Coronary artery disease (CAD) remains the leading cause of death and morbidity worldwide. To date, diagnostic evaluation of patients with suspected CAD has relied upon the use of physiologic non-invasive testing by stress electrocardiography, echocardiography, myocardial perfusion imaging (MPI) and magnetic resonance imaging. Indeed, the importance of physiologic evaluation of CAD has been highlighted by large-scale randomized trials that demonstrate the propitious benefit of an integrated anatomic-physiologic evaluation method by performing lesion-specific ischemia assessment by fractional flow reserve (FFR)-widely considered the "gold" standard for ischemia assessment-at the time of invasive angiography. Coronary CT angiography (CCTA) has emerged as an attractive non-invasive test for anatomic illustration of the coronary arteries and atherosclerotic plaque. In a series of prospective multicenter trials, CCTA has been proven as having high diagnostic performance for stenosis detection as compared to invasive angiography. Nevertheless, CCTA evaluation of obstructive stenoses is prone to overestimation of severity and further, detection of stenoses by CCTA does not reliably determine the hemodynamic significance of the visualized lesions. Recently, a series of technological innovations have advanced the possibility of CCTA to enable physiologic evaluation of CAD, thereby creating the potential of this test to provide an integrated anatomic-physiologic assessment of CAD. These advances include rest-stress MPI by CCTA as well as the use of computational fluid dynamics to non-invasively calculate FFR from a typically acquired CCTA. The purpose of this review is to summarize the most recent data addressing these 2 physiologic methods of CAD evaluation by CCTA.
Angiography ; Cause of Death ; Constriction, Pathologic ; Coronary Artery Disease ; Coronary Vessels ; Echocardiography ; Electrocardiography ; Hemodynamics ; Humans ; Hydrodynamics ; Inventions ; Ischemia ; Magnetic Resonance Imaging ; Multicenter Studies as Topic ; Myocardial Perfusion Imaging ; Plaque, Atherosclerotic ; Prognosis

Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide. Over the last decade coronary computed tomography angiography (CCTA) has gained wide acceptance as a reliable, cost-effective and non-invasive modality for diagnosis and prognostication of CAD. Use of CCTA is now expanding to characterization of plaque morphology and identification of vulnerable plaque. Additionally, CCTA is developing as a non-invasive modality to monitor plaque progression, which holds future potential in individualizing treatment. In this review, we discuss the role of CCTA in diagnosis and management of CAD. Additionally, we discuss the recent advancements and the potential clinical applications of CCTA in management of CAD.
Angiography ; Atherosclerosis ; Coronary Artery Disease ; Diagnosis ; Mortality ; Plaque, Atherosclerotic

Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide. Over the last decade coronary computed tomography angiography (CCTA) has gained wide acceptance as a reliable, cost-effective and non-invasive modality for diagnosis and prognostication of CAD. Use of CCTA is now expanding to characterization of plaque morphology and identification of vulnerable plaque. Additionally, CCTA is developing as a non-invasive modality to monitor plaque progression, which holds future potential in individualizing treatment. In this review, we discuss the role of CCTA in diagnosis and management of CAD. Additionally, we discuss the recent advancements and the potential clinical applications of CCTA in management of CAD.

Coronary artery disease (CAD) is the leading cause of mortality worldwide, and various cardiovascular imaging modalities have been introduced for the purpose of diagnosing and determining the severity of CAD. More recently, advances in computed tomography (CT) technology have contributed to the widespread clinical application of cardiac CT for accurate and noninvasive evaluation of CAD. In this review, we focus on imaging assessment of CAD based upon CT, which includes coronary artery calcium screening, coronary CT angiography, myocardial CT perfusion, and fractional flow reserve CT. Further, we provide a discussion regarding the potential implications, benefits and limitations, as well as the possible future directions according to each modality.
Angiography ; Calcium ; Coronary Artery Disease* ; Coronary Vessels* ; Mass Screening ; Mortality ; Perfusion

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression.METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included.RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women).CONCLUSIONS: A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500
Adult ; Artificial Intelligence ; Cardiovascular Diseases ; Cohort Studies ; Female ; Follow-Up Studies ; Humans ; Insurance, Health ; Learning ; Male ; Mass Screening ; National Health Programs

