vs 3 (2-4) p=0.74 and 1 (0-1) vs 1(0-1) p=0.98. Despite numeric differences, no statistically significant differences in any of the analyzed clinical characteristics entities were found. Therefore, similar conclusions on cohort characteristics would be made in a shorter time.

Conclusions
NLP utilization in Polish EHR may accelerate acquisition and provide accurate data for a retrospective study.Patient flow.
Electronic 1 year follow up after ablation
Authors: Torsvik E 
Published: May 2024

Abstract

Background
Several studies have shown that phone calls are an effective but resource intensive Follow-up (FU) method to collect patient reported data (PROM) after discharge from hospital. Regarding patient preference for FU, who has been ablated for atrial fibrillation (AF), most of the AF patients favoured a questionnaire with a stamped return envelope. In 2019 the National registry for ablation and electrophysiology in Norway (AblaNor) was established. All patients who have been in an EP lab receive an electronic FU questionnaire (ePROM) 1 year after the date of ablation. In Norway, it has been developed a technical solution which allows data submitted from the patient to be registered in the Health Network,"ePROM engine". Little is known about the functionality and response rate of electronic solutions.

Purpose
The aim of our study was to investigate an electronic FU approach in general and in different patient subgroups from AblaNor.

Method
We used data from the National register of ablation and electrophysiologi in Norway, Ablanor. The data are FU questionnaire who all patients received 1 year after ablation. All questionares are the same, independent diagnosis group. The patients received the FU questionnaires electronically via Helse Norge.no. The data is based on registrations from 2020-21.

Results
The data shows that amongst the 7169 selected procedures in 2020 and 2021, 5235 completed the ePROM form . There is a difference in response rates for both age and sex alone. Younger women have a higher response rate than men of same age, whereas in older age groups,men have higher response rates than women . Patients who have been ablated for AF have a higher respons rate than other procedures. Patients who have only undergone an electrophysiological examination (EP) have the lowest response rate.

Conclusion
Mandatory follow-up 1 year after completion of the procedure takes place in electronic form and contains simple registration of PROMS. The response rate among patients is 78% over the two years. This indicates an extremely high level of digital competence and motivation among patients to provide feedback for completed treatment. This shows that there is a discrepancy for FU regarding patient who has been ablated for atrial fibrillation (AF) .

Abbreviations
RFA: Radiofrequency Ablation; FU: Follow-up; AF: Atrial fibrillation; PROM: Patient reported outcome Measurement