Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes

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

Development and External Validation of a Deep Learning Algorithm for Prognostication of Cardiovascular Outcomes

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

Evaluation of Atherosclerotic Plaque in Non-invasive Coronary Imaging

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.

Evaluation of Atherosclerotic Plaque in Non-invasive Coronary Imaging

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.

Multimodality Imaging in Coronary Artery Disease: Focus on Computed Tomography

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.

Physiologic Assessment of Coronary Artery Disease by Cardiac Computed Tomography

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.

Current Applications of Coronary and Cardiac Multidetector Computed Tomography

Cardiovascular disease remains the principal cause of death in the modernized world. Several novel noninvasive imaging techniques have been recently developed to improve diagnosis of cardiac and coronary disease. Of these advances, multidetector computed tomographic [MDCT] angiography has evolved most dramatically to transform computed tomography from a single-slice trans-axial modality to a three-dimensional volumetric technique. Current generation 64-detector row CT scanners allow for large volume coverage with submillimeter spatial and sub-second temporal resolution. These advances enable important new applications for MDCT in the assessment of cardiac and coronary anatomy. In this report, we discuss in depth potential appropriate uses of cardiac and coronary MDCT angiography