Journal of Cardiovascular Development and Disease最新文献

Cardiogenic pulmonary edema (CPE) is a life-threatening manifestation of acute heart failure characterized by rapid accumulation of fluid in the interstitial and alveolar spaces, leading to severe dyspnea, hypoxemia, and respiratory failure. The condition arises from elevated left-sided filling pressures that increase pulmonary capillary hydrostatic pressure, disrupt alveolo-capillary barrier integrity, and impair gas exchange. Neurohormonal activation further perpetuates congestion and increases myocardial workload, creating a vicious cycle of hemodynamic overload and respiratory compromise. Respiratory support is a cornerstone of management in CPE, aimed at stabilizing oxygenation, reducing the work of breathing, and facilitating ventricular unloading while definitive therapies, such as diuretics, vasodilators, inotropes, or mechanical circulatory support (MCS), address the underlying cause. Among available modalities, non-invasive ventilation (NIV) with continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) has the strongest evidence base in moderate-to-severe CPE, consistently reducing the need for intubation and providing rapid relief of dyspnea. High-flow nasal cannula (HFNC) represents an emerging alternative in patients with moderate hypoxemia or intolerance to mask ventilation, and should be considered an adjunctive option in selected patients with less severe disease or NIV intolerance, although its efficacy in severe presentations remains uncertain. Invasive mechanical ventilation is reserved for refractory cases, while extracorporeal membrane oxygenation (ECMO) and other advanced circulatory support modalities may be necessary in cardiogenic shock. Integration of respiratory strategies with hemodynamic optimization is essential, as positive pressure ventilation favorably modulates preload and afterload, synergizing with pharmacological unloading. Future directions include personalization of ventilatory strategies using advanced monitoring, novel interfaces to improve tolerability, and earlier integration of MCS. In summary, respiratory support in CPE is both a bridge and a decisive therapeutic intervention, interrupting the cycle of hypoxemia and hemodynamic deterioration. A multidisciplinary, individualized approach remains central to improving outcomes in this high-risk population.

Understanding thrombosis in acute coronary syndromes (ACSs) has evolved through advances in biomarkers, intracoronary imaging, and emerging analytical tools, improving diagnostic accuracy and risk stratification in high-risk patients. This narrative review provides an integrative overview of contemporary evidence from clinical trials, meta-analyses, and international guidelines addressing circulating biomarkers, intracoronary imaging modalities-including optical coherence tomography (OCT), intravascular ultrasound (IVUS), and near-infrared spectroscopy (NIRS)-artificial intelligence-based analytical approaches, and emerging antithrombotic therapies. High-sensitivity cardiac troponins and natriuretic peptides remain the most robust and guideline-supported biomarkers for diagnosis and prognostic assessment in ACS, whereas inflammatory markers and multimarker strategies offer incremental prognostic information but lack definitive validation for routine therapeutic guidance. Intracoronary imaging with IVUS or OCT is supported by current guidelines to guide percutaneous coronary intervention in selected patients with ACS and complex coronary lesions, leading to improved procedural optimization and clinical outcomes compared with angiography-guided strategies. Beyond procedural guidance, OCT enables detailed plaque characterization and mechanistic insights into ACS, while NIRS provides complementary information on lipid-rich plaque burden, primarily for risk stratification based on observational evidence. Artificial intelligence represents a rapidly evolving tool for integrating clinical, laboratory, and imaging data, with promising results in retrospective and observational studies; however, its clinical application in thrombosis management remains investigational due to the lack of outcome-driven randomized trials. In the therapeutic domain, factor XI inhibitors have demonstrated favorable safety profiles with reduced bleeding and preserved antithrombotic efficacy in phase II and early phase III studies, but their definitive role in ACS management awaits confirmation in large, outcome-driven randomized trials. Overall, the integration of biomarkers, intracoronary imaging, and emerging analytical and pharmacological strategies highlights the potential for more individualized cardiovascular care. Nevertheless, careful interpretation of existing evidence, rigorous validation, and alignment with guideline-directed practice remain essential before widespread clinical adoption.

