Coronary Risk Prediction Research Articles

Prediction of Coronary Artery Disease Risk Using Genetic and Phenotypic Variables.

Coronary artery disease (CAD) has the highest disease burden worldwide. To manage this burden, predictive models are required to screen patients for preventative treatment. A range of variables have been explored for their capacity to predict disease, including phenotypic (age, sex, BMI and smoking status), medical imaging (carotid artery thickness) and genotypic. We use a machine learning models and the UK Biobank cohort to measure the prediction capacity of these 3 variable categories, both in combination and isolation. We demonstrate that phenotypic variables from the Framingham risk score have the best prediction capacity, although a combination of phenotypic, medical imaging and genotypic variables deliver the most specific models. Furthermore, we demonstrate that Variant Spark, a random forest based GWAS platform, performs effective feature selection for SNP-based genotype variables, identifying 115 significantly associated SNPs to the CAD phenotype.

Comparison of seven surrogate insulin resistance indexes for prediction of incident coronary heart disease risk: a 10-year prospective cohort study.

There were seven novel and easily accessed insulin resistance (IR) surrogates established, including the Chinese visceral adiposity index (CVAI), the visceral adiposity index (VAI), lipid accumulation product (LAP), triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC) and TyG-waist to height ratio (TyG-WHtR). We aimed to explore the association between the seven IR surrogates and incident coronary heart disease (CHD), and to compare their predictive powers among Chinese population. This is a 10-year prospective cohort study conducted in China including 6393 participants without cardiovascular disease (CVD) at baseline. We developed Cox regression analyses to examine the association of IR surrogates with CHD (hazard ratio [HR], 95% confidence intervals [CI]). Moreover, the receiver operating characteristic (ROC) curve was performed to compare the predictive values of these indexes for incident CHD by the areas under the ROC curve (AUC). During a median follow-up period of 10.25 years, 246 individuals newly developed CHD. Significant associations of the IR surrogates (excepted for VAI) with incident CHD were found in our study after fully adjustment, and the fifth quintile HRs (95% CIs) for incident CHD were respectively 2.055(1.216-3.473), 1.446(0.948-2.205), 1.753(1.099-2.795), 2.013(1.214-3.339), 3.169(1.926-5.214), 2.275(1.391-3.719) and 2.309(1.419-3.759) for CVAI, VAI, LAP, TyG, TyG-BMI, TyG-WC and TyG-WHtR, compared with quintile 1. Furthermore, CVAI showed maximum predictive capacity for CHD among these seven IR surrogates with the largest AUC: 0.632(0.597,0.667). The seven IR surrogates (excepted for VAI) were independently associated with higher prevalence of CHD, among which CVAI is the most powerful predictor for CHD incidence in Chinese populations.

Development of a Nomogram That Predicts the Risk of Coronary Heart Disease in Patients With Hyperlipidemia.

Hyperlipidemia is one of the independent risk factors for the onset of coronary heart disease (CHD), and our aim is to construct a coronary risk prediction model for patients with hyperlipidemia based on carotid ultrasound in combination with other risk factors. The nomogram risk prediction model is based on a retrospective study on 820 patients with hyperlipidemia. The predictive accuracy and discriminative ability of the nomogram were determined by receiver operating characteristic (ROC) curves and calibration curves. The results were validated using bootstrap resampling and a prospective study on 39 patients with hyperlipidemia accepted at consenting institutions from 2021 to 2022. In the modeling cohort, 820 patients were included. A total of 33 variables were included in univariate logistic regression. On multivariate analysis of the modeling cohort, independent factors for survival were sex, age, hypertension, plaque score, LVEF, PLT, and HbAlc, which were all selected into the nomogram. The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The area under the curve (AUC) of the nomogram model was 0.881 (95% CI 0.858∼0.905), with a sensitivity of 79% and a specificity of 81.7%. In the validation cohort, the AUC was 0.75, 95% CI (0.602∼0.906). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of this model were 54.16%, 80%, 81.25%, 52.17% and 64.1%. This model showed a good fitting and calibration and positive net benefits in decision curve analysis. A nomogram model for CHD risk in patients with hyperlipidemia was developed and validated using 7 predictors, which may have potential application value in clinical risk assessment, decision-making, and individualized treatment associated with CHD.

