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. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. Conclusion: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.

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. Patients were randomized 1:1 to a strategy of delivering or withholding MitraClip. Of 506 patients randomized, the mean age of the patients was 70 ± 10 years, and 99 of them (20%) were women. The median EuroSCORE II was 5.3 (2.8-9.0) and median plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP) was 2745 (1407-5385) pg/ml. Most patients were prescribed beta-blockers (96%), diuretics (96%), angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor-neprilysin inhibitors (82%) and mineralocorticoid receptor antagonists (82%). The use of sodium-glucose cotransporter 2 inhibitors was rare (7%). Cardiac resynchronization therapy (CRT) devices had been previously implanted in 29% of patients. Mean LVEF, left ventricular end-diastolic volume and effective regurgitant orifice area (EROA) were 31 ± 8%, 211 ± 76 ml and 0.25 ± 0.08 cm2, respectively, whereas 44% of patients had mitral regurgitation severity of grade 4+. Compared to patients enrolled in COAPT and MITRA-FR, those enrolled in RESHAPE-HF2 were less likely to have mitral regurgitation grade 4+ and, on average, HAD lower EROA, and plasma NT-proBNP and higher estimated glomerular filtration rate, but otherwise had similar age, comorbidities, CRT therapy and LVEF. CONCLUSION: Patients enrolled in RESHAPE-HF2 represent a third distinct population where MitraClip was tested in, that is one mainly comprising of patients with moderate-to-severe FMR instead of only severe FMR, as enrolled in the COAPT and MITRA-FR trials. The results of RESHAPE-HF2 will provide crucial insights regarding broader application of the transcatheter edge-to-edge repair procedure in clinical practice.

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. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the glocal (aggregated local attributions over multiple samples) and global (concept based XAI) perspectives. We have established a set of sanity checks to identify saliency as the most sensible attribution method. We provide a dataset-wide analysis across entire patient subgroups, which goes beyond anecdotal evidence, to establish the first quantitative evidence for the alignment of model behavior with cardiologists' decision rules. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.

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). Patients were grouped by tertile of echocardiographic LVmass/height2 (women, < 50, 50-61, > 61 g/m2; men, < 60, 60-74, > 74 g/m2) and by presence of wasting syndrome with unintentional weight loss (BMI < 24 kg/m2 and weight loss ≥ 5% in the prior 12 months). Repeat echocardiograms were obtained usually within 3-6 months for 85 patients. RESULTS: Patients in the lowest LVmass/height2 tertile had higher plasma GH (median (IQR) for 1st, 2nd, and 3rd tertile women, 1.8 (0.9-4.2), 0.8 (0.2-2.2), 0.5 (0.3-1.6) ng/mL, p = 0.029; men, 2.1 (0.8-3.2), 0.6 (0.1-1.7), 0.7 (0.2-1.9) ng/mL, p = 0.003). Among women, lower LVmass was associated with higher plasma IGF-I (68 (48-116), 72 (48-95), 49 (35-76) ng/mL, p = 0.007), whereas such association did not exist for men. Patients with lower LVmass had lower log IGF-I/GH ratio (women, 1.60 ± 0.09, 2.02 ± 0.09, 1.88 ± 0.09, p = 0.004; men, 1.64 ± 0.09, 2.14 ± 0.11, 2.04 ± 0.11, p = 0.002). GHBP was not associated with LVmass. Patients with wasting syndrome with unintentional weight loss had higher plasma GH and GHBP, lower log IGF-I/GH ratio, and similar IGF-I. Overall, GHBP correlated inversely with log IGF-I/GH ratio (women, r = - 0.591, p < 0.001; men, r = - 0.575, p < 0.001). Additionally, higher baseline IGF-I was associated with a decline in LVmass during follow-up (r = - 0.318, p = 0.003). CONCLUSION: In advanced cancer, reduced LVmass is associated with increased plasma GH and reduced IGF-I/GH ratio, suggesting increasing GH resistance, especially for patients with wasting syndrome with unintentional weight loss. Higher baseline IGF-I was associated with a decrease in relative LVmass during follow-up.