Background: There exists some inconsistent evidence on the relationship between altered cardiac morphology, its function, and frailty. Therefore, this study aimed to assess the associations among frailty, lean body mass, central arterial stiffness, and cardiac structure and geometry in older people with a normal ejection fraction. Methods: A total of 205 patients >65 years were enrolled into this ancillary analysis of the FRAPICA study and were assessed for frailty with the Fried phenotype scale. Left ventricular dimensions and geometry were assessed with two-dimensional echocardiography. Fat-free mass was measured using three-site skinfold method. Parametric and non-parametric statistics and analysis of covariance were used for statistical calculations. Results: Frail patients were older and women comprised the majority of the frail group. Frail men and women had comparable weight, height, fat-free mass, blood pressure, central blood pressure, and carotid-femoral pulse wave velocity to their non-frail counterparts. There was a linear correlation between the sum of frailty criteria and left ventricular end-diastolic diameter (Spearman R = -0.17; p < 0.05) and relative wall thickness (Spearman R = 0.23; p < 0.05). In the analysis of covariance, frailty and gender were independently associated with left ventricular mass (gender: β of -0.37 and 95% CI of -0.50--0.24 at p < 0.001), the left ventricular mass index (gender: β of -0.23 and 95% CI of -0.37--0.09 at p < 0.001), and relative wall thickness (frailty: β of -0.15 and 95% CI of -0.29--0.01 at p < 0.05; gender: β of 0.23 and 95% CI of 0.09-0.36 at p < 0.01). Frailty was associated with a shift in heart remodeling toward concentric remodeling/hypertrophy. Conclusions: Frailty is independently associated with thickening of the left ventricular walls and a diminished left ventricular end-diastolic diameter, which are features of concentric remodeling or hypertrophy. This association appears to be more pronounced in women. Such adverse cardiac remodeling may represent another phenotypic feature linked to frailty according to the phenotype frailty criteria.

Objectives: Non-ST-segment elevation myocardial infarction (NSTEMI) in the elderly is frequently complicated by multiple comorbidities, which influence clinical outcomes. However, the prognostic significance of atrial fibrillation (AF) in this context remains uncertain. This study aimed to evaluate the impact of AF on short- and long-term mortality in elderly patients (≥65 years) with NSTEMI.

Methods: This cross-sectional observational study included 474 NSTEMI patients aged 65 years and older. Participants were stratified into four groups based on age (65-74 vs. ≥75 years) and the presence or absence of AF. One-month and one-year all-cause mortality were assessed as the primary and secondary endpoints, respectively.

Results: AF was detected in 23 (11.6%) of 199 patients aged 65-74 and in 80 (29.1%) of 275 patients aged ≥75. While one-month mortality did not differ significantly among the four groups (p = 0.514), one-year mortality showed a statistically significant difference (p < 0.001). Univariate analysis revealed that AF was not predictive of one-month mortality. In multivariate Cox regression analysis, AF, reduced creatinine clearance, and left ventricular ejection fraction <50% were identified as independent predictors of one-year mortality.

Conclusion: AF is not associated with short-term mortality in elderly NSTEMI patients; however, it serves as an independent predictor of one-year mortality. These findings highlight the importance of long-term rhythm monitoring and management in this high-risk population.

The pharmacologic management of patent ductus arteriosus (PDA) presents a challenge to clinicians due to the interindividual variability in drug response to available medications. There is evidence that CYP2C9 is associated with the response to PDA treatment; however, no data from the Middle East is available. This study aimed to investigate the association between CYP2C8 and CYP2C9 genetic polymorphisms and response to ibuprofen or indomethacin in neonates with PDA. We conducted a retrospective cohort study of neonates with a gestational age < 32 weeks and birthweight < 1500 g with PDA between 2019 and 2023. Eligible neonates were those diagnosed with PDA and treated with at least one course of ibuprofen or indomethacin. Genotyping was performed to identify four single-nucleotide polymorphisms (SNPs), namely CYP2C8*3 rs10509681, CYP2C9*2 rs1799853, CYP2C9 rs2153628, and CYP2C9*3 rs1057910. Allele frequencies were compared between responders and non-responders, and non-genetic predictors were assessed using logistic regression. A total of 146 infants were identified. Of these, 86 were enrolled. Genetic analysis showed that the heterozygote genotype (TC) for the CYP2C8 gene was the most common (45%), while wild-type alleles were predominant for CYP2C9 variants. No significant differences in allele frequencies were found between responders and non-responders to the treatment (p > 0.05). In a secondary analysis, the need for multiple surfactant doses independently predicted poor response (aOR 0.244, 95% CI 0.086-0.693, p = 0.008), while extremely low birth weight showed a borderline association (aOR 0.281, 95% CI 0.062-1.268, p = 0.099). Carriers of CYP2C8*3 rs10509681, CYP2C9*2 rs1799853, CYP2C9 rs2153628, and CYP2C9*3 rs1057910 were not associated with variations in response to NSAIDs.

We appreciate the careful and critical reading of our work by Eltawil et al [...].

Low QRS voltage relative to left ventricle (LV) thickness is one of the red flag characteristics in the diagnosis of cardiac amyloidosis, and it can be measured by specific indexes. Few studies have clearly defined the diagnostic threshold of voltage indexes for light chain amyloidosis cardiomyopathy (AL-CA) patients and other patients with LV hypertrophy. This case-control study analyzed electrocardiograms and echocardiograms of patients with AL-CA, hypertrophic cardiomyopathy (HCM), and hypertension left ventricular hypertrophy (HTN-LVH) seen at a single university center from 2008 to 2022. Low QRS voltage and three different precordial voltage indexes were evaluated. Diagnostic thresholds for rule-in and rule-out were calculated for AL-CA against each control group. A single voltage-mass ratio based on cross-sectional area (CSA) exhibited most accurate diagnostic accuracy, and the value of ≤1.72 aids the rule-in of AL-CA against other causes of left ventricular hypertrophy, providing a positive predictive value (PPV) of 86% versus HCM and 75% versus HTN-LVH.

Fontan patients experience anatomical remodeling over time, yet the mechanisms driving these changes remain unclear. This study aimed to characterize full-route Fontan remodeling and evaluate whether observed morphological changes arise from somatic growth alone or from the combined influence of conduit properties, surgical design, thoracic anatomy, and mechanical forces. Five Fontan patients (four extracardiac, one lateral tunnel) underwent analysis using two MRI-derived 3D models obtained between 1 and 4 years apart. Directional displacement was assessed using 3D shape overlays, surface geometry was quantified using the Koenderink Shape Index (KSI), and patient-specific growth mapping estimated localized tissue dynamics. Statistical analyses included a one-sample t-test for mean anterior displacement, the Grubbs' test for outlier detection, and the Wilcoxon signed-rank test for KSI comparisons across time points. All patients exhibited anterior displacement of the Fontan route, with a mean shift of 0.29″ ± 0.33″ and one significant outlier (lateral tunnel, 0.87″). Four of five patients showed increased convexity over time. Growth mapping revealed minimal, heterogeneous native-tissue expansion, with localized growth up to 0.2 mm/year. Individual remodeling trajectories varied and did not consistently align with localized anterior growth, indicating that Fontan route remodeling is highly individualized and cannot be explained by somatic growth alone. This retrospective longitudinal case series study highlights the value of quantitative 3D geometric tools for assessing subtle Fontan route remodeling and supports the feasibility of growth-aware, patient-specific modeling frameworks in single-ventricle physiology.

Machine learning is increasingly applied to cardiovascular disease prediction yet reported performance metrics often appear implausibly high due to methodological errors. Recent work has reported nearly perfect predictive accuracy (≈99%) using a k-Nearest Neighbors (kNN) model on CDC heart-disease data. Such performance greatly exceeds typical BRFSS-based benchmarks and strongly indicates data leakage. In this commentary, we replicate and re-analyze the original workflow, showing that the authors applied the SMOTE-ENN resampling method prior to the train/test split, thereby allowing synthetic data generated from the full dataset to contaminate the test set. Combined with an excessively small neighborhood parameter (k = 2), this produced misleadingly high accuracy. It is noted that (1) with SMOTE-ENN performed globally, synthetic samples appear nearly identical to test points, leading to near-perfect classification, and (2) this kNN choice is unusually small for a dataset of this scale and further amplifies leakage bias. Correcting the workflow by restricting oversampling to the training data or using undersampling restores realistic results, reducing predictive accuracy to approximately 80%, confirming the inflation caused by pre-split resampling and aligning with literature norms. This case underscores the critical importance of rigorous validation, transparent reporting, and leakage-free pipelines in medical AI. We outline practical guidelines for avoiding such pitfalls and ensuring reproducible, realistic, and clinically reliable machine-learning studies.