BACKGROUND AND OBJECTIVES: We aim to explore the additional discriminative accuracy of a deep learning (DL) algorithm using repeated-measures data for identifying people at high risk for cardiovascular disease (CVD), compared to Cox hazard regression. METHODS: Two CVD prediction models were developed from National Health Insurance Service-Health Screening Cohort (NHIS-HEALS): a Cox regression model and a DL model. Performance of each model was assessed in the internal and 2 external validation cohorts in Koreans (National Health Insurance Service-National Sample Cohort; NHIS-NSC) and in Europeans (Rotterdam Study). A total of 412,030 adults in the NHIS-HEALS; 178,875 adults in the NHIS-NSC; and the 4,296 adults in Rotterdam Study were included. RESULTS: Mean ages was 52 years (46% women) and there were 25,777 events (6.3%) in NHIS-HEALS during the follow-up. In internal validation, the DL approach demonstrated a C-statistic of 0.896 (95% confidence interval, 0.886–0.907) in men and 0.921 (0.908–0.934) in women and improved reclassification compared with Cox regression (net reclassification index [NRI], 24.8% in men, 29.0% in women). In external validation with NHIS-NSC, DL demonstrated a C-statistic of 0.868 (0.860–0.876) in men and 0.889 (0.876–0.898) in women, and improved reclassification compared with Cox regression (NRI, 24.9% in men, 26.2% in women). In external validation applied to the Rotterdam Study, DL demonstrated a C-statistic of 0.860 (0.824–0.897) in men and 0.867 (0.830–0.903) in women, and improved reclassification compared with Cox regression (NRI, 36.9% in men, 31.8% in women). CONCLUSIONS A DL algorithm exhibited greater discriminative accuracy than Cox model approaches.TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02931500

Background: and Purpose Recent studies suggested an increased incidence of cerebral venous thrombosis (CVT) during the coronavirus disease 2019 (COVID-19) pandemic. We evaluated the volume of CVT hospitalization and in-hospital mortality during the 1st year of the COVID-19 pandemic compared to the preceding year. Methods: We conducted a cross-sectional retrospective study of 171 stroke centers from 49 countries. We recorded COVID-19 admission volumes, CVT hospitalization, and CVT in-hospital mortality from January 1, 2019, to May 31, 2021. CVT diagnoses were identified by International Classification of Disease-10 (ICD-10) codes or stroke databases. We additionally sought to compare the same metrics in the first 5 months of 2021 compared to the corresponding months in 2019 and 2020 (ClinicalTrials.gov Identifier: NCT04934020). Results: There were 2,313 CVT admissions across the 1-year pre-pandemic (2019) and pandemic year (2020); no differences in CVT volume or CVT mortality were observed. During the first 5 months of 2021, there was an increase in CVT volumes compared to 2019 (27.5%; 95% confidence interval [CI], 24.2 to 32.0; P<0.0001) and 2020 (41.4%; 95% CI, 37.0 to 46.0; P<0.0001). A COVID-19 diagnosis was present in 7.6% (132/1,738) of CVT hospitalizations. CVT was present in 0.04% (103/292,080) of COVID-19 hospitalizations. During the first pandemic year, CVT mortality was higher in patients who were COVID positive compared to COVID negative patients (8/53 [15.0%] vs. 41/910 [4.5%], P=0.004). There was an increase in CVT mortality during the first 5 months of pandemic years 2020 and 2021 compared to the first 5 months of the pre-pandemic year 2019 (2019 vs. 2020: 2.26% vs. 4.74%, P=0.05; 2019 vs. 2021: 2.26% vs. 4.99%, P=0.03). In the first 5 months of 2021, there were 26 cases of vaccine-induced immune thrombotic thrombocytopenia (VITT), resulting in six deaths. Conclusions During the 1st year of the COVID-19 pandemic, CVT hospitalization volume and CVT in-hospital mortality did not change compared to the prior year. COVID-19 diagnosis was associated with higher CVT in-hospital mortality. During the first 5 months of 2021, there was an increase in CVT hospitalization volume and increase in CVT-related mortality, partially attributable to VITT.