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.

Development and validation of ischemia risk scores

BACKGROUND

The likelihood of ischemia on myocardial perfusion imaging is central to physician decisions regarding test selection, but dedicated risk scores are lacking. We derived and validated two novel ischemia risk scores to support physician decision making.

METHODS

Risk scores were derived using 15,186 patients and validated with 2,995 patients from a different center. Logistic regression was used to assess associations with ischemia to derive point-based and calculated ischemia scores. Predictive performance for ischemia was assessed using area under the receiver operating characteristic curve (AUC) and compared with the CAD consortium basic and clinical models.

RESULTS

During derivation, the calculated ischemia risk score (0.801) had higher AUC compared to the point-based score (0.786, p < 0.001). During validation, the calculated ischemia score (0.716, 95% CI 0.684- 0.748) had higher AUC compared to the point-based ischemia score (0.699, 95% CI 0.666- 0.732, p = 0.016) and the clinical CAD model (AUC 0.667, 95% CI 0.633- 0.701, p = 0.002). Calibration for both ischemia scores was good in both populations (Brier score  < 0.100).

CONCLUSIONS

We developed two novel risk scores for predicting probability of ischemia on MPI which demonstrated high accuracy during model derivation and in external testing. These scores could support physician decisions regarding diagnostic testing strategies.

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

BACKGROUND

Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed.

OBJECTIVES

The authors developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups.

METHODS

Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC).

RESULTS

During internal testing, patients with normal perfusion and elevated HARD MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P < 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress total perfusion deficit (TPD) (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P < 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external).

CONCLUSIONS

The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.

Prognostic Significance of Nonobstructive Left Main Coronary Artery Disease in Patients With and Without Diabetes: Long-Term Outcomes From the CONFIRM Registry

BACKGROUND

Prognostic significance of non-obstructive left main (LM) disease was recently reported. However, the influence of diabetes mellitus (DM) on event rates in patients with and without non-obstructive LM disease is not well-known.

METHODS

We evaluated 27,252 patients undergoing coronary computed tomographic angiography from the COroNary CT Angiography Evaluation For Clinical Outcomes: An InteRnational Multicenter (CONFIRM) Registry. Cumulative long-term incidence of all-cause mortality (ACM) was assessed between DM and non-DM patients by normal or non-obstructive LM disease (1-49% stenosis).

RESULTS

The mean age of the study population was 57.6±12.6 years. Of the 27,252 patients, 4,434 (16%) patients had DM. A total of 899 (3%) deaths occurred during the follow-up of 3.6±1.9. years. Compared to patients with normal LM, those with non-obstructive LM had more pronounced overall coronary atherosclerosis and more cardiovascular risk factors. After clinical risk factors, segment involvement score, and stenosis severity adjustment, compared to patients without DM and normal LM, patients with DM were associated with increased ACM regardless of normal (HR 1.48, 95% CI 1.22-1.78, p<0.001) or non-obstructive LM (HR 1.46, 95% CI 1.04-2.04, p=0.029), while nonobstructive LM disease was not associated with increased ACM in patients without DM (HR 0.85, 95% CI 0.67-1.07, p=0.165) and there was no significant interaction between DM and LM status (HR 1.03, 95% CI 0.69-1.54, p=0.879).

CONCLUSION

From the CONFIRM registry, we demonstrated that DM was associated with increased ACM. However, the presence of non-obstructive LM was not an independent risk marker of ACM, and there was no significant interaction between DM and non-obstructive LM disease for ACM.

Ischemia With Nonobstructive Coronary Arteries: Insights From the ISCHEMIA Trial

BACKGROUND

Ischemia with nonobstructive coronary arteries (INOCA) is common clinically, particularly among women, but its prevalence among patients with at least moderate ischemia and the relationship between ischemia severity and non-obstructive atherosclerosis severity are unknown.

OBJECTIVES

The authors investigated predictors of INOCA in enrolled, nonrandomized participants in ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches), sex differences, and the relationship between ischemia and atherosclerosis in patients with INOCA.

METHODS

Core laboratories independently reviewed screening noninvasive stress test results (nuclear imaging, echocardiography, magnetic resonance imaging or nonimaging exercise tolerance testing), and coronary computed tomography angiography (CCTA), blinded to results of the screening test. INOCA was defined as all stenoses <50% on CCTA in a patient with moderate or severe ischemia on stress testing. INOCA patients, who were excluded from randomization, were compared with randomized participants with ≥50% stenosis in ≥1 vessel and moderate or severe ischemia.

RESULTS

Among 3,612 participants with core laboratory-confirmed moderate or severe ischemia and interpretable CCTA, 476 (13%) had INOCA. Patients with INOCA were younger, were predominantly female, and had fewer atherosclerosis risk factors. For each stress testing modality, the extent of ischemia tended to be less among patients with INOCA, particularly with nuclear imaging. There was no significant relationship between severity of ischemia and extent or severity of nonobstructive atherosclerosis on CCTA. On multivariable analysis, female sex was independently associated with INOCA (odds ratio: 4.2 [95% CI: 3.4-5.2]).

CONCLUSIONS

Among participants enrolled in ISCHEMIA with core laboratory-confirmed moderate or severe ischemia, the prevalence of INOCA was 13%. Severity of ischemia was not associated with severity of nonobstructive atherosclerosis. (International Study of Comparative Health Effectiveness With Medical and Invasive Approaches [ISCHEMIA]; NCT01471522).

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

PURPOSE

Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing.

METHODS

Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6 months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (model 2), and patients without CAD and TPD < 2% (model 3). All models were tested in an external population of patients with invasive angiography within 6 months (n = 332) or low likelihood of CAD (n = 179).

RESULTS

Model 3 achieved the best calibration (Brier score 0.104 vs 0.121, p < 0.01). Improvement in calibration was particularly evident in women (Brier score 0.084 vs 0.124, p < 0.01). In external testing (n = 511), the area under the receiver operating characteristic curve (AUC) was higher for model 3 (0.930), compared to U-TPD (AUC 0.897) and S-TPD (AUC 0.900, p < 0.01 for both).

CONCLUSION

Training AI models with augmentation of low-risk patients can improve calibration of AI models developed to identify patients with CAD, allowing more accurate assignment of disease probability. This is particularly important in lower-risk populations and in women, where overestimation of disease probability could significantly influence down-stream patient management.

MutaGAN: A sequence-to-sequence GAN framework to predict mutations of evolving protein populations

The ability to predict the evolution of a pathogen would significantly improve the ability to control, prevent, and treat disease. Machine learning, however, is yet to be used to predict the evolutionary progeny of a virus. To address this gap, we developed a novel machine learning framework, named MutaGAN, using generative adversarial networks with sequence-to-sequence, recurrent neural networks generator to accurately predict genetic mutations and evolution of future biological populations. MutaGAN was trained using a generalized time-reversible phylogenetic model of protein evolution with maximum likelihood tree estimation. MutaGAN was applied to influenza virus sequences because influenza evolves quickly and there is a large amount of publicly available data from the National Center for Biotechnology Information's Influenza Virus Resource. MutaGAN generated 'child' sequences from a given 'parent' protein sequence with a median Levenshtein distance of 4.00 amino acids. Additionally, the generator was able to generate sequences that contained at least one known mutation identified within the global influenza virus population for 72.8 per cent of parent sequences. These results demonstrate the power of the MutaGAN framework to aid in pathogen forecasting with implications for broad utility in evolutionary prediction for any protein population.

Mortality impact of low CAC density predominantly occurs in early atherosclerosis: explainable ML in the CAC consortium

BACKGROUND

Machine learning (ML) models of risk prediction with coronary artery calcium (CAC) and CAC characteristics exhibit high performance, but are not inherently interpretable.

OBJECTIVES

To determine the direction and magnitude of impact of CAC characteristics on 10-year all-cause mortality (ACM) with explainable ML.

METHODS

We analyzed asymptomatic subjects in the CAC consortium. We trained ML models on 80% and tested on 20% of the data with XGBoost, using clinical characteristics ​+ ​CAC (ML 1) and additional CAC characteristics of CAC density and number of calcified vessels (ML 2). We applied SHAP, an explainable ML tool, to explore the relationship of CAC and CAC characteristics with 10-year all-cause and CV mortality.

