bluuebunny/crossref_metadata_2025_split huggingface dataset:
SLCO1B1*5 is protective against non-senile cataracts in cohort prescribed statins: analysis in a British-South Asian cohort
Authors: Magavern E F , Van Heel D A , Smedley D , Caulfield M J 
Published: November 2023

Abstract

Background
Reported association between statin use and cataract risk is controversial. The SLCO1B1 gene encodes a transport protein responsible for statin clearance. The SLCO1B1*5 reduced function variant is known to increase systemic exposure to statins. The aim of this study was to investigate a possible association between the SLCO1B1*5 reduced function variant and cataract risk in statin users of South Asian ethnicity.

Methods
The Genes & Health cohort consists of UK participants of Bangladeshi and Pakistani ancestry. SLCO1B1*5 genotype was assessed with the Illumina GSAMD-24v3-0-EA chip. Medication data from primary care health record linkage was used to compare those who had regularly used statins compared to those who had not. Multivariable logistic regression was used to test for association between statin use and cataracts, adjusting for population characteristics and potential confounders in 36,513 participants. Multivariable logistic regression was used to test association between SLCO1B1*5 heterozygotes or homozygotes and cataracts, in subgroups having been regularly prescribed statins versus not.

Results
Statins were prescribed to 35% (12,704) of participants (average age 41 years old, 45% male). Non-senile cataract was diagnosed in 5% (1686) of participants. An apparent association between statins and non-senile cataract (12% in statin users and 0.8% in non-statin users) was negated by inclusion of confounders. In those prescribed a statin, presence of the SLCO1B1*5 genotype was independently associated with a decreased risk of non-senile cataract (OR 0.7 (CI 0.5-0.9, p 0.007)).

Conclusions
Our findings suggest that there is no independent association between statin use and non-senile cataract risk after adjusting for confounders. Among statin users, the SLCO1B1*5 genotype is associated with a 30% risk reduction of non-senile cataracts. Stratification of on-drug cohorts by validated pharmacogenomic variants is a useful tool to support or repudiate adverse drug events in observational cohorts.Study results overview
Impact of qualified nursing education on self-care and disease knowledge in heart failure patients
Authors: Ayed S 
Published: November 2023

Abstract

Purpose
Guidelines on management of heart failure patients support intensive patient education on self-care. To evaluate the short and long-term impact of a structured education by a qualified heart failure nurse on patient´s self-care and disease knowledge.

Methods
131 patients (66±12 years) hospitalized for heart failure participated in a structured one hour education program by a heart failure nurse. Patients completed a questionnaire consisting of 15 questions, nine of the European Heart Failure Self-Care Behaviour Scale (EHFScB-9) and additionally six questions on the patient´s disease knowledge 1 day before, 6 months and 12 months after the education course. Possible responses ranged from 1 (complete agreement) to 5 (complete disagreement). Clinical events during the 12 months follow-up period were determined. Factors predicting with potential impact on the teaching effect were evaluated. Seven patients refused further participation during follow-up, 13 patients were lost during follow-up, and one patient was excluded from the study due to progressive dementia.

Results
Nine patients died during the study and 33 patients had recurrent hospitalization for heart failure. EHFScB-9 scale improved from 24.0±7.2 at baseline to 17.0±7.4 (p&lt;0.001) at 6 months, and 17.2±6.1 (p&lt;0.001 vs. baseline) after 12 months. Concerning the disease knowledge score, it improved from 17.9±5.3 at baseline to 12.1±4.3 (p&lt;0.05) at 6 months follow-up, and to 12.0±5.5 (p&lt;0.05) at 12 months after education. Scores to individual questions prior to education ranged from 1.2±0.8 on adherence to the medication protocol to 2.7±1.4 on everyday weighting habits. Greatest improvement after education were obtained on reaction to weight gains (-1.9±1.3), daily weight control (-1.6±1.4) and knowledge on the reason of patient’s heart failure (-1.6±1.2). The NYHA-classification improved from 2.8±0.8 before teaching to 2.2±0.7 (p&lt;0.001) at 12-months follow-up. No predictor of an insufficient teaching effect could be defined. Patients with a clinical event (death, recurrent hospitalization) had a combined EHFScB-9 and disease knowledge score of 32.6±8.1vs. 28.9±9.9 in those patients without a clinical event. Patients with more than one hospitalization due to acute decompensated heart failure during the follow-up period express insignificant improvement of the combined EHFScB-9 and disease knowledge score immediately after the education session.

Conclusions
A qualified nurse education program improves self-care behaviour and patient´s disease knowledge with a persisting effect at 6 and 12 months follow-up. There are no patient characteristics which preclude the implementation of an educational session.
Association of clinical outcomes and the use of telemetry monitors in patients with advanced heart failure
Authors: Saffi M , Silva Cruz R , Rodrigues Ilha V , Severgnini Eugenio C , Machado Pires Lemos D , Regina Da Luz K , Fantin S , Pereira De Souza L , Rejane Rabelo-Silva E 
Published: November 2023

Abstract

Background
Telemetry is a remote technology transmitted by radio frequency that sends patients' electrocardiographic signals in real-time, through a Bluetooth wireless network to a monitor located in a cardiovascular intensive care unit (ICU). This device is indicated for patients admitted in hospital clinical units that need monitoring (24 hours) for previous history or risk of developing cardiac arrhythmias. Patients with advanced heart failure often receive inotropic/antiarrhythmic drugs in the cardiovascular ICU. When clinically stable, this type of patient can be discharged from the cardiovascular ICU and monitored remotely in hospital clinical units using telemetry.

Purpose
We aimed o evaluate the clinical profile of patients with telemetry and to associate those with advanced heart failure who receive inotropic/antiarrhythmic drugs with unfavorable composite outcomes (death or cardiac arrhythmia).

Methods
Retrospective cohort study. Telemetry records were collected by nurses from the cardiovascular intensive care unit of a public university hospital. The study period was from January 2020 to May 2022. All the procedures in this study were in accordance with the Helsinki Declaration and approved by the Institutional Ethics Committee.

Results
In a sample of 703 patients, the mean (±SD) age was 63.4 ± 14.4 years, predominantly male 452 (64.3%). The median stay of the monitored patients was 4 (2-7) days. Among the monitored patients, regarding telemetry indications, patients diagnosed with advanced heart failure who receive inotropic/antiarrhythmic drugs had a prevalence rate of 12.8%. Of these, 32 (35.6%) had at least 1 complication, identified by telemetry, related significantly to unfavorable composite outcomes (P&lt;0.001). All these patients who had the event identification through telemetry received immediate assistance by the cardiovascular intensive care team.

Conclusion
The use of telemetry in patients with advanced heart failure who receive inotropic/antiarrhythmic drugs admitted to hospital clinical units was associated significantly with unfavorable composite outcomes. The early detection of clinical outcomes might be useful in decision-making and in reducing major complications.
Novel training concept utilizing a real world-like EP simulator
Authors: Kozlik M , Kosiuk J K , Cogiel M , Kost J K , Hemmerling D , Staniszewski M , Szczesna A , Wojakowski W , Jadczyk T 
Published: November 2023

Abstract

Background
Training in the field of invasive cardiac electrophysiology (EP) is challenging and requires intellectual skills related to the interpretation of intracardiac electrograms. Classic approach to education of young electrophysiologists focused solely on theoretical knowledge and overseen procedures in patients as no real life like simulation of complete diagnostic and therapeutic EP studies was available.

Purpose
The purpose of this study was to assess a novel tool for EP training based on fully interactive, online simulator providing real clinical experience to the users.

