Disagreements in Medical Ethics Question Answering Between Large Language Models and Physicians.

Importance: Medical ethics is inherently complex, shaped by a broad spectrum of opinions, experiences, and cultural perspectives. The integration of large language models (LLMs) in healthcare is new and requires an understanding of their consistent adherence to ethical standards.

Objective: To compare the agreement rates in answering questions based on ethically ambiguous situations between three frontier LLMs (GPT-4, Gemini-pro-1.

A strategy for cost-effective large language model use at health system-scale.

Large language models (LLMs) can optimize clinical workflows; however, the economic and computational challenges of their utilization at the health system scale are underexplored. We evaluated how concatenating queries with multiple clinical notes and tasks simultaneously affects model performance under increasing computational loads. We assessed ten LLMs of different capacities and sizes utilizing real-world patient data.

Short-term safety and feasibility of a practical approach to combined atrial and ventricular physiological pacing: An initial single-center experience.

Background: Traditional right atrial appendage (RAA) pacing accentuates conduction disturbances as opposed to Bachmann bundle pacing (BBP).

Objective: The purpose of this study was to evaluate the feasibility, efficacy, and safety of routine anatomically guided high right atrial septal (HRAS) pacing with activation of Bachmann bundle combined with routine left bundle branch area pacing (LBBAP).

Methods: This retrospective single-center study included 96 consecutive patients who underwent 1 of 2 strategies: physiological pacing (PP) (n = 32) with HRAS and LBBAP leads and conventional pacing (CP) (n = 64) with traditional RAA and right ventricular apical leads.

Quantitative Prediction of Right Ventricular Size and Function From the ECG.

Background: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep learning-enabled ECG analysis for estimation of right ventricular (RV) size or function is unexplored.

Methods And Results: We trained a deep learning-ECG model to predict RV dilation (RVEDV >120 mL/m), RV dysfunction (RVEF ≤40%), and numerical RVEDV and RVEF from a 12-lead ECG paired with reference-standard cardiac magnetic resonance imaging volumetric measurements in UK Biobank (UKBB; n=42 938).

A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes.

Background: Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients with PVCs from a 12-lead electrocardiogram (ECG).

Objectives: This study aims to assess a deep-learning model to predict cardiomyopathy among patients with PVCs.

A foundational vision transformer improves diagnostic performance for electrocardiograms.

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches for biomedical problems may result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create a vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis.

Safely Shaping the Breast After Implant Removal and Total Intact Capsulectomy Using the Mammary Imbrication Lift and Fixation Technique.

Background: Implant-based breast augmentation is one of the most popular plastic surgery procedures performed worldwide. As the number of patients who have breast implants continues to rise, so does the number of those who request breast implant removal without replacement. There is little in the current scientific literature describing total intact capsulectomy and simultaneous mastopexy procedures.

Quantitative prediction of right ventricular and size and function from the electrocardiogram.

Background: Right ventricular ejection fraction (RVEF) and end-diastolic volume (RVEDV) are not readily assessed through traditional modalities. Deep-learning enabled 12-lead electrocardiogram analysis (DL-ECG) for estimation of RV size or function is unexplored.

Methods: We trained a DL-ECG model to predict RV dilation (RVEDV>120 mL/m), RV dysfunction (RVEF≤40%), and numerical RVEDV/RVEF from 12-lead ECG paired with reference-standard cardiac MRI (cMRI) volumetric measurements in UK biobank (UKBB; n=42,938).

Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction.

Background: Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making.

Methods: In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients.

Cardiovascular Complications of Interatrial Conduction Block: JACC State-of-the-Art Review.

Interatrial block (IAB) is an electrocardiographic pattern describing the conduction delay between the right and left atria. IAB is classified into 3 degrees of block that correspond to decreasing conduction in the region of Bachmann's bundle. Although initially considered benign in nature, specific subsets of IAB have been associated with atrial arrhythmias, elevated thromboembolic stroke risk, cognitive impairment, and mortality.

Prognostic Value of Electrocardiographic QRS Diminution in Patients Hospitalized With COVID-19 or Influenza.

During the clinical care of hospitalized patients with COVID-19, diminished QRS amplitude on the surface electrocardiogram (ECG) was observed to precede clinical decompensation, culminating in death. This prompted investigation into the prognostic utility and specificity of low QRS complex amplitude (LoQRS) in COVID-19. We retrospectively analyzed consecutive adults admitted to a telemetry service with SARS-CoV-2 (n = 140) or influenza (n = 281) infection with a final disposition-death or discharge.

Cardiogenic Shock and Mitral Valve Chord Rupture: A Rare Presentation of Libman-Sacks Endocarditis.

Distinguishing Libman-Sacks endocarditis from other valvular heart disease etiologies has important implications for management. We present a case of a 23-year-old man who presented in extremis with fever and cardiogenic shock caused by Libman-Sacks endocarditis with associated mitral valve chord rupture. ().

Syncope and presyncope in patients with COVID-19.

Introduction: Recent studies have described several cardiovascular manifestations of COVID-19 including myocardial ischemia, myocarditis, thromboembolism, and malignant arrhythmias. However, to our knowledge, syncope in COVID-19 patients has not been systematically evaluated. We sought to characterize syncope and/or presyncope in COVID-19.

Urologic Complications in Patients Receiving Indwelling Urinary Catheters During Transcatheter Aortic Valve Replacement.

Objectives: The minimalist approach to transcatheter aortic valve replacement (TAVR) focuses on avoiding extraneous invasive measures. Data describing the clinical impact of routine indwelling urinary catheter (IUC) in TAVR patients is limited. We sought to examine outcomes after IUC placement in patients undergoing TAVR.