Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.

BACKGROUND: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples. METHODS: In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction. RESULTS: Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; P=0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume. CONCLUSIONS: Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.

Reproducibility of artificial intelligence-enabled plaque measurements between systolic and diastolic phases from coronary computed tomography angiography.

OBJECTIVES: Current coronary CT angiography (CTA) guidelines suggest both end-systolic and mid-diastolic phases of the cardiac cycle can be used for CTA image acquisition. However, whether differences in the phase of the cardiac cycle influence coronary plaque measurements is not known. We aim to explore the potential impact of cardiac phases on quantitative plaque assessment. METHODS: We enrolled 39 consecutive patients (23 male, age 66.2 ± 11.5 years) who underwent CTA with dual-source CT with visually evident coronary atherosclerosis and with good image quality. End-systolic and mid- to late-diastolic phase images were reconstructed from the same CTA scan. Quantitative plaque and stenosis were analyzed in both systolic and diastolic images using artificial intelligence (AI)-enabled plaque analysis software (Autoplaque). RESULTS: Overall, 186 lesions from 39 patients were analyzed. There were excellent agreement and correlation between systolic and diastolic images for all plaque volume measurements (Lin's concordance coefficient ranging from 0.97 to 0.99; R ranging from 0.96 to 0.98). There were no substantial intrascan differences per patient between systolic and diastolic phases (p > 0.05 for all) for total (1017.1 ± 712.9 mm3 vs. 1014.7 ± 696.2 mm3), non-calcified (861.5 ± 553.7 mm3 vs. 856.5 ± 528.7 mm3), calcified (155.7 ± 229.3 mm3 vs. 158.2 ± 232.4 mm3), and low-density non-calcified plaque volume (151.4 ± 106.1 mm3 vs. 151.5 ± 101.5 mm3) and diameter stenosis (42.5 ± 18.4% vs 41.3 ± 15.1%). CONCLUSION: Excellent agreement and no substantial differences were observed in AI-enabled quantitative plaque measurements on CTA in systolic and diastolic images. Following further validation, standardized plaque measurements can be performed from CTA in systolic or diastolic cardiac phase. CLINICAL RELEVANCE STATEMENT: Quantitative plaque assessment using artificial intelligence-enabled plaque analysis software can provide standardized plaque quantification, regardless of cardiac phase. KEY POINTS: • The impact of different cardiac phases on coronary plaque measurements is unknown. • Plaque analysis using artificial intelligence-enabled software on systolic and diastolic CT angiography images shows excellent agreement. • Quantitative coronary artery plaque assessment can be performed regardless of cardiac phase.

Comprehensive Myocardial Assessment by Computed Tomography: Impact on Short-Term Outcomes After Transcatheter Aortic Valve Replacement.

BACKGROUND: Quantification of myocardial changes in severe aortic stenosis (AS) is prognostically important. The potential for comprehensive myocardial assessment pre-transcatheter aortic valve replacement (TAVR) by computed tomography angiography (CTA) is unknown. OBJECTIVES: This study sought to evaluate whether quantification of left ventricular (LV) extracellular volume-a marker of myocardial fibrosis-and global longitudinal strain-a marker of myocardial deformation-at baseline CTA associate with post-TAVR outcomes. METHODS: Consecutive patients with symptomatic severe AS between January 2021 and June 2022 who underwent pre-TAVR CTA were included. Computed tomography extracellular volume (CT-ECV) was derived from septum tracing after generating the 3-dimensional CT-ECV map. Computed tomography global longitudinal strain (CT-GLS) used semi-automated feature tracking analysis. The clinical endpoint was the composite outcome of all-cause mortality and heart failure hospitalization. RESULTS: Among the 300 patients (80.0 ± 9.4 years of age, 45% female, median Society of Thoracic Surgeons Predicted Risk of Mortality score 2.80%), the left ventricular ejection fraction (LVEF) was 58% ± 12%, the median CT-ECV was 28.5% (IQR: 26.2%-32.1%), and the median CT-GLS was -20.1% (IQR: -23.8% to -16.3%). Over a median follow-up of 16 months (IQR: 12-22 months), 38 deaths and 70 composite outcomes occurred. Multivariable Cox proportional hazards model, accounting for clinical and echocardiographic variables, demonstrated that CT-ECV (HR: 1.09 [95% CI: 1.02-1.16]; P = 0.008) and CT-GLS (HR: 1.07 [95% CI: 1.01-1.13]; P = 0.017) associated with the composite outcome. In combination, elevated CT-ECV and CT-GLS (above median for each) showed a stronger association with the outcome (HR: 7.14 [95% CI: 2.63-19.36]; P < 0.001). CONCLUSIONS: Comprehensive myocardial quantification of CT-ECV and CT-GLS associated with post-TAVR outcomes in a contemporary low-risk cohort with mostly preserved LVEF. Whether these imaging biomarkers can be potentially used for the decision making including timing of AS intervention and post-TAVR follow-up will require integration into future clinical trials.