Methods
EP Simulator replicates EP recording system routinely used in cardiac electrophysiology laboratories and therefore enable easier transition from training to clinical situation. Users are able to access a fully functional system with simulated surface electrocardiogram and intracardiac electrograms mimicking signals recorded by a catheter located at high right atrium, His bundle, coronary sinus, and right ventricular apex. Normal electrophysiology study and the following cardiac arrhythmias are available for simulation: sick sinus syndrome, AV conduction blocks, atrial ectopy, atrial flutter and fibrillation, atrio-ventricular and atrio-ventricular nodal reentrant tachycardia and ventricular tachycardias. Evaluation by 40 electrophysiologists (mean experience 8±6y) from 16 countries was collected via online questionnaire.

Results
Realism of ECG signals was described as excellent or very good by 90% of responders, realism of intracardial signals by 82.5%. Realism of signal interactions and user experience was judged as excellent or very good by 75% and 70%, respectively. 100% of users agree definitely or mostly that EP Simulator helps to translate theoretical into practical knowledge. 97.5% of responders would include it in EP training programs as it is extremely or very useful for training purposes in opinion of 87.5%. 72.5% of responders think that training on EP simulator can potentially reduce rate of complications. In 87.5% the overall experience of EP simulator was completely or mostly satisfying and it would be recommended by 100% of respondents.

Conclusion
EP simulator is a feasible tool for training of young electrophysiologist and it may be potentially included in cardiologist curriculum. We should particularly emphasize the positive respondents’ evaluation of EP simulator overall realism. Further systematic evaluation of the training progress is needed.
CineECG for visualization of changes in ventricular electrical activity during ischemia
Authors: Van Der Schaaf I , Kloosterman M , Loh P , Van Dam P M 
Published: November 2023

Abstract

Background
CineECG is a newly developed method which provides an anatomical visualization of ventricular de- and repolarization in a non-invasive manner. It combines the electrical information from the 12-lead electrocardiogram (ECG) with a 3D heart model. The location and direction of the average ventricular de- and repolarization over time is projected on the model. Until now, it has not been used for visualization of ventricular electrical activity during myocardial ischemia.

Purpose
We aimed to visualize changes in ventricular de- and repolarization during acute ischemia with CineECG and relate these to changes in the ECG.

Methods
CineECG was used to provide the location and direction of the average ventricular de- and repolarization over time, using a standardized 3D heart/torso model. Continuous ECG’s during percutaneous transluminal coronary angioplasty with prolonged balloon inflation from the STAFF III database were used as study sample. ECG’s at baseline and at every 10 seconds during the first 150 seconds of balloon inflation were analyzed with CineECG. For the terminal QRS-complex, ST-segment and terminal T-wave, the direction of the CineECG was determined. The changes in CineECG direction relative to the direction at baseline were quantified by calculating the Δangle between the CineECG direction at baseline and the direction after every 10 seconds of inflation. Additionally, the root mean square amplitude (rmsA) of the ST-segment of all ECG leads was computed for every ECG.

Results
94 patients from the STAFF III database were included, 10 patients were excluded due to too much signal noise of the ECG. Within 10 seconds after start inflation, the median Δangle for the terminal QRS-complex, ST-segment and terminal T-wave were 21.8° [11.4 – 34.2] , 20.6° [8.0 – 43.9] and 23.5° [11.8 – 48.0], respectively. The median Δangle for the terminal QRS-complex reached its maximum after 130 seconds (41.0° [23.0 – 70.8]). The median Δangle of the ST-segment gradually increased to 26.1° [13.1 – 56.0] after 140 seconds of inflation. The Δangle of the terminal T-wave was greatest 30 seconds after start inflation (26.3° [12.6 – 49.2]), and changed only slightly during inflation. The rmsA of the ST-segment was 0.04 mV [0.03 – 0.08] during the first 10 seconds of inflation, and gradually increased until 0.08 mV [0.05 – 0.15] mV at 150 seconds.

