Daily Cardiology Research Analysis

AIM: The "2025 AHA/ACC/AANP/AAPA/ABC/ACCP/ACPM/AGS/AMA/ASPC/NMA/PCNA/SGIM Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults" retires and replaces the "2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults." METHODS: A comprehensive literature search was conducted from December 2023 to June 2024 to identify clinical studies, reviews, and other evidence performed on human subjects that were published since February 2015 in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. STRUCTURE: The focus of this clinical practice guideline is to create a living, working document updating current knowledge in the field of high blood pressure aimed at all practicing primary care and specialty clinicians who manage patients with hypertension.

OBJECTIVES: To investigate the role of right ventricular (RV) maladaptive response to increased afterload in the early diagnosis of heart failure with preserved ejection fraction (HFpEF) using cardiac magnetic resonance (CMR) and invasive cardiopulmonary exercise testing (iCPET). This study evaluates biventricular function and its association with exercise performance in HFpEF. MATERIALS AND METHODS: We prospectively recruited 145 patients with suspected HFpEF from two centers, of whom 113 underwent echocardiography, iCPET, and CMR. Patients met the 2016 European Society of Cardiology HFpEF criteria, with iCPET confirming HFpEF as a pulmonary capillary wedge pressure (PCWP) > 15 mmHg at rest and > 25 mmHg at peak exercise. The diagnostic performance of CMR parameters was assessed using the area under the curve (AUC). RESULTS: Among the 113 patients, 72 had HFpEF (68 ± 10 years) and 41 were non-HFpEF (66 ± 11 years). HFpEF patients exhibited significantly reduced resting pulmonary artery compliance. CMR-derived RV longitudinal strain and left atrial (LA) reservoir strain had the highest diagnostic accuracy for HFpEF (AUC 0.805 and 0.776, respectively). A sex disparity was observed in the LA reservoir strain's diagnostic performance, with higher accuracy in males (AUC 0.801) compared to females (AUC 0.559). Additionally, impaired LA reservoir and booster strains, correlated with reduced exercise capacity and increased PCWP during exercise, highlighting their clinical relevance. CONCLUSIONS: RV systolic dysfunction and impaired LA strain serve as early HFpEF markers. The more pronounced LA dysfunction in males suggests potential sex-specific differences, underscoring the need to integrate RV and LA strain assessment into HFpEF diagnostics and personalized treatment approaches. KEY POINTS: Question Can cardiac MRI (CMR)-derived RV strain and LA reservoir strain improve the early diagnosis of HFpEF in symptomatic patients with preserved ejection fraction? Findings CMR-derived RV longitudinal strain and LA reservoir strain effectively differentiate early HFpEF; diagnostic accuracy of LA strain varies significantly by sex. Clinical relevance CMR-based RV and LA strain measurements enhance early HFpEF detection, with higher diagnostic accuracy in males, supporting sex-specific diagnostic strategies for timely and personalized heart failure care.

BACKGROUND: Acute myocardial infarction (MI) alters cardiomyocyte geometry and architecture, leading to changes in the acoustic properties of the myocardium. OBJECTIVES: This study examines ultrasomics-a novel cardiac ultrasound-based radiomics technique to extract high-throughput pixel-level information from images-for identifying ultrasonic texture and morphologic changes associated with infarcted myocardium. METHODS: The authors included 684 participants from multisource data: a) a retrospective single-center matched case-control dataset; b) a prospective multicenter matched clinical trial dataset; and c) an open-source international and multivendor dataset. Handcrafted and deep transfer learning-based ultrasomics features from 2- and 4-chamber echocardiographic views were used to train machine learning (ML) models with the use of leave-one-source-out cross-validation for external validation. RESULTS: The ML model showed a higher AUC than transfer learning-based deep features in identifying MI (AUC: 0.87 [95% CI: 0.84-0.89] vs AUC: 0.74 [95% CI: 0.70-0.77]; P < 0.0001). ML probability was an independent predictor of MI even after adjusting for conventional echocardiographic parameters (adjusted OR: 1.03 [95% CI: 1.01-1.05]; P < 0.0001). ML probability showed diagnostic value in differentiating acute MI, even in the presence of myocardial dysfunction (averaged longitudinal strain [LS] <16%) (AUC: 0.84 [95% CI: 0.77-0.89]). In addition, combining averaged LS with ML probability significantly improved predictive performance compared with LS alone (AUC: 0.86 [95% CI: 0.80-0.91] vs AUC: 0.80 [95% CI: 0.72-0.87]; P = 0.02). Visualization of ultrasomics features with the use of a Manhattan plot discriminated infarcted and noninfarcted segments (P < 0.001) and facilitated parametric visualization of infarcted myocardium. CONCLUSIONS: This study demonstrates the potential of cardiac ultrasomics to distinguish healthy from infarcted myocardium and highlights the need for validation in diverse populations to define its role and incremental value in myocardial tissue characterization beyond conventional echocardiography.