Effects of Metrical Context on the P1 Component.

Background and Objectives: : The temporal structure of sound, characterized by regular patterns, plays a crucial role in optimizing the processing of auditory information. The meter, representing a well-organized sequence of evenly spaced beats in music, exhibits a hierarchical arrangement, with stronger beats occupying higher metrical positions. Moreover, the meter has been shown to influence behavioral and neural processing, particularly the N1, P2, and mismatch negativity components. However, the role of the P1 component in the context of metrical hierarchy remains unexplored. This study aimed to investigate the effects of metrical hierarchy on the P1 component and compare the responses between musicians and non-musicians. Subjects and Methods: : Thirty participants (15 musicians and 15 non-musicians) were enrolled in the study. Auditory stimuli consisted of a synthesized speech syllable presented together with a repeating series of four tones, establishing a quadruple meter. Electrophysiological recordings were performed to measure the P1 component. Results: : The results revealed that metrical position had a significant effect on P1 amplitude, with the strongest beat showing the lowest amplitude. This contrasts with previous findings, in which enhanced P1 responses were typically observed at on-the-beat positions. The reduced P1 response on the strong beat can be interpreted within the framework of predictive coding and temporal prediction, where a higher predictability of pitch changes at the strong beat leads to a reduction in the P1 response. Furthermore, higher P1 amplitudes were observed in musicians compared to non-musicians, suggesting that musicians have enhanced sensory processing. Conclusions: : This study demonstrates the effects of metrical hierarchy on the P1 component, thereby enriching our understanding of auditory processing. The results suggest that predictive coding and temporal prediction play important roles in shaping sensory processing. Further, they suggest that musical training may enhance P1 responses.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning.

BACKGROUND: Quantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. METHODS: AI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). RESULTS: AI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. CONCLUSION: AI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Quantitative Coronary Angiography vs Intravascular Ultrasonography to Guide Drug-Eluting Stent Implantation: A Randomized Clinical Trial.

Importance: Although intravascular ultrasonography (IVUS) guidance promotes favorable outcomes after percutaneous coronary intervention (PCI), many catheterization laboratories worldwide lack access. Objective: To investigate whether systematic implementation of quantitative coronary angiography (QCA) to assist angiography-guided PCI could be an alternative strategy to IVUS guidance during stent implantation. Design, Setting, and Participants: This randomized, open-label, noninferiority clinical trial enrolled adults (aged ≥18 years) with chronic or acute coronary syndrome and angiographically confirmed native coronary artery stenosis requiring PCI. Patients were enrolled in 6 cardiac centers in Korea from February 23, 2017, to August 23, 2021, and follow-up occurred through August 25, 2022. All principal analyses were performed according to the intention-to-treat principle. Interventions: After successful guidewire crossing of the first target lesion, patients were randomized in a 1:1 ratio to receive either QCA- or IVUS-guided PCI. Main Outcomes and Measures: The primary outcome was target lesion failure at 12 months, defined as a composite of cardiac death, target vessel myocardial infarction, or ischemia-driven target lesion revascularization. The trial was designed assuming an event rate of 8%, with the upper limit of the 1-sided 97.5% CI of the absolute difference in 12-month target lesion failure (QCA-guided PCI minus IVUS-guided PCI) to be less than 3.5 percentage points for noninferiority. Results: The trial included 1528 patients who underwent PCI with QCA guidance (763; mean [SD] age, 64.1 [9.9] years; 574 males [75.2%]) or IVUS guidance (765; mean [SD] age, 64.6 [9.5] years; 622 males [81.3%]). The post-PCI mean (SD) minimum lumen diameter was similar between the QCA- and IVUS-guided PCI groups (2.57 [0.55] vs 2.60 [0.58] mm, P = .26). Target lesion failure at 12 months occurred in 29 of 763 patients (3.81%) in the QCA-guided PCI group and 29 of 765 patients (3.80%) in the IVUS-guided PCI group (absolute risk difference, 0.01 percentage points [95% CI, -1.91 to 1.93 percentage points]; hazard ratio, 1.00 [95% CI, 0.60-1.68]; P = .99). There was no difference in the rates of stent edge dissection (1.2% vs 0.7%, P = .25), coronary perforation (0.2% vs 0.4%, P = .41), or stent thrombosis (0.53% vs 0.66%, P = .74) between the QCA- and IVUS-guided PCI groups. The risk of the primary end point was consistent regardless of subgroup, with no significant interaction. Conclusions and Relevance: Findings of this randomized clinical trial indicate that QCA and IVUS guidance during PCI showed similar rates of target lesion failure at 12 months. However, due to the lower-than-expected rates of target lesion failure in this trial, the findings should be interpreted with caution. Trial Registration: ClinicalTrials.gov Identifier: NCT02978456.

