Future cardiology最新文献

Coronary artery disease (CAD) remains one of the leading causes of death worldwide, making precise risk stratification essential for personalized medicine. Hyperspectral imaging (HSI) has emerged as a novel noninvasive medical imaging technology with significant potential in CAD detection and risk stratification. Previous reviews focusing on machine learning (ML) in CAD have predominantly centered on traditional imaging modalities or broadly explored the applications of ML in CAD. By exclusively focusing on the synergistic potential of HSI and ML, this review aims to advance CAD diagnosis and risk stratification. It systematically summarizes the current application, core technical advantages, and unresolved challenges of HSI in CAD diagnosis. By integrating HSI with ML, we propose a novel noninvasive diagnostic framework capable of extracting CAD-related biomarkers from superficial body features. Although challenges remain in data standardization, model generalizability, and clinical translation, this integrated approach holds great promise for advancing precision cardiology.

As renal handling of chloride (Cl) and bicarbonate (HCO₃) under different cardiac states would affect the acid-base balance and subsequently influence respiratory function, cardio-renal-pulmonary interactions can be predicted in heart failure (HF) pathophysiology, but have not yet been confirmed. We report a 93-year-old male HF patient taking diuretics who presented with mild body fluid retention, elevated b-type natriuretic peptide (BNP), and hypercapnia during a hospital stay. To improve the hypercapnia and correct metabolic alkalosis, he was given oral acetazolamide (250 mg/day). Peripheral venous blood tests before and after 1 month on acetazolamide showed that his body weight (39.8 to 36.8 kg) and BNP level (348 to 158 pg/mL) decreased. Arterial blood gas analysis showed that his blood pH (7.41 to 7.37) and PaO₂ (91 to 95 mmHg) increased, and PaCO₂ (59.7 to 37.8 mmHg), HCO₃ (36.7 to 21.3 mmol/L) and base excess (11.5 to -3.1 mmol/L) decreased. These observations indicated that acetazolamide treatment reduced the cardiac burden and enhanced pulmonary ventilation possibly by stimulating the brain's respiratory center. This case highlights a new clinical HF entity of pathophysiologic cardio-renal pulmonary syndrome as a pathologic spectrum defined by cardio-pulmonary interactions linked to kidney function through renal modulation of the acid-base balance.

Transcatheter aortic valve replacement has rapidly expanded from high-risk populations to younger patients with aortic stenosis. This shift raises important questions about valve durability, reintervention strategies, and long-term outcomes compared with surgical aortic valve replacement. Younger patients often present with unique anatomical challenges, including bicuspid aortic valves, and are expected to outlive their first valve prosthesis, making lifetime management a central concern. While new valve technologies show promise, long-term data remain limited. Careful patient selection, shared decision-making, and ongoing prospective studies are essential to guide the role of TAVR in this population.

Background: Many patients remain functionally limited after transcatheter aortic valve replacement (TAVR) despite successful correction of aortic stenosis. Exercise-based cardiac rehabilitation (EBCR) is effective in other cardiac populations, but its benefits after TAVR remain uncertain. This study evaluated the impact of EBCR on functional capacity, cardiac function, quality of life, and safety outcomes in post-TAVR patients.

Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs) published through February 2025 was conducted using major databases. Outcomes were pooled using mean differences or risk ratios with 95% confidence intervals.

Results: Six RCTs with 272 patients were included. No significant difference was found between EBCR and usual care for peak VO₂ change (MD: 1.46, 95% CI: [-0.16 to 3.08], p = 0.076) and six-minute walk distance (6MWD) change (MD: 18.72, 95% CI: [-2.24 to 39.68], p = 0.08). Similarly, no significant difference was observed between EBCR and usual care for left ventricular ejection fraction (LVEF) change (MD: 1.31, 95% CI: [-2.06 to 4.69], p = 0.45), and aortic valve orifice area change (AVOA) (MD: -0.03, 95% CI: [-0.24 to 0.18], p = 0.78).

Conclusion: EBCR did not significantly improve outcomes after TAVR; however, near-significant trends in functional capacity warrant further large-scale investigation.

Protocol registration: PROSPERO ID CRD420250652719.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and its prevalence expected to double by 2060. This is due to aging populations and increasing rates of obesity, hypertension, and diabetes. It is often asymptomatic and can go undetected which will intensify the risk of serious complications such as ischemic stroke, heart failure, and cognitive decline. Early detection through opportunistic and systematic screening is essential for anticoagulation therapy, which can reduce stroke risk by 65%. AF arises from disorganized atrial electrical activity that is frequently originating in the pulmonary veins, and is sustained by a complex interplay of electrical, structural, and autonomic factors. Lifestyle and dietary habits play a significant role in AF risk and progression with obesity, and alcohol consumption linked to increased risk, while heart-healthy diets and weight control can alleviate symptoms and reduce recurrence. Additionally, genetic predisposition contributes to AF, particularly in early-onset cases. Genome-wide studies have identified over 100 associated loci, highlighting the value of personalized approaches in diagnosis and treatment. This paper explores the epidemiology, pathophysiology, and modifiable risk factors of AF with a dedicated focus on the precision medicine approach by highlighting personalized strategies for early detection and targeted management despite current clinical challenges.

Transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) is a progressive, often fatal disease. TTR stabilizers bind directly to TTR, inhibiting tetramer dissociation and the resulting amyloidogenic process. This comprehensive review synthesizes clinical outcomes data from the ATTRibute-CM study program, including primary analyses, prespecified sensitivity studies, and open-label extension (OLE) follow-up, to characterize the clinical profile of acoramidis, an oral TTR stabilizer approved for ATTR-CM treatment. In clinical trials, acoramidis demonstrated consistent clinical benefits, with statistically significant reductions in the composite of all-cause mortality or first cardiovascular-related hospitalization evident within 3 months and sustained through 30 months. Prespecified analyses confirmed treatment robustness. Efficacy was maintained regardless of the N-terminal pro-B-type natriuretic peptide (NT-proBNP) thresholds (≥500 pg/mL, ≥750 pg/mL, and ≥1000 pg/mL), was unaffected by concomitant tafamidis use, and was similar in high-risk participants with stage 4 chronic kidney disease (CKD), who are typically excluded from clinical trials. OLE studies through 42 months showed sustained benefits with no new safety concerns. Results demonstrate robust clinical benefits of acoramidis across diverse ATTR-CM populations and across NYHA classes and NAC stages, independent of NT-proBNP thresholds, concomitant tafamidis use, or high-risk CKD. An ongoing prevention study in asymptomatic ATTR-CM gene-mutation carriers may further expand its therapeutic range for ATTR management.

Consumer wearables are increasingly used to document palpitations, but their algorithms are almost exclusively validated for atrial fibrillation (AF). We report a 70-year-old man with recurrent palpitations, no prior cardiovascular history, and controlled hypertension. A single-lead Apple Watch ECG classified sinus rhythm at 75 bpm, while a same-day 12-lead ECG revealed typical atrial flutter with sawtooth waves and regular atrioventricular conduction. After adequate anticoagulation, the patient underwent successful electrical cardioversion with 120 J and remains in stable sinus rhythm. This case highlights that AF-validated smartwatch algorithms may miss other supraventricular arrhythmias, particularly with regular ventricular response. Smartwatches can aid AF screening and symptom capture, but persistent symptoms require confirmation with standard 12-lead ECG. Future work should prioritize algorithm refinement and rigorous, post-market validation beyond AF to ensure that consumer devices transition from wellness tools to clinically reliable instruments for arrhythmia management.

Background: Echocardiography after myocardial infarction (MI) provides clinically useful information through assessment of regional wall motion abnormalities, but interpretation requires expertise and remains subject to observer variability. Artificial intelligence (AI) shows promise in automatic interpretation, but it is unclear how explainability affects human-AI collaborative performance.

Methods: A ResNet18-LSTM model was trained to classify normal vs MI on 127 apical four chamber (A4C) and 120 apical two chamber (A2C) echocardiogram videos from the HMC-QU dataset. Gradient-weighted Class Activation Mapping (Grad-CAM provided visual explanations. Eight cardiology trainees compared diagnostic performance across three conditions: (a) echo clips alone, (b) echo clips with AI predictions, and (c) echo clips with AI predictions plus Grad-CAM explanations.

Results: The AI models demonstrated strong discriminative performance with AUCs of 0.9429 (A2C) and 0.9250 (A4C). AI alone achieved 80.0% accuracy versus 77.0% for clinicians alone. Surprisingly, combining AI with human judgment did not improve performance, and introducing visual explanations reduced accuracy to 72% and specificity from 93.8% to 83.8% (p = 0.046).

Conclusion: While AI models can effectively detect MI on echocardiographic videos, current explainability techniques may misalign with clinical reasoning, potentially impairing diagnostic performance. Future integration requires AI visual explanation strategies that complement clinician expertise.

In this review of right ventricular (RV) preload, we discuss RV physiology, assessment of RV preload, and optimization of RV preload in the setting of acute RV failure. Early recognition and ongoing reassessment of invasive or noninvasive hemodynamics are critical to the management of acute RV failure. Central venous pressure (CVP) estimates RV preload but should not be the sole parameter guiding management. A comprehensive approach that integrates multiple hemodynamic indices should be considered to guide the resuscitative process in acute RV failure.