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. However, these features, while informative, may potentially obscure underlying data relationships. In this paper we present the following contributions: (1) We employ a deep-learning model and a tree-based model to analyze ECG data from a robust dataset of healthy individuals across varying ages in both raw signals and ECG feature format. (2) We use explainable AI methods to identify the most discriminative ECG features across age groups.(3) Our analysis with tree-based classifiers reveals age-related declines in inferred breathing rates and identifies notably high SDANN values as indicative of elderly individuals, distinguishing them from younger adults. (4) Furthermore, the deep-learning model underscores the pivotal role of the P-wave in age predictions across all age groups, suggesting potential changes in the distribution of different P-wave types with age. These findings shed new light on age-related ECG changes, offering insights that transcend traditional feature-based approaches.

[Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?] / Durch künstliche Intelligenz verstärkte Elektrokardiographie : Wird sie Diagnostik und Management unserer Patienten revolutionieren?

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. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.

Eficácia das drogas antiarrítmicas em pacientes com doença ventricular direita arritmogênica / Efficacy of antiarrhythic drugs in patients witharrhythmogenic right ventricular disease: results in patients with inducible and noninducible ventricular tachycardia

As taquiarritmias ventriculares säo as maiores manifestaçöes da doença ventricular direita arritmogênica. Embora a terapia antiarrítmica tenha sido largamente recomendada, há apenas informaçöes disponíveis limitadas sobre a eficácia de drogas antiarrítmicas. Métodos e resultados. A eficácia a curto e a longo prazo de vários agentes antiarrítmicos foi analisada retrospectivamente e prospectivamente em 81 pacientes (idade média de 39 ñ 14 anos; variando de 16 a 68 anos; 61,7 por cento do sexo masculino) com doença ventricular direita arritmogênica. Em 42 pacientes com taquicardia ventricular induzida, durante estimulaçäo ventricular programada, as seguintes taxas de eficácia foram obtidas: drogas classe la e lb (n = 18), 5,6 por cento; drogas classe lc (n = 25); 12 por cento; ß-bloqueaddores (n = 8), 0 por cento; sotalol (n = 38), 68,4 por cento; amiodarona (n = 13), 15,4 por cento; verapamil (n = 5), 0 por cento; e combinaçöes de drogas ( n = 26), 15,4 por cento. Apenas um dos 10 pacientes que näo responderam ao tratamento com sotalol, foi tratado de maneira eficaz com amiodarona, enquanto que os outros nove pacientes mostraram-se refratários a todas as outras drogas testadas (3,8 ñ 2,3 drogas, incluindo amiodarona em cinco casos) e receberam tratamento näo farmacológico. Durante um acompanhamento de 34 ñ 25 meses, três dos 31 pacientes (9,7 por cento) que receberam alta em uso de terapia farmacológica, tiveram recorrência de taquicardia ventricular näo fatal, após 0,5, 51 e 63 meses, respectivamente. Em 39 pacientes com taquicardia ventricular näo induzida durante estimulaçäo ventricular programada, as seguintes taxas de eficácia foram observadas: drogas classe la e lb (n = 16), 10 por cento; agentes classe lc (n = 23), 17,4 por cento; ß-bloqueadores (n = 7), 28,6 por cento; sotalol (n = 35), 82,8 por cento; amiodarona (n = 4), 25 por cento; verapamil (n = 24), 50 por cento; e combinaçöes de drogas (n = 11), 9,1 por cento. Durante um acompanhamento de 14 ñ 13 meses, quatro dos 33 pacientes (12,1 por cento) que tiveram alta sob uso de drogas antiarrítmicas, tiveram recorrência de episódios näo fatais de suas arritmias ventriculares...