Ten-Year Evolution of Statin Eligibility and Use in a Population-Based Cohort

Studies showing that the management of dyslipidemia is suboptimal are hampered by their cross-sectional design or short follow-up. Using recent data from a population-based cohort with a 10-year follow-up, we assessed the use of statins, including their intensity. We used data from the CoLaus|PsyColaus study, involving 4,655 participants at baseline (2003 to 2006) and 3,587 at 10-year follow-up (2014 to 2017). We assessed the cardiovascular risk of participants according to established guidelines from the European Society of Cardiology (ESC) and from the American Heart Association/American College of Cardiology and estimated 10-year cardiovascular risk using corresponding risk scores, Systemic Coronary Risk Evaluation risk prediction model and Pooled Cohort Equations. We first determined eligibility for statins and adherence to recommendations at 2 time periods. Additionally, we assessed the prevalence of statin users from 2014 to 2017 in persons without atherosclerotic cardiovascular disease at baseline and who developed it during the follow-up (secondary prevention). A total of 219 participants developed a first atherosclerotic cardiovascular disease during follow-up. Statin use in eligible subjects was 25.9% and 24.0% from 2003 to 2006 and 35.9% and 26.3% from 2014 to 2017, according to ESC and American Heart Association/American College of Cardiology guidelines, respectively. Per ESC guidelines, only 28.2% of treated persons achieved low-density lipoproteins cholesterol target levels from 2014 to 2017 (15.8% from 2003 to 2006), and women less frequently attained goals. Only 18% of subjects used high-intensity statins from 2014 to 2017, with women less often receiving them (14% vs 22%). In secondary prevention, only 74% of eligible subjects were using statins. In conclusion, based on contemporaneous data, management of dyslipidemia is suboptimal, including in secondary prevention, especially in women who are less frequently treated and, if treated, less frequently receive high-intensity treatment.

Acute coronary syndrome risk prediction based on gradient boosted tree feature selection and recursive feature elimination: A dataset-specific modeling study.

Acute coronary syndrome (ACS) is a serious cardiovascular disease that can lead to cardiac arrest if not diagnosed promptly. However, in the actual diagnosis and treatment of ACS, there will be a large number of redundant related features that interfere with the judgment of professionals. Further, existing methods have difficulty identifying high-quality ACS features from these data, and the interpretability work is insufficient. In response to this problem, this paper uses a hybrid feature selection method based on gradient boosting trees and recursive feature elimination with cross-validation (RFECV) to reduce ACS feature redundancy and uses interpretable feature learning for feature selection to retain the most discriminative features. While reducing the feature set search space, this method can balance model simplicity and learning performance to select the best feature subset. We leverage the interpretability of gradient boosting trees to aid in understanding key features of ACS, linking the eigenvalue meaning of instances to model risk predictions to provide interpretability for the classifier. The data set used in this paper is patient records after percutaneous coronary intervention (PCI) in a tertiary hospital in Fujian Province, China from 2016 to 2021. In this paper, we experimentally explored the impact of our method on ACS risk prediction. We extracted 25 key variables from 430 complex ACS medical features, with a feature reduction rate of 94.19%, and identified 5 key ACS factors. Compared with different baseline methods (Logistic Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and 1D Convolutional Networks), the results show that our method achieves the highest Accuracy of 98.8%.

Cholesterol and Hypertension Treatment Improve Coronary Risk Prediction but Not Time-Dependent Covariates or Competing Risks.

Background and AimsCardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, their discrimination ability is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there are no data on CV risk functions including time-dependent covariates together with other variables. Our aim was to examine the effect of including time-dependent covariates, competing risks, and treatments in coronary risk prediction.MethodsParticipants from the REGICOR population cohorts (North-Eastern Spain) aged 35–74 years without previous history of cardiovascular disease were included (n = 8470). Coronary and stroke events and mortality due to other CV causes or to cancer were recorded during follow-up (median = 12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with basal measurement of classical risk factors, treatments, or competing risks. Models were cross-validated and compared for discrimination (area under ROC curve), calibration (Hosmer–Lemeshow test), and reclassification (categorical net reclassification index).ResultsCancer mortality was the highest cumulative-incidence event. Adding cholesterol and hypertension treatment to classical risk factors improved discrimination of coronary events by 2% and reclassification by 7–9%. The inclusion of competing risks and/or 2 measurements of risk factors provided similar coronary event prediction, compared to a single measurement of risk factors.ConclusionCoronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary risk prediction does not improve with 2 measurements of covariates or inclusion of competing risks.