Optimal Minimal Stent Area and Impact of Stent Underexpansion in Left Main Up-Front 2-Stent Strategy.

BACKGROUND: We previously reported the use of minimal stent area to predict angiographic in-stent restenosis after drug-eluting stent implantation for unprotected left main (LM) disease. We aimed to evaluate the optimal minimal stent area criteria for up-front LM 2-stenting based on long-term clinical outcomes. METHODS: We identified 292 consecutive patients with LM bifurcation stenosis who were treated using the crush technique. The final minimal stent area was measured in the ostial left anterior descending artery (LAD), ostial left circumflex artery (LCX), and distal LM. The primary outcome was 5-year major adverse cardiac events, including all-cause death, myocardial infarction, and target lesion revascularization. RESULTS: The minimal stent area cutoff values that best predicted the 5-year major adverse cardiac events were 11.8 mm2 for distal LM (area under the curve, 0.57; P=0.15), 8.3 mm2 for LAD ostium (area under the curve, 0.62; P=0.02), and 5.7 mm2 for LCX ostium (area under the curve, 0.64; P=0.01). Using these criteria, the risk of 5-year major adverse cardiac events was significantly associated with stent underexpansion in the LAD ostium (hazard ratio, 3.14; [95% CI, 1.23-8.06]; P=0.02) and LCX ostium (hazard ratio, 2.60 [95% CI, 1.11-6.07]; P=0.03) but not in the distal LM (hazard ratio, 0.81 [95% CI, 0.34-1.91]; P=0.63). Patients with stent underexpansion in both ostial LAD and LCX had a significantly higher rate of 5-year major adverse cardiac events than those with no or 1 underexpanded stent of either ostium (P<0.01). CONCLUSIONS: Stent underexpansion in the LAD and LCX ostium was significantly associated with long-term outcomes in patients who underwent up-front 2-stenting for LM bifurcation stenosis.

Deep Learning-Based Lumen and Vessel Segmentation of Intravascular Ultrasound Images in Coronary Artery Disease.

BACKGROUND AND OBJECTIVES: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. METHODS: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. RESULTS: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. CONCLUSIONS: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

Valor pronóstico a 10 años de la angiografía coronaria por TAC en pacientes asintomáticos con diabetes tipo 2 / Ten-year prognostic value of coronary CT angiography in asymptomatic patients with type 2 diabetes

Introducción y objetivos: La escala UKPDS (acrónimo inglés de «Estudio Prospectivo de Diabetes del Reino Unido») tiene un valor limitado para la predicción de eventos de enfermedad arterial coronaria (EAC). El estudio pretende investigar el valor añadido de la angiografía coronaria por tomografía computarizada (ACTC) sobre la escala de riesgo UKPDS para la predicción a 10 años de eventos cardiacos adversos en pacientes asintomáticos con diabetes tipo 2. Métodos: Se evaluó a 589 pacientes diabéticos asintomáticos sin historia de EAC a quienes se les realizó una ACTC. El objetivo principal estaba compuesto por muerte cardiaca, infarto de miocardio no mortal, angina inestable que requiere hospitalización y revascularización. Se estimó la habilidad de discriminación y reclasificación para modelos de predicción que incluían combinaciones de la categoría UKPDS, gravedad de estenosis y puntuación de calcio arterial coronario por ACTC. Resultados: La incidencia del objetivo primario fue del 12,4%. A lo largo de 10 años de seguimiento, los pacientes sin placa ateroesclerótica por ACTC tendieron a tener un bajo ratio de eventos coronarios en tanto que aquellos con EAC obstructiva tuvieron una mayor ratio de eventos, independientemente de la categoría de riesgo de la escala UKPDS. EL modelo que solo incluyó la categoría UKPDS tuvo un índice C de Harrell de 0,658; añadiendo el grado de estenosis coronaria al modelo se incrementó significativamente el índice C en 0,066 (p=0,004), en tanto que la adición del CSC incrementó el índice C en solo 0,039 (p=0,056). Globalmente, la información de la ACTC añadida a la categoría de riesgo UKPDS mejoró el ratio de reclasificación para la predicción del objetivo primario. Conclusiones: En pacientes asintomáticos con diabetes tipo 2, la información de la ACTC para EAC...(AU)