Conclusions
CineECG proves to be very sensitive in detection of early changes in the ECG during prolonged balloon inflation, even before great changes occur in ST-amplitude. Therefore, CineECG might support the early detection of acute ischemia, even before clear ECG changes are visible.
Single-lead ECG based detection of arterial hypertension using artificial intelligence algorithms
Authors: Angelaki-Kaxiras E , Kochiadakis G , Barmparis G , Maragkoudakis S , Fragkiadakis K , Plevritaki A , Savva E , Zacharis E , Kampanieris E , Kassotakis S , Kalomoirakis P , Tsironis G , Marketou M 
Published: November 2023

Abstract

Background
Unawareness of arterial hypertension is an important contributing factor to the inadequate control of the disease and the absence of appropriate antihypertension treatments. Population screening programs have shown that more than 50% of hypertensives were unaware they had hypertension. Wearable devices, a ubiquitous accessory in our days, can record single-lead electrocardiograms that are then analyzed by machine learning (ML) algorithms. The recorded signal, most often of lead I, could potentially reveal an undiagnosed hypertension.

Purpose
The purpose of this study is to create and train a ML algorithm that detects the existence of arterial hypertension using features derived from a single-lead ECG (lead I),

Methods
We enrolled 1220 consecutive individuals without cardiovascular disease. The participants were classified into hypertensive and normotensive group. Hypertensive patients were recruited from the outpatient clinics of the respective centers. Normotensive healthy individuals were referred either for the investigation of atypical chest pain or for the modification of risk factors for cardiovascular disease such as hyperlipidemia. We trained a Random Forest (RF) model to classify subjects into 2 groups: hypertensive or normotensive. A feature-based machine learning algorithm, such as a RF, can also provide an interpretation of its results in the form of feature importance and interactions between features.

Results
The prevalence of hypertensive people in our study was 66.5%. Our RF model was able to distinguish hypertensive from normotensive patients on a hold-out test set, with an AUC 0.85, accuracy 81%, sensitivity 80% and specificity 83% (both using a threshold of 0.5), and an F1 score 0.86. When predicting on the subset of the test set containing people younger than 59 years old, it achieved AUC of 0.75, sensitivity of 50% and specificity of 87%.
Age, BMI, the area under the T wave divided by the QRS complex area, the area under QRS segment adjusted for BMI, the QT corrected interval, and the PQ interval, were the most important anthropometric and ECG-derived features in terms of the success of our model (Figure 1).

Conclusions
Our findings indicate that one-lead ECG based detection of arterial hypertension is possible by using ML algorithms. This study demonstrates the huge potential of artificial intelligence to transform smartwatches ECG signals into diagnostic tools.Figure 1
The V6R/V5R index, a novel ECG criterion, can identify idiopathic ventricular arrhythmias arising from the aortic cusp
Authors: Naruse Y , Hasegawa K , Nakamura K , Tsuji T , Kaeriyama T , Horiguchi S , Miyoshi M , Okazaki Y , Sasaki K , Minami K , Naito S , Tada H 
Published: November 2023

Abstract

Background
Idiopathic ventricular arrhythmias (VAs) with a left ventricular outflow tract (LVOT) origin often have dominant R waves (R wave amplitude&gt;S wave amplitude) in the precordial leads (V1 through V6). Radiofrequency catheter ablation (RFCA) within the aortic cusp can eliminate LVOT-VAs. However, some require RFCA at the aorto-mitral continuity (AMC), the endocardium just below the LV summit, and/or mitral annulus for a cure. No ECG indices have been fully established to help differentiate between VAs that can be ablated from the aortic cusp and those that require ablation from other sites.

Purpose
The purpose of this study was to identify ECG indices useful in differentiating VAs with an aortic cusp origin from those from outside the cusp among the LVOT-VAs.

Methods
Among 200 patients in whom successful RFCA of idiopathic VAs was obtained at the OT, this study included 36 (totaling 37 premature ventricular complexes [PVCs]) who had dominant R waves in the precordial leads and an LVOT origin. According to the PVC origins, the PVCs were classified into 2 groups: 1) Cusp group (n=22); PVCs were successfully eliminated by RFCA within or just under the aortic cusp and 2) non-cusp group (n=15); PVCs were successfully ablated from the other LVOT sites described above or by a sequential unipolar ablation (combination of the cusp, AMC, and mitral annulus). The detailed PVC morphology was compared between the 2 groups.

