Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care.

Aims: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department.

Methods And Results: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses.

Percutaneous repair of moderate-to-severe or severe functional mitral regurgitation in patients with symptomatic heart failure: Baseline characteristics of patients in the RESHAPE-HF2 trial and comparison to COAPT and MITRA-FR trials.

Aim: The RESHAPE-HF2 trial is designed to assess the efficacy and safety of the MitraClip device system for the treatment of clinically important functional mitral regurgitation (FMR) in patients with heart failure (HF). This report describes the baseline characteristics of patients enrolled in the RESHAPE-HF2 trial compared to those enrolled in the COAPT and MITRA-FR trials.

Methods And Results: The RESHAPE-HF2 study is an investigator-initiated, prospective, randomized, multicentre trial including patients with symptomatic HF, a left ventricular ejection fraction (LVEF) between 20% and 50% with moderate-to-severe or severe FMR, for whom isolated mitral valve surgery was not recommended.

Explaining deep learning for ECG analysis: Building blocks for auditing and knowledge discovery.

Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed.

Using explainable AI to investigate electrocardiogram changes during healthy aging-From expert features to raw signals.

Cardiovascular diseases remain the leading global cause of mortality. Age is an important covariate whose effect is most easily investigated in a healthy cohort to properly distinguish the former from disease-related changes. Traditionally, most of such insights have been drawn from the analysis of electrocardiogram (ECG) feature changes in individuals as they age.

Association of an impaired GH-IGF-I axis with cardiac wasting in patients with advanced cancer.

Background: Growth hormone (GH) resistance is characterized by high GH levels but low levels of insulin-like growth factor-I (IGF-I) and growth hormone binding protein (GHBP) and, for patients with chronic disease, is associated with the development of cachexia.

Objectives: We investigated whether GH resistance is associated with changes in left ventricular (LV) mass (cardiac wasting) in patients with cancer.

Methods: We measured plasma IGF-I, GH, and GHBP in 159 women and 148 men with cancer (83% stage III/IV).

[Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?].

The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis.

Addendum to the German Consensus Recommendations on Ponatinib in the Treatment of Chronic Myeloid Leukemia.

Background: Based on the new data from the primary analysis of the OPTIC (Optimizing Ponatinib Treatment in CP-CML) trial on dose optimization of ponatinib in patients with chronic phase (CP)-CML, the German consensus paper on ponatinib published in 2020 (Saussele S et al., Acta Haematol. 2020) has been updated in this addendum.

[Big hype about ChapGPT in medicine : Is it something for rhythmologists? What must be taken into consideration?].

ChatGPT, a chatbot based on a large language model, is currently attracting much attention. Modern machine learning (ML) architectures enable the program to answer almost any question, to summarize, translate, and even generate its own texts, all in a text-based dialogue with the user. Underlying technologies, summarized under the acronym NLP (natural language processing), go back to the 1960s.

PTB-XL+, a comprehensive electrocardiographic feature dataset.

Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public.

Clinical and Prognostic Relevance of Cardiac Wasting in Patients With Advanced Cancer.

Background: Body wasting in patients with cancer can affect the heart.

Objectives: The frequency, extent, and clinical and prognostic importance of cardiac wasting in cancer patients is unknown.

Methods: This study prospectively enrolled 300 patients with mostly advanced, active cancer but without significant cardiovascular disease or infection.

[Alternative treatment strategies for atrial fibrillation : Benefits from yoga?].

Atrial fibrillation, the most common sustained cardiac arrhythmia, is associated with significant morbidity, mortality, and healthcare utilization. Since the procedures used to treat atrial fibrillation have a number of limitations and risks, there is a growing interest in alternative treatment strategies for patients with atrial fibrillation. One such option is yoga.

Rare Variation in Drug Metabolism and Long QT Genes and the Genetic Susceptibility to Acquired Long QT Syndrome.

Background: Acquired long QT syndrome (aLQTS) is a serious unpredictable adverse drug reaction. Pharmacogenomic markers may predict risk.

Methods: Among 153 aLQTS patients (mean age 58 years [range, 14-88], 98.

[Second-degree AV block type Mobitz I (Wenckebach) in a 49-year-old man with dilative cardiomyopathy].

A 49-year-old man with dilated cardiomyopathy (left ventricular ejection fraction 50%, unremarkable left ventricular biopsy) developed atrioventricular conduction abnormalities (AV block II type Wenckebach) during exercise testing.

Spontaneous Non-Sustained Ventricular Tachycardia and Premature Ventricular Contractions and Their Prognostic Relevance in Patients with Cancer in Routine Care.

It is largely unknown whether cancer patients seen in routine care show ventricular arrhythmias in 24 h electrocardiograms (ECGs), and whether when they are detected they carry prognostic relevance. We included 261 consecutive cancer patients that were referred to the department of cardiology for 24 h ECG examination and 35 healthy controls of similar age and sex in the analysis. To reduce selection bias, cancer patients with known left ventricular ejection fraction <45% were not included in the analysis.