Introduction and objectives: The United Kingdom Prospective Diabetes Study (UKPDS) risk score has limited value for predicting coronary artery disease (CAD) events. We investigated the additive value of coronary computed tomography angiography (CCTA) on top of the UKPDS risk score in predicting 10-year adverse cardiac events in asymptomatic patients with type 2 diabetes. Methods: We evaluated 589 asymptomatic diabetic patients without a history of CAD who underwent CCTA. The primary outcome was a composite of cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and revascularization. We estimated the discrimination and reclassification ability for the prediction models, which included combinations of the UKPDS category, severity of stenosis, and coronary artery calcium score by CCTA. Results: The incidence of the primary outcome was 12.4%. During 10 years of follow-up, patients without plaque by CCTA tended to have a low CAD event rate, while those with obstructive CAD tended to have a high event rate, irrespective of the baseline UKPDS risk category. The model that included only the UKPDS category had a Harrell's c-index of 0.658; adding the degree of stenosis to the model significantly increased the c-index by 0.066 (P=.004), while adding coronary artery calcium score increased the c-index by only 0.039 (P=.056). Overall, CCTA information in addition to the UKPDS risk category improved the reclassification rate for predicting the primary outcome. Conclusions: In asymptomatic patients with type 2 diabetes, CCTA information for CAD provided significant incremental discriminatory power beyond the UKPDS risk score category for predicting 10-year adverse coronary events.(AU)

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm.

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography.

BACKGROUND: Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room. PURPOSE: This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA. METHODS: Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients. RESULTS: The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second. CONCLUSION: Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.

Machine Learning-Based prediction of Post-Treatment ambulatory blood pressure in patients with hypertension.

Purpose. Pre-treatment prediction of individual blood pressure (BP) response to anti-hypertensive medication is important to determine the specific regimen for promptly and safely achieving a target BP. This study aimed to develop supervised machine learning (ML) models for predicting patient-specific treatment effects using 24-hour ambulatory BP monitoring (ABPM) data.Materials and Methods. A total of 1,129 patients who had both baseline and follow-up ABPM data were randomly assigned into training, validation and test sets in a 3:1:1 ratio. Utilising the features including clinical and laboratory findings, initial ABPM data, and anti-hypertensive medication at baseline and at follow-up, ML models were developed to predict post-treatment individual BP response. Each case was labelled by the mean 24-hour and daytime BPs derived from the follow-up ABPM.Results. At baseline, 616 (55%) patients had been treated using mono or combination therapy with 45 anti-hypertensive drugs and the remaining 513 (45%) patients had been untreated (drug-naïve). By using CatBoost, the difference between predicted vs. measured mean 24-hour systolic BP at follow-up was 8.4 ± 7.0 mm Hg (% difference of 6.6% ± 5.7%). The difference between predicted vs. measured mean 24-hour diastolic BP was 5.3 ± 4.3 mm Hg (% difference of 6.8% ± 5.5%). There were significant correlations between the CatBoost-predicted vs. the ABPM-measured changes in the mean 24-hour Systolic (r = 0.74) and diastolic (r = 0.68) BPs from baseline to follow-up. Even in the patients with renal insufficiency or diabetes, the correlations between CatBoost-predicted vs. ABPM-measured BP changes were significant.Conclusion. ML algorithms accurately predict the post-treatment ambulatory BP levels, which may assist clinicians in personalising anti-hypertensive treatment.

