Upgrading Right Ventricular Pacing to Cardiac Resynchronization in HFrEF Patients Improves Symptoms and Functional Outcomes.

Background: In the BUDAPEST (Biventricular Upgrade on left ventricular reverse remodeling and clinical outcomes in patients with left ventricular Dysfunction and intermittent or permanent APical/SepTal right ventricular pacing)-CRT Upgrade randomized trial, the authors have demonstrated improved mortality and morbidity after cardiac resynchronization therapy (CRT) upgrade in patients with heart failure with reduced ejection fraction (HFrEF) with high right ventricular (RV) pacing burden.

Objectives: This substudy sought to examine the impact of CRT upgrade on symptoms, functional outcome, and exercise capacity.

Methods: In the BUDAPEST-CRT Upgrade trial, 360 HFrEF patients with pacemaker or implantable cardioverter-defibrillator (ICD) and ≥20% RV pacing burden were randomly assigned (3:2) to cardiac resynchronization therapy with defibrillator (CRT-D) upgrade (n = 215) or ICD (n = 145).

Ocular and Systemic Anomalies in an Infant With 4q Deletion and 3q Duplication: Case Report and Review of Literature.

This is a case report of a female infant with two rare pathogenic chromosomal abnormalities: partial trisomy of chromosome 3 (3q25.2 to 3q29) and partial monosomy of chromosome 4 (4q34.1 to 4q35.

Electronic Health Record Data Quality and Performance Assessments: Scoping Review.

Background: Electronic health records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality (DQ) and performance assessment.

Objective: This review aims to streamline the current best practices on EHR DQ and performance assessments as a replicable standard for researchers in the field.

Risk-Specific Training Cohorts to Address Class Imbalance in Surgical Risk Prediction.

Importance: Machine learning tools are increasingly deployed for risk prediction and clinical decision support in surgery. Class imbalance adversely impacts predictive performance, especially for low-incidence complications.

Objective: To evaluate risk-prediction model performance when trained on risk-specific cohorts.

Community-engaged artificial intelligence research: A scoping review.

The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation.