Citations

Abstract Background Patient age can impact the safety and efficacy of ulcerative colitis (UC) treatments. Etrasimod is an oral, once daily (QD), selective sphingosine 1-phosphate1,4,5 receptor modulator for the treatment of moderately to severely active UC. Here, we evaluate the impact of age on etrasimod safety and efficacy in patients with UC in the phase 3 ELEVATE UC clinical program. Methods Data were pooled from ELEVATE UC 52 and ELEVATE UC 12 in patients receiving etrasimod 2 mg QD or placebo. Proportions and incidence rates (IRs) per 100 patient-years of treatment-emergent adverse events (AEs) were stratified by age (<40, 40-59, and ≥60 years). With the same age stratifications, efficacy was evaluated in patients with baseline Modified Mayo scores of 5-9 and 4-9 for the primary efficacy endpoint (clinical remission) and secondary efficacy endpoints. Results Overall, 787 patients were enrolled (<40 years, n = 420 [53.4%]; 40-59 years, n = 276 [35.1%]; and ≥60 years, n = 91 [11.6%]). Arthralgia, fatigue, and hypertension IRs were higher in older patients, irrespective of treatment. Serious AEs and AEs leading to treatment discontinuation were low and consistent across age groups. Significantly more patients receiving etrasimod 2 mg QD vs placebo achieved efficacy endpoints, regardless of age. Conclusions The safety profile of etrasimod 2 mg QD in the ELEVATE UC population was consistent across age groups, with no change in the incidence of AEs. Patients receiving etrasimod vs placebo showed significant clinical benefit, regardless of age. ClinicalTrials.gov NCT03945188; NCT03996369.

Abstract Advancements in machine learning and computing methods have given new life and great excitement to one of the most essential diagnostic tools to date—the electrocardiogram (ECG). The application of artificial intelligence‐enabled ECG (AI‐ECG) has resulted in the ability to identify electrocardiographic signatures of conventional and unique variables and pathologies, giving way to tremendous clinical potential. However, what these AI‐ECG models are detecting that the human eye is missing remains unclear. In this article, we highlight some of the recent developments in the field and their potential clinical implications, while also attempting to shed light on the physiologic and pathophysiologic features that enable these models to have such high diagnostic yield. © 2022 American Physiological Society. Compr Physiol 12:3417‐3424, 2022.

Abstract The pharmacological provocation test is a pivotal tool in cardiac electrophysiology for the diagnosis of potential causes of sudden cardiac death, sudden cardiac arrest (SCA), arrhythmias, symptoms, or ECG abnormalities. The 2022 European Society of Cardiology Guidelines for the Treatment of Ventricular Arrhythmias and Prevention of Sudden Cardiac Death offered guidance on provocation testing but did not describe the indications and requirements in depth. This clinical consensus statement, led by the European Heart Rhythm Association and approved by major international stakeholders, aims to advise the general cardiologist and the arrhythmia expert who to test and when, where, and how to do it. The statement focuses on current practice for the diagnosis of subclinical arrhythmia syndromes and the causes of SCA, building upon the recommendations of the Guidelines. We address the sodium channel blocker provocation test for patients suspected of Brugada syndrome as well as the use of epinephrine, isoproterenol, adenosine, ergonovine, and acetylcholine.

Abstract Aims Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Methods and results An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusion The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.

Abstract Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene–environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10−8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.