The prediction of post-treatment BP response is essential to plan the appropriate optimal treatment strategy for achieving the target BP level.The poor predictability of the post-treatment BP level is due to the complex pathophysiology of individual BP response, which can partly explain the poor rate to achieve the target systolic BP.In this current study including both treated and untreated patients with hypertension, machine leaning models predicted the post-treatment mean BP levels on 24-hr ABPM even in high-risk patients and patients with a high BP variability.Model-derived selection and optimisation of anti-hypertension drugs may facilitate prompt achievement of adequate BP control without drug-related complications and avoiding repeating 24-hour ABPM or multiple visits for drug readjustment.

Tratamiento antiagregante plaquetario doble tras la intervención coronaria percutánea del tronco coronario izquierdo / Dual antiplatelet therapy after percutaneous coronary intervention for left main coronary artery disease

Introducción y objetivos Son escasos los datos sobre la duración y el impacto pronóstico del tratamiento antiagregante plaquetario doble (TAPD) tras una intervención coronaria percutánea (ICP) del tronco coronario izquierdo (TCI) con stents farmacoactivos de segunda generación. El objetivo de este estudio es investigar los patrones de prescripción y el efecto pronóstico a largo plazo de la duración del TAPD en pacientes sometidos a ICP del TCI con stents farmacoactivos segunda generación. Métodos A partir de los datos individuales de los registros IRIS-MAIN y KOMATE, se incluyó a 1.827 pacientes sometidos a ICP del TCI con stents farmacoactivos de segunda generación de los que hubiese información válida sobre la duración del TAPD. El objetivo de eficacia fue la aparición de eventos cardiovasculares adversos mayores (MACE) (un combinado de muerte cardiaca, infarto de miocardio y trombosis del stent) y el de seguridad fue la presencia de hemorragia mayor según TIMI. Resultados Las duraciones del TAPD fueron <6 meses (n=273), de 6-12 meses (n=477), de 12-24 meses (n=637) y ≥ 24 meses (n=440). La mediana de la duración del seguimiento fue de 3,9 [intervalo intercuartílico, 3,01-5,00] años. El TAPD prolongado se asoció con menor incidencia de MACE. En el análisis de puntuación de propensión multigrupo, las HR ajustadas de los MACE fueron significativamente mayores con los TAPD de menos de 6 meses y de 6-12 meses (HR=4,51; IC95%, 2,96-6,88) frente al TAPD de 12-24 meses (HR=1,92; IC95%, 1,23-3,00). No hubo diferencias en la HR de hemorragia mayor entre los grupos evaluados. Conclusiones La duración del TAPD tras la ICP para la enfermedad del TCI es muy variable. Aunque debe considerarse en función de la situación clínica de cada paciente, un TAPD de menos de 12 meses se asoció con mayor incidencia de MACE (AU)

Introduction and objectives There are scarce data on the optimal duration and prognostic impact of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) with second-generation drug-eluting stents for left main coronary artery (LMCA) disease. The aim of this study was to investigate the practice pattern and long-term prognostic effect of DAPT duration in patients undergoing PCI with second-generation drug-eluting stents for LMCA disease. Methods Using individual patient-level data from the IRIS-MAIN and KOMATE registries, 1827 patients undergoing PCI with second-generation drug-eluting stents for LMCA disease with valid information on DAPT duration were included. The efficacy outcome was major adverse cardiovascular events (MACE, a composite of cardiac death, myocardial infarction, and stent thrombosis) and the safety outcome was TIMI major bleeding. Results DAPT duration was <6 months (n=273), 6 to 12 months (n=477), 12 to 24 months (n=637), and ≥ 24 months (n=440). The median follow-up duration was 3.9 [interquartile range, 3.01-5.00] years. Prolonged DAPT duration was associated with lower incidences of MACE. In multigroup propensity score analysis, adjusted HR for MACE were significantly higher for DAPT <6 months and DAPT 6 to 12 months than for DAPT 12 to 24 months (HR, 4.51; 95%CI, 2.96-6.88 and HR 1.92; 95%CI, 1.23-3.00). There was no difference in HR for major bleeding among the assessed groups. Conclusions DAPT duration following PCI for LMCA disease is highly variable. Although the duration of DAPT should be considered in the context of the clinical situation of each patient, <12 months of DAPT was associated with higher incidence of MACE. Registration identifiers: NCT01341327; NCT03908463 (AU)

