興梠 貴英

BACKGROUND: Coronary artery disease (CAD) and aortic valve stenosis (AS) often coexist, with AS exacerbating myocardial ischemia and affecting prognosis. AIMS: To investigate the prognostic impact of AS stratified by peak aortic jet velocity (AV-Vel) in patients undergoing PCI. METHODS AND RESULTS: We conducted retrospective multicenter observational study involving patients who underwent percutaneous coronary intervention (PCI) between April 2013 and March 2019. The patients were divided into non-AS group and AS group. The AS group was further categorized: 2.6 ≤ AV-Vel < 3.0 m/s, mild AS; 3.0 ≤ AV-Vel < 4.0 m/s, moderate AS; and AV-Vel ≥ 4.0 m/s, severe AS. The primary outcome was all-cause mortality, and the secondary outcome was major adverse cardiovascular events (MACE), defined as a composite of all-cause mortality, myocardial infarction, or stroke. Multivariable Cox proportional hazards analysis was performed over 5-year observation period, with landmark analyses conducted at 30 days after PCI and from day 31 after PCI to 5 years. In total, 9,690 patients were analyzed (AS group, n = 361). Over a median follow-up of 2.57 (IQR: 0.89-4.24) years, AS group exhibited higher rates of mortality (HR: 3.06; 95% CI: 2.41-3.90; p < 0.001) and MACE (HR: 2.45; 95%CI: 1.97-3.04; p < 0.001) compared with non-AS group. Subgroup analysis revealed that patients with moderate and severe AS had worse short-term mortality and MACE within 30 days after PCI than the non-AS group, while patients with mild to severe AS showed significantly worse long-term outcomes than the non-AS group. CONCLUSIONS: AV-Vel is independently associated with both short- and long-term outcomes in patients undergoing PCI.

Coronary artery disease (CAD) is a major risk factor for the development of heart failure (HF) with preserved ejection fraction (HFpEF) and is associated with increased mortality. However, an optimal strategy to screen for HFpEF among patients with CAD has not yet been established. The HFpEF-ABA score was introduced to estimate the pretest probability of HFpEF and was shown to predict adverse HF events. This retrospective multicenter cohort study included patients registered in the Clinical Deep Data Accumulation System database who underwent percutaneous coronary intervention from April 2013 to March 2019. Patients with a left ventricular (LV) ejection fraction ≥ 50% and no known history of HF were included. Age, body mass index, and a history of atrial fibrillation were used to calculate the HFpEF-ABA score, and patients were dichotomized at a 50% threshold for descriptive risk stratification. The primary endpoint was a composite of all-cause death and unplanned HF hospitalization. Among 3307 patients, those with high HFpEF-ABA scores were more likely to have hypertension, renal dysfunction, anemia, larger left atrial size, higher LV mass index, and elevated brain natriuretic peptide levels, consistent with an HFpEF phenotype. Over a median follow-up of 721 days, 275 patients experienced the primary endpoint. The HFpEF-ABA score was independently associated with the primary endpoint as both a continuous and a categorical variable (hazard ratio: 1.07 [95% confidence interval: 1.00-1.14], P = 0.043, and 1.37 [1.07-1.76], P = 0.015, respectively). The HFpEF-ABA score identifies CAD patients with an HFpEF phenotype and predicts adverse outcomes.

AIMS: Hypoxia-inducible factor (HIF) signalling influences cardiomyocyte differentiation, maturation, and metabolic adaptation under pathological conditions. HIF-Prolyl hydroxylase domain (HIF-PH) inhibitors, which target this pathway, have been introduced for the treatment of renal anaemia. Their precise effect or safety on cardiac function remains unclear because their pharmacokinetics and distribution are not well-understood. This study aimed to examine HIF signalling activation in adult cardiomyocytes (CMs). METHODS AND RESULTS: We used tamoxifen (TAM)-inducible, CM-specific von Hippel-Lindau (VHL) knockout (VHL-MCM) mice to activate CM HIF signalling. Then we subjected the mice to normal ageing or high-fat diet (HFD) and L-NAME feeding, a murine model of heart failure with preserved ejection fraction (HFpEF). In normal ageing group, there was no difference in the echocardiographic parameters or tissue fibrosis between VHL-MCM and control mice. VHL-MCM mice exhibited significantly increased capillary density and higher expression levels of HIF-target genes (P = 0.0248, two-way ANOVA). Under HFD + L-NAME treatment, VHL-MCM mice showed transient but significantly preserved global longitudinal strain (GLS) at 12 weeks post-TAM injection compared to controls (P = 0.0284, two-way ANOVA). Sirius red staining indicated a trend towards reduced whole-heart and interstitial fibrosis with significant increase in capillary density in VHL-MCM mice. CONCLUSION: Sustained HIF signalling activation in adult CM does not impair the cardiac structure and function in normal ageing process and shows transient yet beneficial effect in murine HFpEF model.

Background Accurately predicting left ventricular ejection fraction (LVEF) recovery after percutaneous coronary intervention (PCI) in patients with chronic coronary syndrome (CCS) is crucial for clinical decision-making. Objective This study aimed to develop and compare multiple machine learning (ML) models to predict LVEF recovery and identify key contributing features. Methods We retrospectively analyzed 520 patients with CCS from the Clinical Deep Data Accumulation System database. Patients were categorized into 4 binary classification tasks based on baseline LVEF (≥50% or &lt;50%) and degree of recovery: (1) good recovery, defined as an LVEF increase of &gt;10% compared with ≤0%; and (2) normal recovery, defined as an LVEF increase of 0% to 10% compared with ≤0%. For each task, 3 feature selection strategies (all features, least absolute shrinkage and selection operator [LASSO] regression, and recursive feature elimination [RFE]) were combined with 4 ML algorithms (extreme gradient boosting [XGBoost], categorical boosting, light gradient boosting machine, and random forest), resulting in 48 models. Models were evaluated using 10-fold cross-validation and assessed by the area under the curve (AUC), decision curve analysis, and calibration plots. Results The highest AUCs were achieved by RFE combined with XGBoost (AUC=0.93) for preserved LVEF with good recovery, LASSO combined with XGBoost (AUC=0.79) for preserved LVEF with normal recovery, LASSO combined with XGBoost (AUC=0.88) for reduced LVEF with good recovery, and RFE combined with XGBoost (AUC=0.84) for reduced LVEF with normal recovery. Shapley Additive Explanation analysis identified uric acid, platelets, hematocrit, brain natriuretic peptide, glycated hemoglobin, glucose, creatinine, baseline LVEF, left ventricular end-diastolic internal diameter, heart rate, R wave amplitude in V5, and R wave amplitude in V6 as important predictive factors of LVEF recovery. Conclusions ML models incorporating feature selection strategies demonstrated strong predictive performance for LVEF recovery after PCI. These interpretable models may support clinical decision-making and can improve the management of patients with CCS after PCI.