祖父江 嘉洋

AIMS: Sudden cardiac death (SCD) is a common mode of death in patients with congestive heart failure (CHF). Implantable cardioverter defibrillator (ICD) implantation is established treatment for SCD prevention, but current eligibility criteria based on left ventricular ejection fraction (LVEF) and New York Heart Association (NYHA) functional class may be due for reconsideration given the increasing effectiveness of pharmacological therapy. We sought to reconsider the risk stratification of SCD in patients with symptomatic CHF. METHODS: In total, 1,676 consecutive patients (74 ± 13 years old; 56% male) with NYHA class II or III CHF between 2008 and 2015 were enrolled for this prospective study. The endpoint was SCD. RESULTS: During a median (interquartile range) follow-up period of 25 (4-70) months, 198 (11.8%) patients suffered SCD. Of those events, 23% occurred within 3 months of discharge. In the adjusted analyses, estimated glomerular filtration rate (eGFR) < 30 ml/min/1.73 m2 [hazard ratio (HR) 1.73, 95% confidence interval (CI) 1.11-2.70, P = 0.01] and LVEF ≤ 35% (HR 2.31, 95% CI 1.47-3.66, P < 0.01) were independent risk predictors of SCD. Addition of eGFR to LVEF significantly improved prediction of SCD in the C-index (P = 0.04), and in two metrics, net reclassification improvement (P = 0.01) and integrated discrimination improvement (P = 0.03). The predictive power of eGFR declined time-dependently over 2 years. CONCLUSIONS: The addition of eGFR to current eligibility criteria may be useful for risk assessment of SCD, although its predictive power wanes over time. Roughly a quarter of the SCD occurred within 3 months after discharge in patients with CHF.

BACKGROUND: A lesion-level risk prediction for acute coronary syndrome (ACS) needs better characterization. OBJECTIVES: This study sought to investigate the additive value of artificial intelligence-enabled quantitative coronary plaque and hemodynamic analysis (AI-QCPHA). METHODS: Among ACS patients who underwent coronary computed tomography angiography (CTA) from 1 month to 3 years before the ACS event, culprit and nonculprit lesions on coronary CTA were adjudicated based on invasive coronary angiography. The primary endpoint was the predictability of the risk models for ACS culprit lesions. The reference model included the Coronary Artery Disease Reporting and Data System, a standardized classification for stenosis severity, and high-risk plaque, defined as lesions with ≥2 adverse plaque characteristics. The new prediction model was the reference model plus AI-QCPHA features, selected by hierarchical clustering and information gain in the derivation cohort. The model performance was assessed in the validation cohort. RESULTS: Among 351 patients (age: 65.9 ± 11.7 years) with 2,088 nonculprit and 363 culprit lesions, the median interval from coronary CTA to ACS event was 375 days (Q1-Q3: 95-645 days), and 223 patients (63.5%) presented with myocardial infarction. In the derivation cohort (n = 243), the best AI-QCPHA features were fractional flow reserve across the lesion, plaque burden, total plaque volume, low-attenuation plaque volume, and averaged percent total myocardial blood flow. The addition of AI-QCPHA features showed higher predictability than the reference model in the validation cohort (n = 108) (AUC: 0.84 vs 0.78; P < 0.001). The additive value of AI-QCPHA features was consistent across different timepoints from coronary CTA. CONCLUSIONS: AI-enabled plaque and hemodynamic quantification enhanced the predictability for ACS culprit lesions over the conventional coronary CTA analysis. (Exploring the Mechanism of Plaque Rupture in Acute Coronary Syndrome Using Coronary Computed Tomography Angiography and Computational Fluid Dynamics II [EMERALD-II]; NCT03591328).