Myocardial Scar and Revascularization on Mortality in Ischemic Cardiomyopathy (from the Late Gadolinium Enhancement Cardiac Magnetic Resonance Study).

Myocardial viability test to guide revascularization remains uncertain in patients with ischemic cardiomyopathy. We evaluated the different impacts of revascularization on cardiac mortality according to the extent of myocardial scar assessed by cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) in patients with ischemic cardiomyopathy. A total of 404 consecutive patients with significant coronary artery disease and an ejection fraction ≤35% were assessed by LGE-CMR before revascularization. Of them, 306 patients underwent revascularization and 98 patients received medical treatment alone. The primary outcome was cardiac death. During a median follow-up of 6.3 years, cardiac death occurred in 158 patients (39.1%). Revascularization was associated with a significantly lower risk of cardiac death than medical treatment alone in the overall population (adjusted hazard ratio [aHR] 0.29, 95% confidence interval (CI) 0.19 to 0.45, p <0.001). There was a significant interaction between the number of segments with >75% transmural LGE and revascularization on the risk of cardiac death (p = 0.037 for interaction). In patients with limited myocardial scar (<6 segments with >75% transmural LGE, n = 354), revascularization had a significantly lower risk of cardiac death than medical treatment alone (aHR 0.24, 95% CI 0.15 to 0.37, p <0.001); in patients with extensive myocardial scar (≥6 segments with >75% transmural LGE, n = 50), there was no significant difference between revascularization and medical treatment alone regarding the risk of cardiac death (aHR 1.33, 95% CI 0.46 to 3.80, p = 0.60). In conclusion, the assessment of myocardial scar by LGE-CMR may be helpful in the decision-making process for revascularization in patients with ischemic cardiomyopathy.

Ten-year prognostic value of coronary CT angiography in asymptomatic patients with type 2 diabetes.

INTRODUCTION AND OBJECTIVES: The United Kingdom Prospective Diabetes Study (UKPDS) risk score has limited value for predicting coronary artery disease (CAD) events. We investigated the additive value of coronary computed tomography angiography (CCTA) on top of the UKPDS risk score in predicting 10-year adverse cardiac events in asymptomatic patients with type 2 diabetes. METHODS: We evaluated 589 asymptomatic diabetic patients without a history of CAD who underwent CCTA. The primary outcome was a composite of cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, and revascularization. We estimated the discrimination and reclassification ability for the prediction models, which included combinations of the UKPDS category, severity of stenosis, and coronary artery calcium score by CCTA. RESULTS: The incidence of the primary outcome was 12.4%. During 10 years of follow-up, patients without plaque by CCTA tended to have a low CAD event rate, while those with obstructive CAD tended to have a high event rate, irrespective of the baseline UKPDS risk category. The model that included only the UKPDS category had a Harrell's c-index of 0.658; adding the degree of stenosis to the model significantly increased the c-index by 0.066 (P=.004), while adding coronary artery calcium score increased the c-index by only 0.039 (P=.056). Overall, CCTA information in addition to the UKPDS risk category improved the reclassification rate for predicting the primary outcome. CONCLUSIONS: In asymptomatic patients with type 2 diabetes, CCTA information for CAD provided significant incremental discriminatory power beyond the UKPDS risk score category for predicting 10-year adverse coronary events.

Dual antiplatelet therapy after percutaneous coronary intervention for left main coronary artery disease.

INTRODUCTION AND OBJECTIVES: There are scarce data on the optimal duration and prognostic impact of dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) with second-generation drug-eluting stents for left main coronary artery (LMCA) disease. The aim of this study was to investigate the practice pattern and long-term prognostic effect of DAPT duration in patients undergoing PCI with second-generation drug-eluting stents for LMCA disease. METHODS: Using individual patient-level data from the IRIS-MAIN and KOMATE registries, 1827 patients undergoing PCI with second-generation drug-eluting stents for LMCA disease with valid information on DAPT duration were included. The efficacy outcome was major adverse cardiovascular events (MACE, a composite of cardiac death, myocardial infarction, and stent thrombosis) and the safety outcome was TIMI major bleeding. RESULTS: DAPT duration was <6 months (n=273), 6 to 12 months (n=477), 12 to 24 months (n=637), and ≥ 24 months (n=440). The median follow-up duration was 3.9 [interquartile range, 3.01-5.00] years. Prolonged DAPT duration was associated with lower incidences of MACE. In multigroup propensity score analysis, adjusted HR for MACE were significantly higher for DAPT <6 months and DAPT 6 to 12 months than for DAPT 12 to 24 months (HR, 4.51; 95%CI, 2.96-6.88 and HR 1.92; 95%CI, 1.23-3.00). There was no difference in HR for major bleeding among the assessed groups. CONCLUSIONS: DAPT duration following PCI for LMCA disease is highly variable. Although the duration of DAPT should be considered in the context of the clinical situation of each patient, <12 months of DAPT was associated with higher incidence of MACE. Registration identifiers: NCT01341327; NCT03908463.

Incidence and predictors of intracranial bleeding after coronary artery bypass graft surgery.

Background: There is a paucity of direct data on the incidence and predictors of intracranial bleeding (ICB) after coronary artery bypass graft surgery (CABG). Methods: The Korean National Health Insurance database was used to identify patients without prior ICB who underwent CABG. The outcomes of interest were the time-dependent incidence rates of ICB and the associated mortality. Results: Among 35,021 patients who underwent CABG between 2007 and 2018, 895 (2.6%) experienced an ICB during a median follow-up of 6.0 years. The 1-year cumulative incidence of ICB was 0.76%, with a relatively high incidence rate (9.93 cases per 1,000 person-years) within the first 1-30 days. Subsequent incidence rates showed a sharp decline until 3 years, followed by a steady decrease up to 10 years. The 1-year mortality rate after ICB was 38.1%, with most deaths occurring within 30 days (23.6%). The predictors of ICB after CABG were age ≥ 75 years, hypertension, pre-existing dementia, history of ischemic stroke or transient ischemic attack, and end-stage renal disease. Conclusions: In an unselected nationwide population undergoing CABG, the incidence of ICB was non-negligible and showed a relatively high incidence rate during the early postoperative period. Post-CABG ICB was associated with a high risk of premature death. Further research is needed to stratify high-risk patients and personalize therapeutic decisions for preventing ICB after CABG.

Electronic Medical Record-Based Machine Learning Approach to Predict the Risk of 30-Day Adverse Cardiac Events After Invasive Coronary Treatment: Machine Learning Model Development and Validation.