Importance of imaging-acquisition protocol and post-processing analysis for extracellular volume fraction assessment by computed tomography.

BACKGROUND: Computed tomography angiography (CTA) assessment of myocardial extracellular volume fraction (CT-ECV) is feasible, although the protocols for imaging acquisition and post-processing methodology have varied. We aimed to identify a pragmatic protocol for CT-ECV assessment encompassing both imaging acquisition and post-processing methodologies to facilitate its clinical implementation. METHODS: We evaluated consecutive patients with severe aortic stenosis undergoing evaluation for transcatheter aortic valve replacement (TAVR). Pre-contrast and 3-min-delayed CTA were obtained in systole using either helical prospective-ECG-triggered (high-pitch) or axial sequential-ECG-gated acquisition, adding to standard TAVR CTA protocol. Using a dedicated software for co-registration of CTA datasets, three methodologies for ECV measurement were evaluated: (1) mid-septum region of interest (Septal ECV), (2) averaged-global ECV (Global ECV) encompassing 16-AHA segments, and (3) average of septal and lateral segments (Averaged ECVsep and Averaged ECVlat). RESULTS: Among the 142 patients enrolled (median â€‹= â€‹81 years, 44% females), 8 were excluded due to significant imaging artifacts precluding Global ECV assessment. High-pitch scan mode was performed in 68 patients (48%). Suboptimal image quality for Global ECV assessment was associated with high-pitch scan mode (odds ratio: OR â€‹= â€‹2.26, p â€‹= â€‹0.036), along with the presence of intracardiac leads (OR â€‹= â€‹4.91, p â€‹= â€‹0.002), and BMI≥35 â€‹kg/m2 (OR â€‹= â€‹2.80, p â€‹= â€‹0.026). Septal ECV [median â€‹= â€‹29.4%] and Averaged ECVsep [29.0%] were similar (p â€‹= â€‹0.108), while Averaged ECVlat [27.5%] was lower than Averaged ECVsep (p â€‹< â€‹0.001), resulting in lower Global ECV [28.6%]. CONCLUSIONS: Myocardial CT-ECV assessment is feasible using a systolic sequential acquisition pre-contrast, and similar additional 3-min delayed scan. Septal ECV measurement provides similar values to Global ECV and is equally reproducible.

Feasibility and Potential Effectiveness of a Smartphone Zero-Time Exercise Intervention for Promoting Physical Activity and Fitness in Patients With Coronary Heart Disease: A Pilot Randomized Controlled Trial.

Background: Zero-time Exercise (ZTEx), a simple strength- and stamina-enhancing physical activity (PA) requiring no extra equipment, can potentially increase PA and fitness. This pilot trial examined the feasibility and potential effectiveness of a smartphone ZTEx intervention to promote PA and fitness in patients with coronary heart disease (CHD). Methods: A parallel-group assessor-blinded pilot randomized controlled trial was conducted on Chinese patients with stable coronary heart disease (CHD) in three cardiology clinics. The experimental group received a 15-min brief individual face-to-face session and a 12-week ZTEx instant messaging with 28 picture e-messages and a smartphone ZTEx application (ZTExApp). The control group received the same duration of individual session and number and format of e-messages, but the content was healthy eating and breathing exercise. The feasibility was assessed based on: attrition rate, usage, response rate and perception of the intervention. The outcome evaluation included primary outcome (PA), fitness, exercise self-efficacy and intention, perceived happiness and health, and quality of life. A linear mixed model was used with intention-to-treat analysis adjusting for sex, age and baseline values. A semi-structured interview was conducted to collect feedback from the experiment group. Results: One hundred thirty-nine patients (mean age 59.8 ± 6.6; 71.2% male) were randomized to the experimental group (n = 70) or control group (n = 69), and 80% (56/70) and 82% (57/69) of patients completed the 12-week follow-up assessment, respectively. The attrition rate was 18.7%. The experimental group reported that ZTEx was feasible to integrate PA into their daily life and appreciated the picture e-messages, and 95% of them sent feedback to us, but only 19.6% (13/70) of the participants entered their PA information into the e-diary of the ZTExApp. The experimental group had a significantly greater increase in time spent walking [mean difference (95% CI): 155.3 (10.1, 300.4), P = 0.04, Cohen's d = 0.34] than the control group. Conclusions: This pilot study showed using a brief ZTEx face-to-face session with picture e-messages empowered patients with CHD to integrate PA into daily life. Future definitive trials with a longer follow-up and a more user-friendly ZTExApp interface are necessary to determine the effectiveness of the smartphone ZTEx intervention in enhancing PA and related outcomes. Trial Registration: The research protocol was registered at the Hong Kong University Clinical Trials Registry (HKUCTR) on 22 Jul 2016 (Study identifier: HKUCTR-2165) and was also retrospectively registered at the National Institutes of Health (identifier number: NCT03464331) on 14 March 2018.

Prediction of revascularization by coronary CT angiography using a machine learning ischemia risk score.

OBJECTIVES: The machine learning ischemia risk score (ML-IRS) is a machine learning-based algorithm designed to identify hemodynamically significant coronary disease using quantitative coronary computed tomography angiography (CCTA). The purpose of this study was to examine whether the ML-IRS can predict revascularization in patients referred for invasive coronary angiography (ICA) after CCTA. METHODS: This study was a post hoc analysis of a prospective dual-center registry of sequential patients undergoing CCTA followed by ICA within 3 months, referred from inpatient, outpatient, and emergency department settings (n = 352, age 63 ± 10 years, 68% male). The primary outcome was revascularization by either percutaneous coronary revascularization or coronary artery bypass grafting. Blinded readers performed semi-automated quantitative coronary plaque analysis. The ML-IRS was automatically computed. Relationships between clinical risk factors, coronary plaque features, and ML-IRS with revascularization were examined. RESULTS: The study cohort consisted of 352 subjects with 1056 analyzable vessels. The ML-IRS ranged between 0 and 81% with a median of 18.7% (6.4-34.8). Revascularization was performed in 26% of vessels. Vessels receiving revascularization had higher ML-IRS (33.6% (21.1-55.0) versus 13.0% (4.5-29.1), p < 0.0001), as well as higher contrast density difference, and total, non-calcified, calcified, and low-density plaque burden. ML-IRS, when added to a traditional risk model based on clinical data and stenosis to predict revascularization, resulted in increased area under the curve from 0.69 (95% CI: 0.65-0.72) to 0.78 (95% CI: 0.75-0.81) (p < 0.0001), with an overall continuous net reclassification improvement of 0.636 (95% CI: 0.503-0.769; p < 0.0001). CONCLUSIONS: ML-IRS from quantitative coronary CT angiography improved the prediction of future revascularization and can potentially identify patients likely to receive revascularization if referred to cardiac catheterization. KEY POINTS: • Machine learning ischemia risk from quantitative coronary CT angiography was significantly higher in patients who received revascularization versus those who did not receive revascularization. • The machine learning ischemia risk score was significantly higher in patients with invasive fractional flow ≤ 0.8 versus those with > 0.8. • The machine learning ischemia risk score improved the prediction of future revascularization significantly when added to a standard prediction model including stenosis.

Quantitative global plaque characteristics from coronary computed tomography angiography for the prediction of future cardiac mortality during long-term follow-up.

AIMS: Adverse plaque characteristics determined by coronary computed tomography angiography (CTA) have been associated with future cardiac events. Our aim was to investigate whether quantitative global per-patient plaque characteristics from coronary CTA can predict subsequent cardiac death during long-term follow-up. METHODS AND RESULTS: Out of 2748 patients without prior history of coronary artery disease undergoing CTA with dual-source CT, 32 patients suffered cardiac death (mean follow-up of 5 ± 2 years). These patients were matched to 32 controls by age, gender, risk factors, and symptoms (total 64 patients, 59% male, age 69 ± 10 years). Coronary CTA data sets were analysed by semi-automated software to quantify plaque characteristics over the entire coronary tree, including total plaque volume, volumes of non-calcified plaque (NCP), low-density non-calcified plaque (LD-NCP, attenuation <30 Hounsfield units), calcified plaque (CP), and corresponding burden (plaque volume × 100%/vessel volume), as well as stenosis and contrast density difference (CDD, maximum percent difference in luminal attenuation/cross-sectional area compared to proximal cross-section). In patients who died from cardiac cause, NCP, LD-NCP, CP and total plaque volumes, quantitative stenosis, and CDD were significantly increased compared to controls (P < 0.025 for all). NCP > 146 mm³ [hazards ratio (HR) 2.24; 1.09-4.58; P = 0.027], LD-NCP > 10.6 mm³ (HR 2.26; 1.11-4.63; P = 0.025), total plaque volume > 179 mm³ (HR 2.30; 1.12-4.71; P = 0.022), and CDD > 35% in any vessel (HR 2.85;1.4-5.9; P = 0.005) were associated with increased risk of future cardiac death, when adjusted for segment involvement score. CONCLUSION: Among quantitative global plaque characteristics, total, non-calcified, and low-density plaque volumes as well as CDD predict cardiac death in long-term follow-up.

SYNTAX Score Derived From Coronary CT Angiography for Prediction of Complex Percutaneous Coronary Interventions.

RATIONALE AND OBJECTIVES: SYNTAX score is a useful metric determined at the time of invasive coronary angiography (ICA) to assess the complexity of coronary artery disease, and improves prediction of complications at the time of percutaneous complex intervention (PCI). We aimed to determine whether SYNTAX score can be reliably determined from coronary computed tomography angiography (CCTA) and whether a CCTA-derived SYNTAX score can predict complex PCI. MATERIALS AND METHODS: SYNTAX scores were calculated on per-patient, per-vessel, and per-segment basis in 154 consecutive patients who underwent CCTA and ICA. PCI complexity in 113 patients who underwent intervention was defined by total fluoroscopy time and contrast volume. RESULTS: Compared to ICA, CCTA detected 285 of 302 (94%) obstructive lesions in 230 vessels, for which PCI was performed for 154 lesions in 131 vessels. Overall, on a per-patient basis, ICA-derived SYNTAX score was lower in comparison to CCTA-derived score (10.2 ± 8.0 vs 10.9 ± 8.3, P = 0.001). As compared to lesions in the lowest CCTA-derived segmental SYNTAX tertile, lesions in the highest tertile required longer fluoroscopy time (17.5 ± 12 min vs 11.5 ± 7.9 min, P = 0.01) and greater contrast volume (215.4 ± 125.5 mL vs 144.3 ± 49 mL, P = 0.02). CONCLUSION: SYNTAX scores derived from CCTA are concordant with those derived from ICA and correspond with complex PCI.

Dyspnea predicts mortality among patients undergoing coronary computed tomographic angiography.

The prognostic implications of dyspnea and typical angina in patients referred for coronary CT angiography have not been examined. We examined features associated with incident mortality risk among individuals undergoing coronary computed tomographic angiography (CCTA) presenting with dyspnea, typical angina, and neither of these symptoms. 1147 consecutive individuals without known CAD (mean 61 years, 61.6 %men) undergoing CCTA comprised the study population 132 with dyspnea, 218 with typical angina, and 797 without dyspnea or typical angina (reference group). Mortality risk in relation to dyspnea or typical angina was evaluated with multivariable Cox proportional hazards models compared to reference. In addition, the prognosis associated with dyspnea or typical angina was assessed among age matched subgroups. Patients with dyspnea had a greater prevalence of C70 % stenosis (p\0.001) and coronary segments with plaque (p = 0.02) compared to the other two groups. During a follow-up of 3.1 years, 52 individuals died. By multivariable Cox models, compared to patients in reference group, dyspnea patients experienced higher mortality (HR 2.0, 95 % CI 1.0­4.0, p = 0.049) while typical angina patients did not (HR 1.1, 95 % CI 0.6­2.3, p = 0.76). In the matched group, the patients with dyspnea (HR 2.2, 95 % CI 1.1­4.3, p = 0.03) still had significantly reduced survival compared to the other two groups, while those with typical angina did not (HR 1.2, 95 % CI 0.6­2.6,p = 0.62). Dyspnea is associated with increased mortality ate compared to patients with typical angina and those with neither of these symptoms among patients undergoing CCTA.

A clinical model to identify patients with high-risk coronary artery disease.

OBJECTIVES: This study sought to develop a clinical model that identifies patients with and without high-risk coronary artery disease (CAD). BACKGROUND: Although current clinical models help to estimate a patient's pre-test probability of obstructive CAD, they do not accurately identify those patients with and without high-risk coronary anatomy. METHODS: Retrospective analysis of a prospectively collected multinational coronary computed tomographic angiography (CTA) cohort was conducted. High-risk anatomy was defined as left main diameter stenosis ≥50%, 3-vessel disease with diameter stenosis ≥70%, or 2-vessel disease involving the proximal left anterior descending artery. Using a cohort of 27,125, patients with a history of CAD, cardiac transplantation, and congenital heart disease were excluded. The model was derived from 24,251 consecutive patients in the derivation cohort and an additional 7,333 nonoverlapping patients in the validation cohort. RESULTS: The risk score consisted of 9 variables: age, sex, diabetes, hypertension, current smoking, hyperlipidemia, family history of CAD, history of peripheral vascular disease, and chest pain symptoms. Patients were divided into 3 risk categories: low (≤7 points), intermediate (8 to 17 points) and high (≥18 points). The model was statistically robust with area under the curve of 0.76 (95% confidence interval [CI]: 0.75 to 0.78) in the derivation cohort and 0.71 (95% CI: 0.69 to 0.74) in the validation cohort. Patients who scored ≤7 points had a low negative likelihood ratio (<0.1), whereas patients who scored ≥18 points had a high specificity of 99.3% and a positive likelihood ratio (8.48). In the validation group, the prevalence of high-risk CAD was 1% in patients with ≤7 points and 16.7% in those with ≥18 points. CONCLUSIONS: We propose a scoring system, based on clinical variables, that can be used to identify patients at high and low pre-test probability of having high-risk CAD. Identification of these populations may detect those who may benefit from a trial of medical therapy and those who may benefit most from an invasive strategy.

Current but not past smoking increases the risk of cardiac events: insights from coronary computed tomographic angiography.

AIMS: We evaluated coronary artery disease (CAD) extent, severity, and major adverse cardiac events (MACEs) in never, past, and current smokers undergoing coronary CT angiography (CCTA). METHODS AND RESULTS: We evaluated 9456 patients (57.1 ± 12.3 years, 55.5% male) without known CAD (1588 current smokers; 2183 past smokers who quit ≥3 months before CCTA; and 5685 never smokers). By risk-adjusted Cox proportional-hazards models, we related smoking status to MACE (all-cause death or non-fatal myocardial infarction). We further performed 1:1:1 propensity matching for 1000 in each group evaluate event risk among individuals with similar age, gender, CAD risk factors, and symptom presentation. During a mean follow-up of 2.8 ± 1.9 years, 297 MACE occurred. Compared with never smokers, current and past smokers had greater atherosclerotic burden including extent of plaque defined as segments with any plaque (2.1 ± 2.8 vs. 2.6 ± 3.2 vs. 3.1 ± 3.3, P < 0.0001) and prevalence of obstructive CAD [1-vessel disease (VD): 10.6% vs. 14.9% vs. 15.2%, P < 0.001; 2-VD: 4.4% vs. 6.1% vs. 6.2%, P = 0.001; 3-VD: 3.1% vs. 5.2% vs. 4.3%, P < 0.001]. Compared with never smokers, current smokers experienced higher MACE risk [hazard ratio (HR) 1.9, 95% confidence interval (CI) 1.4-2.6, P < 0.001], while past smokers did not (HR 1.2, 95% CI 0.8-1.6, P = 0.35). Among matched individuals, current smokers had higher MACE risk (HR 2.6, 95% CI 1.6-4.2, P < 0.001), while past smokers did not (HR 1.3, 95% CI 0.7-2.4, P = 0.39). Similar findings were observed for risk of all-cause death. CONCLUSION: Among patients without known CAD undergoing CCTA, current and past smokers had increased burden of atherosclerosis compared with never smokers; however, risk of MACE was heightened only in current smokers.

Prognostic and therapeutic implications of statin and aspirin therapy in individuals with nonobstructive coronary artery disease: results from the CONFIRM (COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry) registry.

OBJECTIVE: We sought to examine the risk of mortality associated with nonobstructive coronary artery disease (CAD) and to determine the impact of baseline statin and aspirin use on mortality. APPROACH AND RESULTS: Coronary computed tomographic angiography permits direct visualization of nonobstructive CAD. To date, the prognostic implications of nonobstructive CAD and the potential benefit of directing therapy based on nonobstructive CAD have not been carefully examined. A total of 27 125 consecutive patients who underwent computed tomographic angiography (12 enrolling centers and 6 countries) were prospectively entered into the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter (CONFIRM) registry. Patients, without history of previous CAD or obstructive CAD, for whom baseline statin and aspirin use was available were analyzed. Each coronary segment was classified as normal or nonobstructive CAD (1%-49% stenosis). Patients were followed up for a median of 27.2 months for all-cause mortality. The study comprised 10 418 patients (5712 normal and 4706 with nonobstructive CAD). In multivariable analyses, patients with nonobstructive CAD had a 6% (95% confidence interval, 1%-12%) higher risk of mortality for each additional segment with nonobstructive plaque (P=0.021). Baseline statin use was associated with a reduced risk of mortality (hazard ratio, 0.44; 95% confidence interval, 0.28-0.68; P=0.0003), a benefit that was present for individuals with nonobstructive CAD (hazard ratio, 0.32; 95% confidence interval, 0.19-0.55; P<0.001) but not for those without plaque (hazard ratio, 0.66; 95% confidence interval, 0.30-1.43; P=0.287). When stratified by National Cholesterol Education Program/Adult Treatment Program III, no mortality benefit was observed in individuals without plaque. Aspirin use was not associated with mortality benefit, irrespective of the status of plaque. CONCLUSIONS: The presence and extent of nonobstructive CAD predicted mortality. Baseline statin therapy was associated with a significant reduction in mortality for individuals with nonobstructive CAD but not for individuals without CAD. CLINICAL TRIAL REGISTRATION: URL: http://clinicaltrials.gov/. Unique identifier NCT01443637.

Gender differences in the prevalence, severity, and composition of coronary artery disease in the young: a study of 1635 individuals undergoing coronary CT angiography from the prospective, multinational confirm registry.

OBJECTIVE: Prior studies examining coronary atherosclerosis in the young have been limited by retrospective analyses in small cohorts. We examined the relationship between cardiovascular risk factors (RFs) and prevalence and severity of coronary atherosclerosis in a large, prospective, multinational registry of consecutive young individuals undergoing coronary computerized tomographic angiography (CCTA). METHOD AND RESULTS: Of 27 125 patients undergoing CCTA, 1635 young (<45 years) individuals without known coronary artery disease (CAD) or coronary anomalies were identified. Coronary plaque was assessed for any CAD, obstructive CAD (≥50% stenosis), and presence of calcified plaque (CP) and non-calcified plaque (NCP). Among 1635 subjects (70% men, age 38 ± 6 years), any CAD, obstructive CAD, CP, and NCP were observed in 19, 4, 5, and 8%, respectively. Compared with women, men demonstrated higher rates of any CAD (21 vs. 12%, P < 0.001), CP (6 vs. 3%, P = 0.01), and NCP (9 vs. 5%, P = 0.008), although no difference was observed for rates of obstructive CAD (5 vs. 4%, P = 0.46). Any CAD, obstructive CAD, and NCP were higher for young individuals with diabetes, hypertension, dyslipidaemia, current smoking, or family history of CAD; while only diabetes and dyslipidaemia were associated with CP. Increasing cardiovascular RFs was associated with a greater prevalence and extent and severity of CAD, with individuals with 0, 1, 2, ≥3 RFs manifesting a dose-response increase in any CAD (P < 0.001, for trend), obstructive CAD (P < 0.001, for trend), NCP (P < 0.001, for trend), and CP (P < 0.001, for trend). In multivariable analysis adjusting for sex and cardiovascular RFs, male sex was the strongest predictor for any CAD (odds ratio [OR] = 1.95, 95% confidence interval [CI] = 1.43-2.66, P < 0.001), CP (OR = 1.46, 95% CI = 1.08-1.98, P = 0.01), and NCP (OR = 1.33, 95% CI = 1.06-1.67, P = 0.01); family history of CAD was the strongest predictor for obstructive CAD (OR = 2.71, 95% CI = 1.65-4.45, P < 0.001). CONCLUSION: Any and obstructive CAD is present in 1 in 5 and 1 in 20 young individuals, respectively, with family history associated with the greatest risk of obstructive CAD.

Incremental value of diagonal earlobe crease to the Diamond-Forrester classification in estimating the probability of significant coronary artery disease determined by computed tomographic angiography.

The Diamond-Forrester (DF) algorithm overestimates the likelihood of significant coronary artery disease (≥50% stenosis, CAD50). The aim of the present study was to evaluate whether the addition of a diagonal earlobe crease (DELC) enhances the predictive ability of DF to detect CAD50 by coronary computed tomographic angiography (CTA). We evaluated 430 patients referred for CTA for symptoms, cardiovascular risk factors, and CAD50 likelihood using DF. Observers blinded to CTA findings evaluated the presence of DELC. The diagnostic accuracy and relation of DF, DELC, and DF + DELC for predicting CAD50 in patients with chest pain were evaluated using receiver operating characteristics curve (area under curve) analyses and multivariate logistic regression analyses. In 199 patients with chest pain, the sensitivity and specificity for CAD50 were 96% and 20% for DF (AUC 0.59, p = 0.59), 91% and 32% for DELC (AUC 0.62, p = 0.03), and 91% and 41% for DF + DELC (AUC 0.66, p = 0.004). On multivariate analyses DELC was the only independent predictor of CAD50 (odds ratio 3.6, 95% confidence interval 1 to 12.9, p = 0.048). DF + DELC increased the predictive ability to detect CAD50 above cardiovascular risk factors (odds ratio 5.6, 95% confidence interval 1.6 to 19.8, p = 0.007). In patients with chest pain, the presence of DELC is related to CAD50 beyond DF. A combined variable of DF + DELC provides superior discriminatory ability for detecting CAD50 than either method alone.

Left ventricular function and volume with coronary CT angiography improves risk stratification and identification of patients at risk for incident mortality: results from 7758 patients in the prospective multinational CONFIRM observational cohort study.

PURPOSE: To assess whether gradations of left ventricular (LV) ejection fraction (LVEF) and volumes measured with coronary computed tomography (CT) would augment risk stratification and discrimination for incident mortality. MATERIALS AND METHODS: This study was approved by the institutional review board, and informed consent was obtained when required. Subjects without known coronary artery disease (CAD) who underwent cardiac CT angiography with quantitative LV measurements were categorized according to LVEF (≥ 55%, 45%-54.9%, 35%-44.9%, or <35%). LV end-systolic volume (LVESV) and LV end-diastolic volume (LVEDV) were classified as normal (≥ 90 mL) or abnormal (≥ 200 mL). CAD extent and severity was categorized as none, nonobstructive, obstructive (≥ 50%), one-vessel, two-vessel, and three-vessel or left main disease. LVEF and volumes were assessed for risk prediction and discrimination of future mortality by using Cox hazards model and receiver operating characteristic curve analysis, respectively. RESULTS: During a follow-up of 2.0 years ± 0.9, 7758 patients (mean age, 58.5 years ± 13.0; 4220 male patients [54.4%]) were studied. At multivariable analysis, worsening LVEF was independently associated with mortality for moderately (hazard ratio = 3.14, P < .001) and severely (hazard ratio = 5.19, P < .001) abnormal ejection fraction. LVEF demonstrated improved discrimination for mortality (Az = 0.816) when compared with CAD risk factors alone (Az = 0.781) or CAD risk factors plus extent and severity. At multivariable analysis of a subgroup of 3706 individuals, abnormal LVEDV (hazard ratio = 4.02) and LVESV (hazard ratio = 6.46) helped predict mortality (P < .001). Similarly, LVESV and LVEDV demonstrated improved discrimination when compared with CAD risk factors or CAD extent and severity (P < .05). CONCLUSION: LV dysfunction and volumes measured with cardiac CT angiography augment risk prediction and discrimination for future mortality.