BACKGROUND AND AIMS: Artificial intelligence quantitative CT (AI-QCT) determines coronary plaque morphology with high efficiency and accuracy. Yet, its performance to quantify lipid-rich plaque remains unclear. This study investigated the performance of AI-QCT for the detection of low-density noncalcified plaque (LD-NCP) using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). METHODS: The INVICTUS Registry is a multi-center registry enrolling patients undergoing clinically indicated coronary CT angiography and IVUS, NIRS-IVUS, or optical coherence tomography. We assessed the performance of various Hounsfield unit (HU) and volume thresholds of LD-NCP using maxLCBI4mm ≥ 400 as the reference standard and the correlation of the vessel area, lumen area, plaque burden, and lesion length between AI-QCT and IVUS. RESULTS: This study included 133 atherosclerotic plaques from 47 patients who underwent coronary CT angiography and NIRS-IVUS The area under the curve of LD-NCP<30HU was 0.97 (95% confidence interval [CI]: 0.93-1.00] with an optimal volume threshold of 2.30 mm3. Accuracy, sensitivity, and specificity were 94% (95% CI: 88-96%], 93% (95% CI: 76-98%), and 94% (95% CI: 88-98%), respectively, using <30 HU and 2.3 mm3, versus 42%, 100%, and 27% using <30 HU and >0 mm3 volume of LD-NCP (p < 0.001 for accuracy and specificity). AI-QCT strongly correlated with IVUS measurements; vessel area (r2 = 0.87), lumen area (r2 = 0.87), plaque burden (r2 = 0.78) and lesion length (r2 = 0.88), respectively. CONCLUSIONS: AI-QCT demonstrated excellent diagnostic performance in detecting significant LD-NCP using maxLCBI4mm ≥ 400 as the reference standard. Additionally, vessel area, lumen area, plaque burden, and lesion length derived from AI-QCT strongly correlated with respective IVUS measurements.

BACKGROUND: We aimed to investigate the association between ventricular repolarization instability and sustained ventricular tachycardia and ventricular fibrillation (VT/VF) occurring within 48 h (acute-phase VT/VF) after the onset of acute coronary syndrome (ACS) and the prognostic role of repolarization instability and heart rate variability (HRV) after discharge from the hospital. METHODS: We studied 572 ACS patients with a left ventricular ejection fraction >35%. The ventricular repolarization instability was assessed by the beat-to-beat T-wave amplitude variability (TAV) using high-resolution 24-h Holter ECGs recorded at a median of 11 days from the date of admission. We calculated the HRV parameters including the deceleration capacity (DC) and non-Gaussian index calculated on a 25 s timescale (λ25s). The DC and λ25s were dichotomized based on previous studies' thresholds. RESULTS: Acute-phase VT/VF developed in 43 (7.5%) patients. In-hospital mortality was significantly higher among VT/VF patients (4.7% vs. 0.9%, p = .03). An adjusted logistic model showed that the maximum TAV (odds ratio 1.02, 95% confidence interval [CI] 1.00-1.29, p = .04) was associated with acute-phase VT/VF. During a median follow-up period of 2.1 years, 19 (3.3%) patients had cardiac deaths or resuscitated cardiac arrest. Acute-phase VT/VF (p = .12) and TAV (p = .72) were not significant predictors of survival. An age and sex-adjusted Cox model showed that the DC (p < .01), λ25s (p < .01), and emergency coronary intervention (p < .01) were independent predictors. CONCLUSION: T-wave amplitude variability was associated with acute-phase VT/VF, but the TAV was not predictive of survival post-discharge. The DC, λ25s, and emergency coronary intervention were independent predictors of survival.

OBJECTIVE: The early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of treatment strategies. However, the ECG waveforms of cardiac care unit (CCU) patients with severe cardiac disease are often complicated by comorbidities and patient conditions, making it difficult to predict the severity of further cardiac disease. Therefore, this study predicts the short-term prognosis of CCU patients to detect further deterioration in CCU patients at an early stage. METHODS: The ECG data (II, V3, V5, aVR induction) of CCU patients were converted to image data. The transformed ECG images were used to predict short-term prognosis with a two-dimensional convolutional neural network (CNN). RESULTS: The prediction accuracy was 77.3%. Visualization by GradCAM showed that the CNN tended to focus on the shape and regularity of waveforms, such as heart failure and myocardial infarction. CONCLUSION: These results suggest that the proposed method may be useful for short-term prognosis prediction using the ECG waveforms of CCU patients. CLINICAL IMPACT: The proposed method could be used to determine the treatment strategy and choose the intensity of treatment after admission to the CCU.