BACKGROUND: Although there is a growing interest in prediction models based on electronic medical records (EMRs) to identify patients at risk of adverse cardiac events following invasive coronary treatment, robust models fully utilizing EMR data are limited. OBJECTIVE: We aimed to develop and validate machine learning (ML) models by using diverse fields of EMR to predict the risk of 30-day adverse cardiac events after percutaneous intervention or bypass surgery. METHODS: EMR data of 5,184,565 records of 16,793 patients at a quaternary hospital between 2006 and 2016 were categorized into static basic (eg, demographics), dynamic time-series (eg, laboratory values), and cardiac-specific data (eg, coronary angiography). The data were randomly split into training, tuning, and testing sets in a ratio of 3:1:1. Each model was evaluated with 5-fold cross-validation and with an external EMR-based cohort at a tertiary hospital. Logistic regression (LR), random forest (RF), gradient boosting machine (GBM), and feedforward neural network (FNN) algorithms were applied. The primary outcome was 30-day mortality following invasive treatment. RESULTS: GBM showed the best performance with area under the receiver operating characteristic curve (AUROC) of 0.99; RF had a similar AUROC of 0.98. AUROCs of FNN and LR were 0.96 and 0.93, respectively. GBM had the highest area under the precision-recall curve (AUPRC) of 0.80, and the AUPRCs of RF, LR, and FNN were 0.73, 0.68, and 0.63, respectively. All models showed low Brier scores of <0.1 as well as highly fitted calibration plots, indicating a good fit of the ML-based models. On external validation, the GBM model demonstrated maximal performance with an AUROC of 0.90, while FNN had an AUROC of 0.85. The AUROCs of LR and RF were slightly lower at 0.80 and 0.79, respectively. The AUPRCs of GBM, LR, and FNN were similar at 0.47, 0.43, and 0.41, respectively, while that of RF was lower at 0.33. Among the categories in the GBM model, time-series dynamic data demonstrated a high AUROC of >0.95, contributing majorly to the excellent results. CONCLUSIONS: Exploiting the diverse fields of the EMR data set, the ML-based 30-day adverse cardiac event prediction models demonstrated outstanding results, and the applied framework could be generalized for various health care prediction models.

Ischemic Burden Assessment Using Single Photon Emission Computed Tomography in Single Vessel Chronic Total Occlusion of Coronary Artery.

BACKGROUND AND OBJECTIVES: Studies evaluating the nature of ischemic burden of chronic total occlusion (CTO) vessels are still lacking. METHODS: A total of 165 patients with single vessel CTO >2.5 mm in an epicardial coronary artery who underwent single photon emission computed tomography (SPECT) were enrolled in the study. Ischemic burden was calculated with the use of semi-quantitative SPECT analysis, and was defined as the summed difference score (SDS) divided by the maximal limit of the score (=SDS/68). RESULTS: The mean age of the participants was 59.5 years and the CTO of the left anterior descending coronary artery (LAD), left circumplex coronary artery (LCX), and right coronary artery (RCA) accounted for 93 (56.4%), 18 (10.9%), and 54 (32.7%) patients, respectively. The median ischemic burden of the total population was 8.8%, and it was highest in the LAD CTO (10.3%) compared with the LCX (5.9%) and RCA CTO (5.9%, p<0.001). High-ischemic burden (ischemic burden >10%) was observed in 66 patients (40.0%), and in 47 patients (50.5%) of the LAD CTO. Ischemic burden was different according to the CTO location only in LAD CTO. The statistically significant predictors for high-ischemic burden were hypertension, baseline ejection fraction >45%, LAD CTO, proximal CTO location, and de novo CTO. Japanese-CTO score and Rentrop scale collateral grade were not associated with high-ischemic burden. CONCLUSIONS: Only 40% of patients with single vessel CTO had ischemic burden >10%. For CTO vessels, measurement of ischemic burden using SPECT prior to revascularization may be helpful in identifying beneficial subjects.

Quantitative coronary angiography versus intravascular ultrasound guidance for drug-eluting stent implantation (GUIDE-DES): study protocol for a randomised controlled non-inferiority trial.

INTRODUCTION: Angiography remains the gold standard for guiding percutaneous coronary intervention (PCI). However, it is prone to suboptimal stent results due to the visual estimation of coronary measurements. Although the benefit of intravascular ultrasound (IVUS)-guided PCI is becoming increasingly recognised, IVUS is not affordable for many catheterisation laboratories. Thus, a more practical and standardised angiography-based approach is necessary to support stent implantation. METHODS AND ANALYSIS: The Quantitative Coronary Angiography versus Intravascular Ultrasound Guidance for Drug-Eluting Stent Implantation trial is a randomised, investigator-initiated, multicentre, open-label, non-inferiority trial comparing the quantitative coronary angiography (QCA)-guided PCI strategy with IVUS-guided PCI in all-comer patients with significant coronary artery disease. A novel, standardised, QCA-based PCI protocol for the QCA-guided group will be provided to all participating operators, while the PCI optimisation criteria will be predefined for both strategies. A total of 1528 patients will be randomised to either group at a 1:1 ratio. The primary endpoint is the 12-month cumulative incidence of target-lesion failure defined as a composite of cardiac death, target-vessel myocardial infarction or ischaemia-driven target-lesion revascularisation. Clinical follow-up assessments are scheduled at 1, 6 and 12 months for all patients enrolled in the study. ETHICS AND DISSEMINATION: Ethics approval for this study was granted by the Institutional Review Board of Asan Medical Center (no. 2017-0060). Informed consent will be obtained from every participant. The study findings will be published in peer-reviewed journal articles and disseminated through public forums and academic conference presentations. Cost-effectiveness and secondary imaging analyses will be shared in secondary papers. TRIAL REGISTRATION NUMBER: NCT02978456.

