Impact of hypertriglyceridaemia on cardiovascular mortality according to low-density lipoprotein cholesterol in a 15.6-million population.

AIMS: The aim of this study was to assess the association between triglyceride (TG) levels and cardiovascular disease (CVD) mortality concerning low-density lipoprotein cholesterol (LDL-C) and age in the general population. METHODS AND RESULTS: From the Korean National Health Insurance Service database, 15 672 028 participants aged 18-99 who underwent routine health examinations were followed up for CVD mortality. Hazard ratios for CVD mortality were calculated using Cox models after adjusting for various confounders. During a mean of 8.8 years of follow-up, 105 174 individuals died of CVD. There was a clear log-linear association between TG and overall CVD mortality down to 50 mg/dL. Each two-fold increase in TG was associated with 1.10-fold (overall CVD), 1.22-fold [ischaemic heart disease (IHD)], 1.24-fold [acute myocardial infarction (AMI)], and 1.10-fold (ischaemic stroke) higher CVD mortality. Haemorrhagic stroke and heart failure were not associated with TG levels. The impact of hypertriglyceridaemia (HTG) on CVD weakened but remained present in persons with LDL-C < 100 mg/dL, in whom each two-fold higher TG was associated with 1.05-fold (overall CVD), 1.12-fold (IHD), 1.15-fold (AMI), and 1.05-fold (ischaemic stroke) higher CVD mortality. The younger population (18-44 years) had stronger associations between TG levels and mortality from overall CVD, IHD, and AMI than the older population. CONCLUSION: Hypertriglyceridaemia independently raises CVD mortality with lingering risks in young and older individuals with low LDL-C levels, suggesting the importance of management of HTG even with controlled LDL-C.

This prospective study evaluated the association between triglyceride (TG) levels and cardiovascular disease (CVD) mortality in the general population, particularly in individuals with well-controlled low-density lipoprotein cholesterol (LDL-C) levels. The TG levels log-linearly increased the mortality from CVD, especially ischaemic heart disease and ischaemic stroke, down to at least 50 mg/dL (0.56 mmol/L), as residual CVD risks associated with high TG were apparent in individuals, even with LDL-C < 100 mg/dL (2.59 mmol/L). Maintaining TG levels below 100 mg/dL may be beneficial even in seemingly low-risk groups, such as young people with normal or optimal LDL-C levels.

Identifying Coronary Artery Calcification Using Chest X-ray Radiographs and Machine Learning: The Role of the Radiomics Score.

PURPOSE: To evaluate the ability of radiomics score (RS)-based machine learning to identify moderate to severe coronary artery calcium (CAC) on chest x-ray radiographs (CXR). MATERIALS AND METHODS: We included 559 patients who underwent a CAC scan with CXR obtained within 6 months and divided them into training (n = 391) and validation (n = 168) cohorts. We extracted radiomic features from annotated cardiac contours in the CXR images and developed an RS through feature selection with the least absolute shrinkage and selection operator regression in the training cohort. We evaluated the incremental value of the RS in predicting CAC scores when combined with basic clinical factor in the validation cohort. To predict a CAC score ≥100, we built an RS-based machine learning model using random forest; the input variables were age, sex, body mass index, and RS. RESULTS: The RS was the most prominent factor for the CAC score ≥100 predictions (odds ratio = 2.33; 95% confidence interval: 1.62-3.44; P < 0.001) compared with basic clinical factor. The machine learning model was tested in the validation cohort and showed an area under the receiver operating characteristic curve of 0.808 (95% confidence interval: 0.75-0.87) for a CAC score ≥100 predictions. CONCLUSIONS: The use of an RS-based machine learning model may have the potential as an imaging marker to screen patients with moderate to severe CAC scores before diagnostic imaging tests, and it may improve the pretest probability of detecting coronary artery disease in clinical practice.

Relation of Gender to Atherosclerotic Plaque Characteristics by Differing Angiographic Stenosis Severity.

It is unknown whether gender influences the atherosclerotic plaque characteristics (APCs) of lesions of varying angiographic stenosis severity. This study evaluated the imaging data of 303 symptomatic patients from the derivation arm of the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial, all of whom underwent coronary computed tomographic angiography and clinically indicated nonemergent invasive coronary angiography upon study enrollment. Index tests were interpreted by 2 blinded core laboratories, one of which performed quantitative coronary computed tomographic angiography using an artificial intelligence application to characterize and quantify APCs, including percent atheroma volume (PAV), low-density noncalcified plaque (LD-NCP), noncalcified plaque (NCP), calcified plaque (CP), lesion length, positive arterial remodeling, and high-risk plaque (a combination of LD-NCP and positive remodeling ≥1.10); the other classified lesions as obstructive (≥50% diameter stenosis) or nonobstructive (<50% diameter stenosis) based on quantitative invasive coronary angiography. The relation between APCs and angiographic stenosis was further examined by gender. The mean age of the study cohort was 64.4 ± 10.2 years (29.0% female). In patients with obstructive disease, men had more LD-NCP PAV (0.5 ± 0.4 vs 0.3 ± 0.8, p = 0.03) and women had more CP PAV (11.7 ± 1.6 vs 8.0 ± 0.8, p = 0.04). Obstructive lesions had more NCP PAV compared with their nonobstructive lesions in both genders, however, obstructive lesions in women also demonstrated greater LD-NCP PAV (0.4 ± 0.5 vs 1.0 ± 1.8, p = 0.03), and CP PAV (17.4 ± 16.5 vs 25.9 ± 18.7, p = 0.03) than nonobstructive lesions. Comparing the composition of obstructive lesions by gender, women had more CP PAV (26.3 ± 3.4 vs 15.8 ± 1.5, p = 0.005) whereas men had more NCP PAV (33.0 ± 1.6 vs 26.7 ± 2.5, p = 0.04). Men had more LD-NCP PAV in nonobstructive lesions compared with women (1.2 ± 0.2 vs 0.6 ± 0.2, p = 0.02). In conclusion, there are gender-specific differences in plaque composition based on stenosis severity.

Impact of statins based on high-risk plaque features on coronary plaque progression in mild stenosis lesions: results from the PARADIGM study.

AIMS: To investigate the impact of statins on plaque progression according to high-risk coronary atherosclerotic plaque (HRP) features and to identify predictive factors for rapid plaque progression in mild coronary artery disease (CAD) using serial coronary computed tomography angiography (CCTA). METHODS AND RESULTS: We analyzed mild stenosis (25-49%) CAD, totaling 1432 lesions from 613 patients (mean age, 62.2 years, 63.9% male) and who underwent serial CCTA at a ≥2 year inter-scan interval using the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (NCT02803411) registry. The median inter-scan period was 3.5 ± 1.4 years; plaques were quantitatively assessed for annualized percent atheroma volume (PAV) and compositional plaque volume changes according to HRP features, and the rapid plaque progression was defined by the ≥90th percentile annual PAV. In mild stenotic lesions with ≥2 HRPs, statin therapy showed a 37% reduction in annual PAV (0.97 ± 2.02 vs. 1.55 ± 2.22, P = 0.038) with decreased necrotic core volume and increased dense calcium volume compared to non-statin recipient mild lesions. The key factors for rapid plaque progression were ≥2 HRPs [hazard ratio (HR), 1.89; 95% confidence interval (CI), 1.02-3.49; P = 0.042], current smoking (HR, 1.69; 95% CI 1.09-2.57; P = 0.017), and diabetes (HR, 1.55; 95% CI, 1.07-2.22; P = 0.020). CONCLUSION: In mild CAD, statin treatment reduced plaque progression, particularly in lesions with a higher number of HRP features, which was also a strong predictor of rapid plaque progression. Therefore, aggressive statin therapy might be needed even in mild CAD with higher HRPs. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov NCT02803411.

Atherosclerosis Imaging Quantitative Computed Tomography (AI-QCT) to guide referral to invasive coronary angiography in the randomized controlled CONSERVE trial.

