興梠 貴英

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 <50%) and degree of recovery: (1) good recovery, defined as an LVEF increase of >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.

OBJECTIVES: The association between blood pressure (BP) and the mortality risk may vary depending on the comorbidities. This study was conducted to investigate the subgroup-specific correlation between systolic BP (SBP) and mortality in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI). METHODS: The Clinical Deep Data Accumulation System for PCI (CLIDAS-PCI), a nation-wide multicenter database with seven tertiary medical hospitals in Japan, retrospectively collected data on patients undergoing PCI for acute coronary syndrome or stable coronary artery disease. Cubic spline curves modeled the relationship between SBP and all-cause death in the entire cohort and subgroups stratified by age, sex, diabetes, left ventricular (LV) hypertrophy, renal function and LV systolic function. We assessed the SBP, which minimizes mortality risk. RESULTS: A total of 8384 patients [71 [IQR 64, 78] years, 6494 (77%) male] with SBP at hospital discharge were analyzed. During 2.7 years of median follow-up, 695 deaths occurred. In the overall population, spline analysis demonstrated a nadir range of mortality risk around an SBP of 110-130 mmHg. Subgroup analyses revealed that elderly (age ≥ 80 years), those with renal dysfunction, and those with preserved LV systolic function had higher SBP levels associated with lowest risk. Conversely, patients <80 years, those with better renal function, and those with LV systolic dysfunction exhibited lower SBP levels at lowest risk. CONCLUSION: This study demonstrated differential association between SBP and mortality risk in various subgroups, highlighting the need for personalized BP management in multimorbid patients with coronary artery disease.

BACKGROUND: There are few data verifying the utility of the CHADS-P2A2RC score in comparison with the CHADS2 score for estimating net adverse clinical events (NACE) in chronic coronary syndrome (CCS) patients without atrial fibrillation (AF) in real-world settings. METHODS: We performed analysis for a total of 3985 CCS patients without AF who underwent percutaneous coronary intervention (PCI) between April 2013 and March 2019 for whom information was obtained from the CLIDAS (Clinical Deep Data Accumulation System)-PCI database. The primary endpoint was NACE defined as the composite of 3-point major adverse cardiovascular events (3P-MACE) (cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke) and GUSTO moderate/severe bleeding events. RESULTS: Kaplan-Meier analysis showed that both the CHADS-P2A2RC and CHADS2 scores stratified the risks. The incidences of NACE were stratified well by the very-high-risk category, which was uniquely defined as a CHADS-P2A2RC score of ≥6 (hazard ratio: 2.38, 95 % CI = 1.91-2.97, p-value <0.001). The area under the curve (AUC) in estimating NACE within 3 years was higher when the CHADS-P2A2RC score was used than when the CHADS2 score was used (0.67 vs. 0.62, p = 0.003). This was mainly due to the accuracy in estimating bleeding events (0.66 vs. 0.60, p = 0.006). CONCLUSIONS: The accuracy in estimating NACE after PCI for CCS patients without AF was higher when the CHADS-P2A2RC score was used than when the CHADS2 score was used, mainly due to the accuracy in predicting bleeding risk. Higher incidences of endpoints were well-stratified by a very-high-risk category defined as a CHADS-P2A2RC score of ≥6.

The prevalence of malignancies in patients undergoing percutaneous coronary intervention (PCI) is increasing with aging. Active malignancy is a significant contributor to high bleeding risk. For cancer patients requiring oral anticoagulant (OAC) therapy, the choice between direct oral anticoagulants (DOAC) and warfarin is critical. The aim of this study was to investigate long-term bleeding events in patients with malignancy undergoing PCI. The CLIDAS (Clinical Deep Data Accumulation System) multicenter database includes data from seven tertiary medical hospitals in Japan. This retrospective analysis included 6451 patients who underwent PCI between April 2013 and March 2019 and completed 3-year follow-up. The patients were divided into two groups; No malignancy (n = 5787) and Malignancy group (n = 664). Malignancy was defined by a history of cancer treatment. These groups were further subcategorized based on OAC therapy; (1) No malignancy without OAC (n = 5134), (2) No malignancy with DOAC (n = 261), (3) No malignancy with warfarin (n = 392), (4) Malignancy without OAC (n = 589), (5) Malignancy with DOAC (n = 38), and (6) Malignancy with warfarin (n = 37). The primary outcome was the incidence of bleeding events, defined according to the Global Use of Streptokinase and t-PA for Occluded Coronary Arteries classification of moderate and severe bleeding. The secondary outcomes were major adverse cardiac events (MACE) and net adverse clinical events (NACE). Multivariable Cox regression analysis showed that the malignancy with warfarin group had a significantly higher risk of bleeding events compared to the malignancy without OAC group (hazard ratio [HR], 3.64; 95% confidence interval [CI], 1.38-9.61, p value = 0.009). No significant differences were observed for MACE (HR, 1.39; 95% CI 0.59-3.25, p value = 0.454) or NACE (HR, 1.62; 95% CI, 0.80-3.29; p value = 0.184). Malignancy patients receiving warfarin were associated with a higher risk of bleeding events. DOACs may represent a preferable alternative to warfarin with regard to bleeding risk in patients with malignancy undergoing PCI.