Primary versus rescue retrograde approach for chronic total coronary occlusion.

OBJECTIVES: We aimed to assess the advantages of using the retrograde approach as an initial strategy rather than as a rescue strategy for complex chronic total occlusions (CTOs). BACKGROUND: Even for complex CTOs where a retrograde approach is deemed necessary, an antegrade approach is frequently used as an initial strategy in real-world practice. METHODS: We evaluated 352 retrograde procedures for CTO conducted at our high-volume center between January 2007 and January 2019. Procedural efficiency and safety was assessed based on the guidewire manipulation time (GWMT) and the occurrence of procedure-related adverse events for the primary retrograde approach (PRA) and the rescue retrograde approach (RRA). RESULTS: PRA and RRA were used in 191 (54.3%) and 161 (45.7%) of the CTO procedures, respectively. The complexity of the CTO lesion was significantly higher in the PRA group than in the RRA group (Japanese-CTO score, 2.62 ± 1.07 vs. 2.38 ± 1.06, p = 0.037). The technical success rate of two groups was similar (p = 0.47). The median GWMT required for PRA was significantly shorter than that for RRA (85 [interquartile range, 55-126] vs. 120 [85-157] min, p < 0.001). The total duration of the procedure and fluoroscopic time were shorter, and the number of guidewires and amount of contrast used during the index procedure were smaller in the PRA group. The incidence of procedure-related adverse events was not significantly different between the two groups. CONCLUSIONS: PRA showed higher procedural efficiency than RRA with comparable safety. Opting for PRA for complex CTOs might be a rational decision to enhance the procedural efficiency.

Correction: Feasibility of Virtual Reality Audiological Testing: Prospective Study.

[This corrects the article DOI: 10.2196/26976.].

Long-Term (7-Year) Clinical Implications of Newly Unveiled Asymptomatic Abnormal Ankle-Brachial Index in Patients With Coronary Artery Disease.

Background The long-term impact of newly discovered, asymptomatic abnormal ankle-brachial index (ABI) in patients with significant coronary artery disease is limited. Methods and Results Between January 2006 and December 2009, ABI was evaluated in 2424 consecutive patients with no history of claudication or peripheral artery disease who had significant coronary artery disease. We previously reported a 3-year result; therefore, the follow-up period was extended. The primary end point was a composite of all-cause death, myocardial infarction (MI), and stroke over 7 years. Of the 2424 patients with significant coronary artery disease, 385 had an abnormal ABI (ABI ≤0.9 or ≥1.4). During the follow-up period, the rate of the primary outcome was significantly higher in the abnormal ABI group than in the normal ABI group (P<0.001). The abnormal ABI group had a significantly higher risk of composite of all-cause death/MI/stroke than the normal ABI group, after adjustment with multivariable Cox proportional hazards regression analysis (hazard ratio [HR], 2.07; 95% CI, 1.67-2.57; P<0.001) and propensity score-matched analysis (HR, 1.97; 95% CI, 1.49-2.60; P<0.001). In addition, an abnormal ABI was associated with a higher risk of all-cause death, MI, and stroke, but not repeat revascularization. Conclusions Among patients with significant coronary artery disease, asymptomatic abnormal ABI was associated with sustained and increased incidence of composite of all-cause death/MI/stroke, all-cause death, MI, and stroke during extended follow-up over 7 years.