RESULTS

2376 deaths occurred among 63,215 patients [68% male, median age 54 (IQR 47-61), CAC 3 (IQR 0-94.3)]. ML2 was similar to ML1 to predict all-cause mortality (Area Under the Curve (AUC) 0.819 vs 0.821, p ​= ​0.23), but superior for CV mortality (0.847 vs 0.845, p ​= ​0.03). Low CAC density increased mortality impact, particularly ≤0.75. Very low CAC density ≤0.75 was present in only 4.3% of the patients with measurable density, and 75% occurred in CAC1-100. The number of diseased vessels did not increase mortality overall when simultaneously accounting for CAC and CAC density.

CONCLUSION

CAC density contributes to mortality risk primarily when it is very low ≤0.75, which is primarily observed in CAC 1-100. CAC and CAC density are more important for mortality prediction than the number of diseased vessels, and improve prediction of CV but not all-cause mortality. Explainable ML techniques are useful to describe granular relationships in otherwise opaque prediction models.

Prediction of Coronary Artery Calcium Using Deep Learning of Echocardiograms

BACKGROUND

Coronary artery calcification (CAC), often assessed by computed tomography (CT), is a powerful marker of coronary artery disease that can guide preventive therapies. Computed tomographies, however, are not always accessible or serially obtainable. It remains unclear whether other widespread tests such as transthoracic echocardiograms (TTEs) can be used to predict CAC.

METHODS

Using a data set of 2,881 TTE videos paired with coronary calcium CTs, we trained a video-based artificial intelligence convolutional neural network to predict CAC scores from parasternal long-axis views. We evaluated the model's ability to classify patients from a held-out sample as well as an external site sample into zero CAC and high CAC (CAC ≥ 400 Agatston units) groups by receiver operating characteristic and precision-recall curves. We also investigated whether such classifications prognosticated significant differences in 1-year mortality rates by the log-rank test of Kaplan-Meier curves.

RESULTS

Transthoracic echocardiogram artificial intelligence models had high discriminatory abilities in predicting zero CAC (receiver operating characteristic area under the curve [AUC] = 0.81 [95% CI, 0.74-0.88], F1 score = 0.95) and high CAC (AUC = 0.74 [0.68-0.8], F1 score = 0.74). This performance was confirmed in an external test data set of 92 TTEs (AUC = 0.75 [0.65-0.85], F1 score = 0.77; and AUC = 0.85 [0.76-0.93], F1 score = 0.59, respectively). Risk stratification by TTE-predicted CAC performed similarly to CT CAC scores in prognosticating significant differences in 1-year survival in high-CAC patients (CT CAC ≥ 400 vs CT CAC < 400, P = .03; TTE-predicted CAC ≥ 400 vs TTE-predicted CAC < 400, P = .02).

CONCLUSIONS

A video-based deep learning model successfully used TTE videos to predict zero CAC and high CAC with high accuracy. Transthoracic echocardiography-predicted CAC prognosticated differences in 1-year survival similar to CT CAC. Deep learning of TTEs holds promise for future adjunctive coronary artery disease risk stratification to guide preventive therapies.

Imaging subclinical coronary atherosclerosis to guide lipid management, are we there yet?

Atherosclerotic cardiovascular disease risk (ASCVD) is an ongoing epidemic, and lipid abnormalities are its primordial cause. Most individuals suffering a first ASCVD event are previously asymptomatic and often do not receive preventative therapies. The cornerstone of primary prevention has been the identification of individuals at risk through risk calculators based on clinical and laboratory traditional risk factors plus risk enhancers. However, it is well accepted that a clinical risk calculator misclassifies a significant proportion of individuals leading to the prescription of a lipid-lowering medication with very little yield or a missed opportunity for lipid-lowering agents with a potentially preventable event. The development of coronary artery calcium scoring (CAC) and CT coronary angiography (CCTA) provide complementary tools to directly visualize coronary plaque and other risk-modifying imaging components that can potentially provide individualized lipid management. Understanding patient selection for CAC or potentially CCTA and the risk implications of the different parameters provided, such as CAC score, coronary stenosis, plaque characteristics and burden, epicardial adipose tissue, and pericoronary adipose tissue, have grown more complex as technologies evolve. These parameters directly affect the shared decision with patients to start or withhold lipid-lowering therapies, to adjust statin intensity or LDL cholesterol goals. Emerging lipid lowering studies with non-invasive imaging as a guide to patient selection and treatment efficacy, plus the evolution of lipid lowering therapies from statins to a diverse armament of newer high-cost agents have pushed these two fields forward with a complex interaction. This review will discuss existing risk estimators, and non-invasive imaging techniques for subclinical coronary atherosclerosis, traditionally studied using CAC and more recently CCTA with qualitative and quantitative measurements. We will also explore the current data, gaps of knowledge and future directions on the use of these techniques in the risk-stratification and guidance of lipid management.