AIMS: We compared diagnostic performance, costs, and association with major adverse cardiovascular events (MACE) of clinical coronary computed tomography angiography (CCTA) interpretation versus semiautomated approach that use artificial intelligence and machine learning for atherosclerosis imaging-quantitative computed tomography (AI-QCT) for patients being referred for nonemergent invasive coronary angiography (ICA). METHODS: CCTA data from individuals enrolled into the randomized controlled Computed Tomographic Angiography for Selective Cardiac Catheterization trial for an American College of Cardiology (ACC)/American Heart Association (AHA) guideline indication for ICA were analyzed. Site interpretation of CCTAs were compared to those analyzed by a cloud-based software (Cleerly, Inc.) that performs AI-QCT for stenosis determination, coronary vascular measurements and quantification and characterization of atherosclerotic plaque. CCTA interpretation and AI-QCT guided findings were related to MACE at 1-year follow-up. RESULTS: Seven hundred forty-seven stable patients (60 ± 12.2 years, 49% women) were included. Using AI-QCT, 9% of patients had no CAD compared with 34% for clinical CCTA interpretation. Application of AI-QCT to identify obstructive coronary stenosis at the ≥50% and ≥70% threshold would have reduced ICA by 87% and 95%, respectively. Clinical outcomes for patients without AI-QCT-identified obstructive stenosis was excellent; for 78% of patients with maximum stenosis < 50%, no cardiovascular death or acute myocardial infarction occurred. When applying an AI-QCT referral management approach to avoid ICA in patients with <50% or <70% stenosis, overall costs were reduced by 26% and 34%, respectively. CONCLUSIONS: In stable patients referred for ACC/AHA guideline-indicated nonemergent ICA, application of artificial intelligence and machine learning for AI-QCT can significantly reduce ICA rates and costs with no change in 1-year MACE.

Risk factors based vessel-specific prediction for stages of coronary artery disease using Bayesian quantile regression machine learning method: Results from the PARADIGM registry.

BACKGROUND AND HYPOTHESIS: The recently introduced Bayesian quantile regression (BQR) machine-learning method enables comprehensive analyzing the relationship among complex clinical variables. We analyzed the relationship between multiple cardiovascular (CV) risk factors and different stages of coronary artery disease (CAD) using the BQR model in a vessel-specific manner. METHODS: From the data of 1,463 patients obtained from the PARADIGM (NCT02803411) registry, we analyzed the lumen diameter stenosis (DS) of the three vessels: left anterior descending (LAD), left circumflex (LCx), and right coronary artery (RCA). Two models for predicting DS and DS changes were developed. Baseline CV risk factors, symptoms, and laboratory test results were used as the inputs. The conditional 10%, 25%, 50%, 75%, and 90% quantile functions of the maximum DS and DS change of the three vessels were estimated using the BQR model. RESULTS: The 90th percentiles of the DS of the three vessels and their maximum DS change were 41%-50% and 5.6%-7.3%, respectively. Typical anginal symptoms were associated with the highest quantile (90%) of DS in the LAD; diabetes with higher quantiles (75% and 90%) of DS in the LCx; dyslipidemia with the highest quantile (90%) of DS in the RCA; and shortness of breath showed some association with the LCx and RCA. Interestingly, High-density lipoprotein cholesterol showed a dynamic association along DS change in the per-patient analysis. CONCLUSIONS: This study demonstrates the clinical utility of the BQR model for evaluating the comprehensive relationship between risk factors and baseline-grade CAD and its progression.

Diabetes, Atherosclerosis, and Stenosis by AI.

OBJECTIVE: This study evaluates the relationship between atherosclerotic plaque characteristics (APCs) and angiographic stenosis severity in patients with and without diabetes. Whether APCs differ based on lesion severity and diabetes status is unknown. RESEARCH DESIGN AND METHODS: We retrospectively evaluated 303 subjects from the Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia (CREDENCE) trial referred for invasive coronary angiography with coronary computed tomographic angiography (CCTA) and classified lesions as obstructive (≥50% stenosed) or nonobstructive using blinded core laboratory analysis of quantitative coronary angiography. CCTA quantified APCs, including plaque volume (PV), calcified plaque (CP), noncalcified plaque (NCP), low-density NCP (LD-NCP), lesion length, positive remodeling (PR), high-risk plaque (HRP), and percentage of atheroma volume (PAV; PV normalized for vessel volume). The relationship between APCs, stenosis severity, and diabetes status was assessed. RESULTS: Among the 303 patients, 95 (31.4%) had diabetes. There were 117 lesions in the cohort with diabetes, 58.1% of which were obstructive. Patients with diabetes had greater plaque burden (P = 0.004). Patients with diabetes and nonobstructive disease had greater PV (P = 0.02), PAV (P = 0.02), NCP (P = 0.03), PAV NCP (P = 0.02), diseased vessels (P = 0.03), and maximum stenosis (P = 0.02) than patients without diabetes with nonobstructive disease. APCs were similar between patients with diabetes with nonobstructive disease and patients without diabetes with obstructive disease. Diabetes status did not affect HRP or PR. Patients with diabetes had similar APCs in obstructive and nonobstructive lesions. CONCLUSIONS: Patients with diabetes and nonobstructive stenosis had an association to similar APCs as patients without diabetes who had obstructive stenosis. Among patients with nonobstructive disease, patients with diabetes had more total PV and NCP.

AI Evaluation of Stenosis on Coronary CTA, Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve: A CREDENCE Trial Substudy.

BACKGROUND: Clinical reads of coronary computed tomography angiography (CTA), especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. Artificial intelligence (AI)-based solutions applied to coronary CTA may overcome these limitations. OBJECTIVES: This study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography (AI-QCT) angiography analyses to core lab-interpreted coronary CTA, core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).

Longitudinal Quantitative Assessment of Coronary Atherosclerotic Plaque Burden Related to Serum Hemoglobin Levels.

Background: Despite a potential role of hemoglobin in atherosclerosis, data on coronary plaque volume changes (PVC) related to serum hemoglobin levels are limited. Objectives: The authors sought to evaluate coronary atherosclerotic plaque burden changes related to serum hemoglobin levels using serial coronary computed tomographic angiography (CCTA). Methods: A total of 830 subjects (age 61 ± 10 years, 51.9% male) who underwent serial CCTA were analyzed. The median interscan period was 3.2 (IQR: 2.5-4.4) years. Quantitative assessment of coronary plaques was performed at both scans. All participants were stratified into 4 groups based on the quartile of baseline hemoglobin levels. Annualized total PVC (mm3/year) was defined as total PVC divided by the interscan period. Results: Baseline total plaque volume (mm3) was not different among all groups (group I [lowest]: 34.1 [IQR: 0.0-127.4] vs group II: 28.8 [IQR: 0.0-123.0] vs group III: 49.9 [IQR: 5.6-135.0] vs group IV [highest]: 34.3 [IQR: 0.0-130.7]; P = 0.235). During follow-up, serum hemoglobin level changes (Δ hemoglobin; per 1 g/dL) was related to annualized total PVC (ß = -0.114) in overall participants (P < 0.05). After adjusting for age, sex, traditional risk factors, baseline hemoglobin and creatinine levels, baseline total plaque volume, and the use of aspirin, beta-blocker, angiotensin-converting enzyme inhibitor or angiotensin receptor blocker, and statin, Δ hemoglobin significantly affected annualized total PVC in only the composite of groups I and II (ß = -2.401; P = 0.004). Conclusions: Serial CCTA findings suggest that Δ hemoglobin has an independent effect on coronary atherosclerosis. This effect might be influenced by baseline hemoglobin levels. (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging [PARADIGM]; NCT02803411).

Best cutoff score of cervical-pedicle thickness as a morphological parameter for predicting cervical central stenosis.

There are various factors for the cause of cervical central stenosis (CCS), such as osteophyte, cervical-disc degeneration, and cervical ligamentum flavum hypertrophy. However, the pedicle of the cervical vertebra has not yet been analyzed for its relationship with CCS. We created a new morphologic parameter called the cervical-pedicle thickness (CPT) to assess the association between CCS and the cervical pedicle. We obtained morphological cases involving the CPT from 82 patients with CCS. There were also 84 in the normal group who underwent cervical spine magnetic resonance imaging (CS-MR) as part of routine health screening. We obtained the T2-weighted CS-MR axial images from group members, and assessed the CPT at the level of the C6 vertebra on CS-MR. The mean CPT was 3.46 ± 0.57 mm in the normal group, 4.97 ± 0.75 mm in the CCS group, which thus had a significantly higher CPT (P < .01) than did the normal group. For the prognostic value of the CPT as a predictor of CCS, ROC analysis indicated that the best cutoff score for the CPT was 4.18 mm, with 93.9% sensitivity, 92.9% specificity, and AUC 0.97. Greater CPT was highly associated with a possibility of CCS. This conclusion will be helpful for assessing the CCS patients.

Longitudinal quantitative assessment of coronary atherosclerosis related to normal systolic blood pressure maintenance in the absence of established cardiovascular disease.

BACKGROUND: Atherosclerosis-related adverse events are commonly observed even in conditions with low cardiovascular (CV) risk. Longitudinal data regarding the association of normal systolic blood pressure maintenance (SBPmaintain ) with coronary plaque volume changes (PVC) has been limited in adults without traditional CV disease. HYPOTHESIS: Normal SBPmaintain is important to attenuate coronary atherosclerosis progression in adults without baseline CV disease. METHODS: We analyzed 95 adults (56.7 ± 8.5 years; 40.0% men) without baseline CV disease who underwent serial coronary computed tomographic angiography with mean 3.5 years of follow-up. All participants were divided into two groups of normal SBPmaintain (follow-up SBP < 120 mm Hg) and ≥elevated SBPmaintain (follow-up SBP ≥ 120 mm Hg). Annualized PVC was defined as PVC divided by the interscan period. RESULTS: Compared to participants with normal SBPmaintain , those with ≥elevated SBPmaintain had higher annualized total PVC (mm3 /year) (0.0 [0.0-2.2] vs. 4.1 [0.0-13.0]; p < .001). Baseline total plaque volume (ß = .10) and the levels of SBPmaintain (ß = .23) and follow-up high-density lipoprotein cholesterol (ß = -0.28) were associated with annualized total PVC (all p < .05). The optimal cutoff of SBPmaintain for predicting plaque progression was 118.5 mm Hg (sensitivity: 78.2%, specificity: 62.5%; area under curve: 0.700; 95% confidence interval [CI]: 0.59-0.81; p < .05). SBPmaintain ≥ 118.5 mm Hg (odds ratio [OR]: 4.03; 95% CI: 1.51-10.75) and baseline total plaque volume (OR: 1.03; 95% CI: 1.01-1.06) independently influenced coronary plaque progression (all p < .05). CONCLUSION: Normal SBPmaintain is substantial to attenuate coronary atherosclerosis progression in conditions without established CV disease.

Coronary CTA With AI-QCT Interpretation: Comparison With Myocardial Perfusion Imaging for Detection of Obstructive Stenosis Using Invasive Angiography as Reference Standard.

BACKGROUND. Deep learning frameworks have been applied to interpretation of coronary CTA performed for coronary artery disease (CAD) evaluation. OBJECTIVE. The purpose of our study was to compare the diagnostic performance of myocardial perfusion imaging (MPI) and coronary CTA with artificial intelligence quantitative CT (AI-QCT) interpretation for detection of obstructive CAD on invasive angiography and to assess the downstream impact of including coronary CTA with AI-QCT in diagnostic algorithms. METHODS. This study entailed a retrospective post hoc analysis of the derivation cohort of the prospective 23-center Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) trial. The study included 301 patients (88 women and 213 men; mean age, 64.4 ± 10.2 [SD] years) recruited from May 2014 to May 2017 with stable symptoms of myocardial ischemia referred for nonemergent invasive angiography. Patients underwent coronary CTA and MPI before angiography with quantitative coronary angiography (QCA) measurements and fractional flow reserve (FFR). CTA examinations were analyzed using an FDA-cleared cloud-based software platform that performs AI-QCT for stenosis determination. Diagnostic performance was evaluated. Diagnostic algorithms were compared. RESULTS. Among 102 patients with no ischemia on MPI, AI-QCT identified obstructive (≥ 50%) stenosis in 54% of patients, including severe (≥ 70%) stenosis in 20%. Among 199 patients with ischemia on MPI, AI-QCT identified nonobstructive (1-49%) stenosis in 23%. AI-QCT had significantly higher AUC (all p < .001) than MPI for predicting ≥ 50% stenosis by QCA (0.88 vs 0.66), ≥ 70% stenosis by QCA (0.92 vs 0.81), and FFR < 0.80 (0.90 vs 0.71). An AI-QCT result of ≥ 50% stenosis and ischemia on stress MPI had sensitivity of 95% versus 74% and specificity of 63% versus 43% for detecting ≥ 50% stenosis by QCA measurement. Compared with performing MPI in all patients and those showing ischemia undergoing invasive angiography, a scenario of performing coronary CTA with AIQCT in all patients and those showing ≥ 70% stenosis undergoing invasive angiography would reduce invasive angiography utilization by 39%; a scenario of performing MPI in all patients and those showing ischemia undergoing coronary CTA with AI-QCT and those with ≥ 70% stenosis on AI-QCT undergoing invasive angiography would reduce invasive angiography utilization by 49%. CONCLUSION. Coronary CTA with AI-QCT had higher diagnostic performance than MPI for detecting obstructive CAD. CLINICAL IMPACT. A diagnostic algorithm incorporating AI-QCT could substantially reduce unnecessary downstream invasive testing and costs. TRIAL REGISTRATION. Clinicaltrials.gov NCT02173275.

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography.

OBJECTIVES: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. BACKGROUND: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. METHODS: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). RESULTS: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had <50% stenosis in all vessel territories evaluated by QCA. Average AI analysis time was 10.3 ± 2.7 min. On a per vessel basis, there were significant differences only in sensitivity for ≥50% stenosis based on contrast type (iso-osmolar 70.0% vs non isoosmolar 92.1% p = 0.0345) and iodine concentration (<350 mg/ml 70.0%, 350-369 mg/ml 90.0%, 370-400 mg/ml 90.0%, >400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. CONCLUSION: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. CONDENSED ABSTRACT: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.

Association between low-density lipoprotein cholesterol and cardiovascular mortality in statin non-users: a prospective cohort study in 14.9 million Korean adults.

BACKGROUND: Limited information is available on detailed sex/age-specific associations between low-density lipoprotein cholesterol (LDL-C) and cardiovascular disease (CVD) mortality and 'the optimal range' associated with the lowest CVD mortality in the general population. METHODS: Korean adults (N = 14 884 975) who received routine health screenings during 2009-2010 were followed until 2018 for CVD mortality. RESULTS: During 8.8 years (mean) of follow-up, 94 344 individuals died from CVD. LDL-C had U-curve associations with mortality from CVD and its subtypes, except haemorrhagic stroke. Optimal range was 90-149 mg/dL for CVD; 70-114 for ischaemic heart disease; 85-129 for ischaemic stroke; ≥85 for subarachnoid haemorrhage; ≥130 for intracerebral haemorrhage; 115-159 for hypertension and heart failure; and 100-144 for sudden cardiac death. Assuming linear associations between 100 and 300 mg/dL, LDL-C was positively associated with CVD mortality [hazard ratio (HR) per 39-mg/dL (1-mmol/L) higher LDL-C = 1.10], largely due to ischaemic heart disease (HR = 1.26), followed by sudden cardiac death (HR = 1.13), ischaemic stroke (HR = 1.11) and heart failure (HR = 1.05). Intracerebral haemorrhage (HR = 0.90), but not subarachnoid haemorrhage, had inverse associations. Women and older adults had weaker positive associations than men and younger adults (Pinteraction < 0.001 for both sex and age). Individuals aged 75-84 years had modest positive associations with CVD mortality, especially ischaemic heart disease and ischaemic stroke. CONCLUSION: LDL-C had U-curve associations for CVD mortality. The associations and optimal ranges differed across CVD subtypes. Women and older adults had weaker positive associations than men and younger adults. Positive associations with ischaemic heart disease and ischaemic stroke were maintained in adults aged 75-84 years.

Fully automated quantification of cardiac chamber and function assessment in 2-D echocardiography: clinical feasibility of deep learning-based algorithms.

We aimed to compare the segmentation performance of the current prominent deep learning (DL) algorithms with ground-truth segmentations and to validate the reproducibility of the manually created 2D echocardiographic four cardiac chamber ground-truth annotation. Recently emerged DL based fully-automated chamber segmentation and function assessment methods have shown great potential for future application in aiding image acquisition, quantification, and suggestion for diagnosis. However, the performance of current DL algorithms have not previously been compared with each other. In addition, the reproducibility of ground-truth annotations which are the basis of these algorithms have not yet been fully validated. We retrospectively enrolled 500 consecutive patients who underwent transthoracic echocardiogram (TTE) from December 2019 to December 2020. Simple U-net, Res-U-net, and Dense-U-net algorithms were compared for the segmentation performances and clinical indices such as left atrial volume (LAV), left ventricular end diastolic volume (LVEDV), left ventricular end systolic volume (LVESV), LV mass, and ejection fraction (EF) were evaluated. The inter- and intra-observer variability analysis was performed by two expert sonographers for a randomly selected echocardiographic view in 100 patients (apical 2-chamber, apical 4-chamber, and parasternal short axis views). The overall performance of all DL methods was excellent [average dice similarity coefficient (DSC) 0.91 to 0.95 and average Intersection over union (IOU) 0.83 to 0.90], with the exception of LV wall area on PSAX view (average DSC of 0.83, IOU 0.72). In addition, there were no significant difference in clinical indices between ground truth and automated DL measurements. For inter- and intra-observer variability analysis, the overall intra observer reproducibility was excellent: LAV (ICC = 0.995), LVEDV (ICC = 0.996), LVESV (ICC = 0.997), LV mass (ICC = 0.991) and EF (ICC = 0.984). The inter-observer reproducibility was slightly lower as compared to intraobserver agreement: LAV (ICC = 0.976), LVEDV (ICC = 0.982), LVESV (ICC = 0.970), LV mass (ICC = 0.971), and EF (ICC = 0.899). The three current prominent DL-based fully automated methods are able to reliably perform four-chamber segmentation and quantification of clinical indices. Furthermore, we were able to validate the four cardiac chamber ground-truth annotation and demonstrate an overall excellent reproducibility, but still with some degree of inter-observer variability.

High-density lipoprotein cholesterol and cardiovascular mortality: a prospective cohort study among 15.8 million adults.

AIMS: We aimed to investigate whether the associations between high-density lipoprotein cholesterol (HDL-C) and cardiovascular disease (CVD) mortality and the optimal range differ by age and CVD subtypes. METHODS AND RESULTS: Korean adults (n = 15 859 501) with no CVD/cancer who received routine health examinations during 2009-2010 were followed until 2018 for CVD mortality. During a mean 8.8 years of follow-up, 108 123 individuals died from CVD. U-curve associations were found between HDL-C and CVD mortality, regardless of sex, age, and CVD subtype. The optimal range was 50-79 mg/dL (1.29-2.06 mmol/L), while it was 40-69 (1.03-1.80), 50-79 (1.29-2.06), and 60-89 (1.55-2.32) mg/dL (mmol/L) in adults aged <45 years, 45-64 years, and 65-99 years, respectively. Assuming linear associations <60 mg/dL, the multivariable-adjusted hazard ratios (HRs) per 39 mg/dL (1 mmol/L) higher level were 0.58 (95% confidence interval = 0.56-0.60), and they were 0.61 (0.52-0.72), 0.58 (0.54-0.62), and 0.59 (0.56-0.61) in individuals aged 18-44, 45-64, and 65-99 years, respectively [Pinteraction (age) = 0.845]. Assuming linear associations in the 60-150 mg/dL range, HDL-C was positively associated with CVD mortality (HR = 1.09, 1.04-1.14). The strongest association was for sudden cardiac death (SCD) (HR = 1.37), followed by heart failure (HF) (HR = 1.20) and intracerebral haemorrhage (ICH) (HR = 1.13). The HRs were 1.47 (1.23-1.76), 1.17 (1.08-1.28), and 1.03 (0.97-1.08) in individuals aged 18-44, 45-64, and 65-99 years, respectively [Pinteraction (age) < 0.001]. CONCLUSION: Both low and high levels of HDL-C were associated with increased mortality from CVD in the general population, especially SCD, HF, and ICH. High HDL-C levels are not necessarily a sign of good cardiovascular health, especially in younger adults.

Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence.

OBJECTIVE: The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). METHODS: This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years. RESULTS: The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm3 vs 48.2 mm3; p<0.04) and non-obstructive lesions (22.1 mm3 vs 49.4 mm3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. CONCLUSION: AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.

Atherogenic index of plasma and the risk of rapid progression of coronary atherosclerosis beyond traditional risk factors.

BACKGROUND AND AIMS: The atherogenic index of plasma (AIP) has been suggested as a marker of plasma atherogenicity. This study aimed to assess the association between AIP and the rapid progression of coronary atherosclerosis using serial coronary computed tomography angiography (CCTA). METHODS: A total of 1488 adults (60.9 ± 9.2 years, 58.9% male) who underwent serial CCTA with a median inter-scan period of 3.4 years were included. AIP was defined as the base 10 logarithm of the ratio of the concentrations of triglyceride to high-density lipoprotein cholesterol. Rapid plaque progression (RPP) was defined as the change of percentage atheroma volume (PAV) ≥1.0%/year. All participants were divided into three groups based on AIP tertiles. RESULTS: Baseline total PAV (median [interquartile range (IQR)]) (%) (group I [lowest]: 1.91 [0.00, 6.21] vs. group II: 2.82 [0.27, 8.83] vs. group III [highest]: 2.70 [0.41, 7.50]), the annual change of total PAV (median [IQR]) (%/year) (group I: 0.27 [0.00, 0.81] vs. group II: 0.37 [0.04, 1.11] vs. group III: 0.45 [0.06, 1.25]), and the incidence of RPP (group I: 19.7% vs. group II: 27.3% vs. group III: 31.4%) were significantly different among AIP tertiles (all p < 0.05). In multiple logistic regression analysis, the risk of RPP was increased in group III (odds ratio: 1.52, 95% confidence interval: 1.02-2.26; p = 0.042) compared to group I after adjusting for clinical factors and baseline total PAV. CONCLUSIONS: Based on serial CCTA findings, AIP is an independent predictive marker for RPP beyond traditional risk factors.

Assessment of Image Quality for Selective Intracoronary Contrast-Injected CT Angiography in a Hybrid Angio-CT System: A Feasibility Study in Swine.

PURPOSE: To compare image quality in selective intracoronary contrast-injected computed tomography angiography (Selective-CTA) with that in conventional intravenous contrast-injected CTA (IV-CTA). MATERIALS AND METHODS: Six pigs (35 to 40 kg) underwent both IV-CTA using an intravenous injection (60 mL) and Selective-CTA using an intracoronary injection (20 mL) through a guide-wire during/after percutaneous coronary intervention. Images of the common coronary artery were acquired. Scans were performed using a combined machine comprising an invasive coronary angiography suite and a 320-channel multi-slice CT scanner. Quantitative image quality parameters of CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), mean lumen diameter (MLD), and mean lumen area (MLA) were measured and compared. Qualitative analysis was performed using intraclass correlation coefficient (ICC), which was calculated for analysis of interobserver agreement. RESULTS: Quantitative image quality, determined by assessing the uniformity of CT attenuation (399.06 vs. 330.21, p<0.001), image noise (24.93 vs. 18.43, p<0.001), SNR (16.43 vs. 18.52, p=0.005), and CNR (11.56 vs. 13.46, p=0.002), differed significantly between IV-CTA and Selective-CTA. MLD and MLA showed no significant difference overall (2.38 vs. 2.44, p=0.068, 4.72 vs. 4.95, p=0.078). The density of contrast agent was significantly lower for selective-CTA (13.13 mg/mL) than for IV-CTA (400 mg/mL). Agreement between observers was acceptable (ICC=0.79±0.08). CONCLUSION: Our feasibility study in swine showed that compared to IV-CTA, Selective-CTA provides better image quality and requires less iodine contrast medium.