Results
There was no significant difference in the ratio of the Q wave amplitude in aVL to aVR, V2 R/S amplitude ratio, V2 R/S duration index, V2 transition ratio, transition zone index, or V2S/V3R index between the 2 groups. However, the ratio of the R wave amplitude in V6 to V4 (V6R/V4R index) was significantly higher in the cusp group than in the non-cusp group (0.68±0.14 mV vs. 0.52±0.15 mV, p=0.002). The receiver operating characteristic curve demonstrated that a V6R/V4R index of &gt;0.56 predicted the PVCs originated from the cusp with a sensitivity of 0.73 and specificity of 0.91 (area under the curve of 0.81).

Conclusion
The V6R/V4R index is a simple and useful ECG parameter and could distinguish PVCs with an aortic cusp origin from those originating from outside the cusp among the LVOT-VAs with a high accuracy.
Deep learning based prediction of atrial fibrillation incidence from 1-lead ECGs: a model development and validation study
Authors: Bremer J , Neyazi M , Knorr M S , Vollmer M , Gross S , Brederecke J , Ojeda F M , Doerr M , Blankenberg S , Schnabel R B 
Published: November 2023

Abstract

Introduction
Wearables capable of deriving biomarkers such as 1-lead ECGs become largely available, therefore offering the potential to screen for prevalent and incident cardiovascular disease within the population. Screening for prevalent atrial fibrillation (AF) from such devices is already widely applied. It has remained unclear whether predicting incident AF using 1-lead ECGs is possible. The assessment of AF risk before clinical manifestation may permit early detection and thus slow or limit the onset and progression of the disease by taking appropriate measures.

Purpose
Utilizing data from the UK-Biobank (UKB), we examined the possibility to predict three-year AF incidence from single-lead ECGs using deep learning-based methods.

Methods
For this study 100,551 1-lead ECGs from 91,898 unique participants were utilized, including 57,897 4-lead resting ECGs during ergometry from 2006-2010 and 9,826 at the first follow-up (FU) from 2010-2013 and 32,828 12-lead resting ECGs during the FU imaging study from 2014-2020. The 12-lead and 4-lead ECGs share information from lead I, which was used for all further analyses. We employ a 1D-Convolutional Neural Network (CNN) to predict the 3-year risk of incident AF. The CNN is compared with the clinically used CHARGE-AF Score. External validation was performed in the SHIP-START cohort including its three FU examinations (n=2,579; nFU1=3,101; nFU2=2,613; nFU3=1,482) and a median FU time of 5-years to FU and SHIP-TREND (n=2,931) with one FU after 7-years.

Results
The CNN provided a good ability to predict of 3-year incident AF at all three UKB FUs (AUC: 0.729, 0.727, 0.705), while the CHARGE-AF Score provides better results at Exam1 (0.773), worse at Exam2 (0.692) and equal at Exam3 (0.717). A combination of the CHARGE-AF Score and the CNN improved the individual predictions at all three FUs (AUC: 0.811, 0.741, 0.753). The CNN provides an equally good ability to predict incident AF at all four FUs in SHIP-START (AUC: 0.659, 0.785 0.689, and 0.772) and in SHIP-TREND (0.780). Combined with the CHARGE-AF-Score score, the performance in SHIP-START increases to 0.757, 0.861, 0.765, and 0.812 and in SHIP-Trend to 0.826 respectively.

Conclusion
Using a CNN to predict incident AF from 1-lead ECGs, we provide further evidence for the added value of clinically applied deep learning-based methods. By combining the CNN predictions on ECG data with the well-established CHARGE-AF score, 3-year AF incidence can be predicted with high accuracy.