Comparison of Outcomes of Invasive or Conservative Management of Chronic Coronary Disease in Four Randomized Controlled Trials

Revascularization and medical therapy for chronic coronary disease have both evolved significantly over the last 50 years. A total of 4 contemporary randomized controlled trials- Clinical Outcomes Utilizing Revascularization and Aggressive drug Evaluation (COURAGE), Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D), Fractional Flow Reserve versus Angiography for Multivessel Evaluation 2 (FAME 2), and International Study of Comparative Health Effectiveness with Medical and Invasive Approaches (ISCHEMIA)-have assessed the incremental benefit of revascularization when added to secondary prevention with intensive pharmacologic and lifestyle intervention. We reviewed these 4 seminal studies with the objective of marshaling evidence to better frame how these results should apply to clinical decision making. These studies differed in study design, end points, intensity of treatment, and revascularization techniques. Nevertheless, they all demonstrate similar rates of "hard" clinical events with invasive and conservative management, and varying degrees of benefit in angina-related quality of life with revascularization. In conclusion, although controversy persists concerning the role of revascularization because of differing interpretations of the clinical trial evidence, we contend that instead of being competing management strategies, invasive and conservative approaches are complementary.

Long-term Survival Benefit From Revascularization Compared With Medical Therapy in Patients With or Without Diabetes Undergoing Myocardial Perfusion Single Photon Emission Computed Tomography

OBJECTIVE

To explore the long-term association of survival benefit from early revascularization with the magnitude of ischemia in patients with diabetes compared with those without diabetes using a large observational cohort of patients undergoing single photon emission computed tomography myocardial perfusion imaging (SPECT-MPI).

RESEARCH DESIGN AND METHODS

Of 41,982 patients who underwent stress and rest SPECT-MPI from 1998 to 2017, 8,328 (19.8%) had diabetes. A propensity score was used to match 8,046 patients with diabetes to 8,046 patients without diabetes. Early revascularization was defined as occurring within 90 days after SPECT-MPI. The percentage of myocardial ischemia was assessed from the magnitude of reversible myocardial perfusion defect on SPECT-MPI.

RESULTS

Over a median 10.3-year follow-up, the annualized mortality rate was higher for the patients with diabetes compared with those without diabetes (4.7 vs. 3.6%; P < 0.001). There were significant interactions between early revascularization and percent myocardial ischemia in patients with and without diabetes (all interaction P values <0.05). After adjusting for confounding variables, survival benefit from early revascularization was observed in patients with diabetes above a threshold of >8.6% ischemia and in patients without diabetes above a threshold of >12.1%. Patients with diabetes receiving insulin had a higher mortality rate (6.2 vs. 4.1%; P < 0.001), but there was no interaction between revascularization and insulin use (interaction P value = 0.405).

CONCLUSIONS

Patients with diabetes, especially those on insulin treatment, had higher mortality rate compared with patients without diabetes. Early revascularization was associated with a mortality benefit at a lower ischemic threshold in patients with diabetes compared with those without diabetes.

The prevalence and predictors of inducible myocardial ischemia among patients referred for radionuclide stress testing

BACKGROUND

The frequency of inducible myocardial ischemia has declined in contemporary stress test cohorts, suggesting a need to re-evaluate its optimal use. To-date, however, a comprehensive analysis of the most potent predictors of myocardial ischemia among cardiac stress test patients has not been conducted.

METHODS

We assessed 27,615 patients referred for stress-rest SPECT myocardial perfusion imaging between January 1, 2004 and December 31, 2017. Chi-square analysis was used to ascertain the most potent predictors of ischemia.

RESULTS

Among our cohort, CAD status (presence/absence of known CAD), rest left ventricular ejection fraction (LVEF), and typical angina were the most potent predictors of ischemia. The frequency of ischemia was only 6.6% among patients with an LVEF > 55% but 38.1% for patients with LVEF < 45% (P < 0.001). The frequency of myocardial ischemia was fourfold higher among patients with known CAD vs no known CAD (28.0% vs 6.5%, P < 0.001) and approximately threefold higher among patients with typical angina vs patients with atypical symptoms (P < 0.001).

CONCLUSIONS

The frequency of myocardial ischemia varies markedly according to the common clinical parameters and is particularly high among patients with known CAD, low LVEF, and typical angina. These observations may be used to develop more cost-effective strategies for referring patients for cardiac stress testing.

Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry

BACKGROUND

Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) vs other clinical variables is not well delineated.

METHODS AND RESULTS

From 19,658 patients who underwent SPECT-MPI, we identified 3122 patients with DM without known coronary artery disease (CAD) (DM+/CAD-) and 3564 without DM with known CAD (DM-/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD- and DM-/CAD+ groups. There was comparable MACE in the matched DM+/CAD- and DM-/CAD+ groups (HR 1.15, 95% CI 0.97-1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and DM status. The combined consideration of mode of stress, TPD, and DM provided synergistic stratification, an 8.87-fold (HR 8.87, 95% CI 7.27-10.82) increase in MACE among pharmacologically stressed patients with DM and TPD > 10% (vs non-ischemic, exercised stressed patients without DM).

CONCLUSIONS

Propensity-matched patients with DM and no known CAD have similar MACE risk compared to patients with known CAD and no DM. DM is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.

Evaluation of California Non-Comprehensive Death File Against National Death Index

The National Death Index (NDI) by the Centers for Disease Control and Prevention and Death Master File (DMF) by Social Security Administration are the two most broadly utilized data files for mortality outcomes in clinical research. NDI's high costs and the elimination of protected death records from California in DMF calls for alternative death files. The recently emerged California Non-Comprehensive Death File (CNDF) serves as an alternative source for vital statistics. This study aims to evaluate the sensitivity and specificity of CNDF compared to NDI. Of 40,724 consented subjects in the Cedars-Sinai Cardiac Imaging Research Registry, 25,836 eligible subjects were queried through the NDI and the CDNF. After exclusion of death records to establish the same temporal and geographic availability of data, NDI identified 5,707 exact matches, while CNDF identified 6,051 death records. CNDF had a sensitivity of 94.3% and specificity of 96.4% compared to NDI exact matches. NDI also produced 581 close matches: all were verified as deaths by CNDF through matching death date and patient identifiers. Combining all NDI death records, CNDF had a sensitivity of 94.8% and specificity of 99.5%. CNDF is a reliable source for obtaining mortality outcomes and providing additional mortality validation. The use of CNDF can aid and replace the use of NDI in the state of California.

Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry

BACKGROUND

The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed.

METHODS

We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia.

RESULTS

In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6 % of patients with LVEF > 55 %, in 26.2 % of patients with LVEF 45 %-54 %, and in 48.3% among patients with LVEF < 45 % (P < 0.001). A similar pattern was noted for rest TPD (P < 0.001). Men had a threefold higher frequency of ischemia versus women (25.8 % vs. 8.4%, P < 0.001). Although the relative ranking of ischemia predictors varied among centers, LVEF and/or rest TPD were among the two most potent predictors of myocardial ischemia within each center.

CONCLUSION

The prevalence of myocardial ischemia varied markedly according to clinical and imaging characteristics. LVEF and rest TPD are robust predictors of myocardial ischemia.

Extent of subclinical atherosclerosis on coronary computed tomography and impact of statins in patients with diabetes without known coronary artery disease: Results from CONFIRM registry

BACKGROUND

Absence of subclinical atherosclerosis is considered safe to defer statin therapy in general population. However, impact of statins on atherosclerotic cardiovascular disease in patients with diabetes stratified by coronary artery calcium (CAC) scores and extent of non-obstructive CAD on coronary computed tomography angiography (CCTA) has not been evaluated.

METHODS

CONFIRM (Coronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multi-center Registry) study enrolled consecutive adults 18 years of age between 2005 and 2009 who underwent ³64-detector row CCTA for suspected CAD. The long-term registry includes data on 12,086 subjects who underwent CCTA at 17 centers in 9 countries. In this sub-study of CONFIRM registry, patients with diabetes mellitus (DM) and without diabetes mellitus with normal CCTA or non-obstructive plaque (<50 % diameter stenosis) for whom data on baseline statin use was available were included. CAC score was calculated using Agatston score. The magnitude of non-obstructive coronary artery disease on CCTA was quantified using segment involvement score (SIS). Primary outcome was major cardiovascular events (MACE) which included all-cause mortality, myocardial infarction, and target vessel re-vascularization.

RESULTS

A total of 7247 patients (Mean age 56.8 years) with a median follow up of 5 years were included. For DM patients, baseline statin therapy significantly reduced MACE for patients with CAC ≥100 (HR: 0.24; 95 % CI 0.07-0.87; p = 0.03) and SIS≥3 (HR: 0.23; 95 % CI 0.06-0.83; p = 0.024) compared to those not on statin therapy. Among Diabetics with lower CAC (<100) and SIS (≤3) scores, MACE was similar in statin and non-statin groups. In contrast, among non-DM patients, MACE was similar in statin and no statin groups irrespective of baseline CAC (1-99 or ≥100) and SIS.

CONCLUSION

In this large multicenter cohort of patients, the presence and extent of subclinical atherosclerosis as assessed by CAC and SIS identified patients most likely to derive benefit from statin therapy.

Glycemic control is independently associated with rapid progression of coronary atherosclerosis in the absence of a baseline coronary plaque burden: a retrospective case-control study from the PARADIGM registry

BACKGROUND

The baseline coronary plaque burden is the most important factor for rapid plaque progression (RPP) in the coronary artery. However, data on the independent predictors of RPP in the absence of a baseline coronary plaque burden are limited. Thus, this study aimed to investigate the predictors for RPP in patients without coronary plaques on baseline coronary computed tomography angiography (CCTA) images.

METHODS

A total of 402 patients (mean age: 57.6 ± 10.0 years, 49.3% men) without coronary plaques at baseline who underwent serial coronary CCTA were identified from the Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging (PARADIGM) registry and included in this retrospective study. RPP was defined as an annual change of ≥ 1.0%/year in the percentage atheroma volume (PAV).

RESULTS

During a median inter-scan period of 3.6 years (interquartile range: 2.7-5.0 years), newly developed coronary plaques and RPP were observed in 35.6% and 4.2% of the patients, respectively. The baseline traditional risk factors, i.e., advanced age (≥ 60 years), male sex, hypertension, diabetes mellitus, hyperlipidemia, obesity, and current smoking status, were not significantly associated with the risk of RPP. Multivariate linear regression analysis showed that the serum hemoglobin A1c level (per 1% increase) measured at follow-up CCTA was independently associated with the annual change in the PAV (β: 0.098, 95% confidence interval [CI]: 0.048-0.149; P < 0.001). The multiple logistic regression models showed that the serum hemoglobin A1c level had an independent and positive association with the risk of RPP. The optimal predictive cut-off value of the hemoglobin A1c level for RPP was 7.05% (sensitivity: 80.0%, specificity: 86.7%; area under curve: 0.816 [95% CI: 0.574-0.999]; P = 0.017).

CONCLUSION

In this retrospective case-control study, the glycemic control status was strongly associated with the risk of RPP in patients without a baseline coronary plaque burden. This suggests that regular monitoring of the glycemic control status might be helpful for preventing the rapid progression of coronary atherosclerosis irrespective of the baseline risk factors. Further randomized investigations are necessary to confirm the results of our study.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02803411.