Clinical parameters of death and heart failure hospitalization in biventricular systolic dysfunction assessed via cardiac magnetic resonance.

Aims: While factors associated with adverse events are well elucidated in setting of isolated left ventricular dysfunction, clinical and imaging-based prognosticators of adverse outcomes are lacking in context of biventricular dysfunction. The purpose of this study was to establish role of clinical variables in prognosis of biventricular heart failure (HF), as assessed by cardiac magnetic resonance imaging.

Methods: Study cohort consisted of 840 patients enrolled in DERIVATE registry with coexisting CMR-derived right ventricular (RV) and left ventricular (LV) dysfunction, as defined by RV and LV ejection fractions ≤45 % and ≤ 50 %, respectively.

Optimizing Trauma Activation Criteria for a Rural Trauma Center.

Background: There is a need for activation criteria that reflect the different factors affecting rural trauma patients.

Objective: To develop effective activation criteria for a rural trauma center among adults, incorporating variables specific to the geography, mechanisms of injury, and population served.

Methods: This is a single-center, retrospective cohort study conducted from (23 years) January 1, 2000, to July 31, 2023.

Disentangling the detrimental effects of local from systemic adipose tissue dysfunction on articular cartilage in the knee.

Objective: Obesity increases osteoarthritis (OA) risk due to adipose tissue dysfunction with associated metabolic syndrome and excess weight. Lipodystrophy syndromes exhibit systemic metabolic and inflammatory abnormalities similar to obesity without biomechanical overloading. Here, we used lipodystrophy mouse models to investigate the effects of systemic versus intra-articular adipose tissue dysfunction on the knee.

Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.

Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFR) to computational fluid dynamics CT-derived FFR (FFR) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFR model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth.