Diabetes and male sex are key risk factor correlates of the extent of coronary artery calcification: A Euro-CCAD study.

Background and aimsAlthough much has been written about the conventional cardiovascular risk factor correlates of the extent of coronary artery calcification (CAC), few studies have been carried out on symptomatic patients. This paper assesses the potential ability of risk factors to associate with an increasing CAC score.MethodsFrom the European Calcific Coronary Artery Disease (Euro-CCAD) cohort, we retrospectively investigated 6309 symptomatic patients, 62% male, from Denmark, France, Germany, Italy, Spain and the USA. All had conventional cardiovascular risk factor assessment and CT scanning for CAC scoring.ResultsAmong all patients, male sex (OR = 4.85, p<0.001) and diabetes (OR = 2.36, p<0.001) were the most important risk factors of CAC extent, with age, hypertension, dyslipidemia and smoking also showing a relationship. Among patients with CAC, age, diabetes, hypertension and dyslipidemia were associated with an increasing CAC score in males and females, with diabetes being the strongest dichotomous risk factor (p<0.001 for both). These results were echoed in quantile regression, where diabetes was consistently the most important correlate with CAC extent in every quantile in both males and females. To a lesser extent, hypertension and dyslipidemia were also associated in the high CAC quantiles and the low CAC quantiles respectively.ConclusionIn addition to age and male sex in the total population, diabetes is the most important correlate of CAC extent in both sexes

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P<0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

Clinical risk factors and atherosclerotic plaque extent to define risk for major events in patients without obstructive coronary artery disease: the long-term coronary computed tomography angiography CONFIRM registry.

AimsIn patients without obstructive coronary artery disease (CAD), we examined the prognostic value of risk factors and atherosclerotic extent.Methods and resultsPatients from the long-term CONFIRM registry without prior CAD and without obstructive (≥50%) stenosis were included. Within the groups of normal coronary computed tomography angiography (CCTA) (N = 1849) and non-obstructive CAD (N = 1698), the prognostic value of traditional clinical risk factors and atherosclerotic extent (segment involvement score, SIS) was assessed with Cox models. Major adverse cardiac events (MACE) were defined as all-cause mortality, non-fatal myocardial infarction, or late revascularization. In total, 3547 patients were included (age 57.9 ± 12.1 years, 57.8% male), experiencing 460 MACE during 5.4 years of follow-up. Age, body mass index, hypertension, and diabetes were the clinical variables associated with increased MACE risk, but the magnitude of risk was higher for CCTA defined atherosclerotic extent; adjusted hazard ratio (HR) for SIS >5 was 3.4 (95% confidence interval [CI] 2.3-4.9) while HR for diabetes and hypertension were 1.7 (95% CI 1.3-2.2) and 1.4 (95% CI 1.1-1.7), respectively. Exclusion of revascularization as endpoint did not modify the results. In normal CCTA, presence of ≥1 traditional risk factors did not worsen prognosis (log-rank P = 0.248), while it did in non-obstructive CAD (log-rank P = 0.025). Adjusted for SIS, hypertension and diabetes predicted MACE risk in non-obstructive CAD, while diabetes did not increase risk in absence of CAD (P-interaction = 0.004).ConclusionAmong patients without obstructive CAD, the extent of CAD provides more prognostic information for MACE than traditional cardiovascular risk factors. An interaction was observed between risk factors and CAD burden, suggesting synergistic effects of both

Coronary atherosclerosis scoring with semiquantitative CCTA risk scores for prediction of major adverse cardiac events: Propensity score-based analysis of diabetic and non-diabetic patients.

AIMS:We aimed to compare semiquantitative coronary computed tomography angiography (CCTA) risk scores - which score presence, extent, composition, stenosis and/or location of coronary artery disease (CAD) - and their prognostic value between patients with and without diabetes mellitus (DM). Risk scores derived from general chest-pain populations are often challenging to apply in DM patients, because of numerous confounders. METHODS:Out of a combined cohort from the Leiden University Medical Center and the CONFIRM registry with 5-year follow-up data, we performed a secondary analysis in diabetic patients with suspected CAD who were clinically referred for CCTA. A total of 732 DM patients was 1:1 propensity-matched with 732 non-DM patients by age, sex and cardiovascular risk factors. A subset of 7 semiquantitative CCTA risk scores was compared between groups: 1) any stenosis ≥50%, 2) any stenosis ≥70%, 3) stenosis-severity component of the coronary artery disease-reporting and data system (CAD-RADS), 4) segment involvement score (SIS), 5) segment stenosis score (SSS), 6) CT-adapted Leaman score (CT-LeSc), and 7) Leiden CCTA risk score. Cox-regression analysis was performed to assess the association between the scores and the primary endpoint of all-cause death and non-fatal myocardial infarction. Also, area under the receiver-operating characteristics curves were compared to evaluate discriminatory ability. RESULTS:A total of 1,464 DM and non-DM patients (mean age 58 ± 12 years, 40% women) underwent CCTA and 155 (11%) events were documented after median follow-up of 5.1 years. In DM patients, the 7 semiquantitative CCTA risk scores were significantly more prevalent or higher as compared to non-DM patients (p ≤ 0.022). All scores were independently associated with the primary endpoint in both patients with and without DM (p ≤ 0.020), with non-significant interaction between the scores and diabetes (interaction p ≥ 0.109). Discriminatory ability of the Leiden CCTA risk score in DM patients was significantly better than any stenosis ≥50% and ≥70% (p = 0.003 and p = 0.007, respectively), but comparable to the CAD-RADS, SIS, SSS and CT-LeSc that also focus on the extent of CAD (p ≥ 0.265). CONCLUSION:Coronary atherosclerosis scoring with semiquantitative CCTA risk scores incorporating the total extent of CAD discriminate major adverse cardiac events well, and might be useful for risk stratification of patients with DM beyond the binary evaluation of obstructive stenosis alone

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Energy and indoor environmental performance of typical Egyptian offices : survey, baseline model and uncertainties

Egyptian electricity demands have increased in recent years and are projected to grow further with significant economic and social impacts. Recently, mandatory and voluntary building codes based on international standards have been increasingly adopted. The performance of existing Egyptian buildings is not well understood making the impact of these new codes uncertain. This paper aims to provide insights into existing Egyptian building performance, and elaborate a process for developing a representative model to assist in future policy. The work presented is for office buildings but intended to be widely replicable. An energy survey was carried out for 59 Egyptian offices, categorised by building service type, it was observed that energy use increases as building services increase, and existing Egyptian offices use less energy than benchmarks. A more detailed investigation for a case study office was carried out, to inform detailed model calibration. This provided insight into energy use, thermal comfort and environmental conditions, and revealed high variability in behaviours. A calibrated model was created for the case study office, then a baseline model and input parameter sets created to represent generalised performance. Future uses including assessment of the impact of codes are discussed, and further replication potentials highlighted

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 smoker

Cardiac magnetic resonance in cocaine-induced myocardial damage

A 54-year-old male with history of cocaine abuse underwent trans-thoracic echocardiography that showed hyper-echogenicity of the basal segments of the septum and infero-lateral wall of the left ventricle. The patient underwent cardiac CT that reported diffuse non-obstructive CAD. Cardiac MR showed LGE patterns consistent with non-ischemic myocardial damage associated with cocaine abuse

Percent atheroma volume: Optimal variable to report whole-heart atherosclerotic plaque burden with coronary CTA, the PARADIGM study.

BACKGROUND AND AIMS:Different methodologies to report whole-heart atherosclerotic plaque on coronary computed tomography angiography (CCTA) have been utilized. We examined which of the three commonly used plaque burden definitions was least affected by differences in body surface area (BSA) and sex. METHODS:The PARADIGM study includes symptomatic patients with suspected coronary atherosclerosis who underwent serial CCTA &gt;2 years apart. Coronary lumen, vessel, and plaque were quantified from the coronary tree on a 0.5 mm cross-sectional basis by a core-lab, and summed to per-patient. Three quantitative methods of plaque burden were employed: (1) total plaque volume (PV) in mm3, (2) percent atheroma volume (PAV) in % [which equaled: PV/vessel volume * 100%], and (3) normalized total atheroma volume (TAVnorm) in mm3 [which equaled: PV/vessel length * mean population vessel length]. Only data from the baseline CCTA were used. PV, PAV, and TAVnorm were compared between patients in the top quartile of BSA vs the remaining, and between sexes. Associations between vessel volume, BSA, and the three plaque burden methodologies were assessed. RESULTS:The study population comprised 1479 patients (age 60.7 ± 9.3 years, 58.4% male) who underwent CCTA. A total of 17,649 coronary artery segments were evaluated with a median of 12 (IQR 11-13) segments per-patient (from a 16-segment coronary tree). Patients with a large BSA (top quartile), compared with the remaining patients, had a larger PV and TAVnorm, but similar PAV. The relation between larger BSA and larger absolute plaque volume (PV and TAVnorm) was mediated by the coronary vessel volume. Independent from the atherosclerotic cardiovascular disease risk (ASCVD) score, vessel volume correlated with PV (P &lt; 0.001), and TAVnorm (P = 0.003), but not with PAV (P = 0.201). The three plaque burden methods were equally affected by sex. CONCLUSIONS:PAV was less affected by patient's body surface area then PV and TAVnorm and may be the preferred method to report coronary atherosclerotic burden

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 CA