Coronary computed tomography angiography improves coronary risk prediction in general population

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Carlos III Health Institute and the European Regional Development FundAgency for Management of University and Research Grants Introduction In the next decades, it is expected an increasing incidence of acute coronary syndrome in the Spanish population. Identify high-risk patients could be the most cost-effective way to reduce its incidence and morbi-mortality burden. We studied the effect of adding coronary artery calcification (CAC) and the extent of atherosclerotic disease (SIS score) to classical cardiovascular (CV) risk factors in coronary risk prediction. Methods This was a prospective cohort study of 325 asymptomatic patients recruited between 2013-2017. Demographic characteristics and CV risk factors were obtained and all participants underwent a coronary computed tomography angiography exam in which CAC and SIS were determined. The cohort was followed-up (median= 4 years) for a composite endpoint that included CV death, myocardial infarction, coronary angiography and/or revascularization. Improvement in discrimination and in reclassification by the inclusion of CAC/SIS in the Framingham-REGICOR function were examined with the Sommer's D index and with the Net reclassification index (NRI) (categorical and continuous), respectively. Results Nine of the 251 individuals included in the study had an event in the follow-up. Of the included participants, 94 had a CAC = 0 and 85 a SIS = 0. These participants had no events. The addition of SIS or of SIS and CAC scores to the Framigham-REGICOR risk function increased significantly the discrimination capacity from 0.71 to 0.88 (Table 1). Reclassification measured by the continuous NRI also improved significantly from 69.5 to 112.4/115.5 when SIS or both scores were included (Table 2). Conclusions CAC and SIS scores were associated to 4-year CV event incidence, independently of coronary risk estimation. Discrimination and reclassification of the Framigham-REGICOR coronary function were significantly improved by both indexes, but SIS overrode the effect of CAC.

Cholesterol and hypertension treatment but not time-dependent covariates or competing risks improve coronary risk prediction: the REGICOR study

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Carlos III Health Institute Agency for Management of University and Research Grants Background Cardiovascular (CV) risk functions are the recommended tool to identify high-risk individuals. However, discrimination of CV risk functions is not optimal. While the effect of biomarkers in CV risk prediction has been extensively studied, there is no data of CV risk functions including time-dependent covariates, competing risks and treatments. Aim To examine the effect of including time-dependent covariates, competing risks and treatments in CV risk prediction. Methods Participants from the REGICOR population cohorts (North-Eastern Spain) aged 35-74 years without previous history of cardiovascular disease were included (n=8,470). Coronary and stroke events, and mortality due to other CV causes or to cancer were recorded during the follow-up (median=12.6 years). A multi-state Markov model was constructed to include competing risks and time-dependent classical risk factors and treatments (2 measurements). This model was compared to Cox models with the basal measurement of classical risk factors, treatments or competing risks. Models were cross-validated and compared by their discrimination (area under the ROC curve), calibration (Hosmer-Lemeshow test) and reclassification (categorical net reclassification index). Results Cancer mortality was the event with the highest cumulative incidence. In coronary event prediction, cholesterol and hypertension treatment addition to classical risk factors, improved significantly discrimination by 2% and reclassification by 7-9%. In stroke event prediction, inclusion of time-dependent covariates decreased significantly discrimination by 3-5%. Conclusion Coronary risk prediction improves when cholesterol and hypertension treatment are included in risk functions. Coronary/stroke prediction does not improve with 2 measurements of covariates or with competing risks.

Acute coronary syndrome risk prediction by ensemble‐MLPs

Acute coronary syndrome (ACS) is a serious cardiovascular disease. The ACS risk prediction model is of great significance during the hospitalisation of ACS patients. However, traditional machine learning methods are not effective in predicting risk events in the ACS treatment process because of sample imbalance and noise. In this letter, the multilayer perceptron (MLP) and ensemble method are combined and then ensemble-MLPs are proposed, which has made two innovations: 1) increase the diversity of the base MLP classifier at the data level, structure level, and parameter level and 2) improve the ensemble performance by proposing a new ensemble method using f1-score weighted average. Experiments have shown that the proposed method outperforms conventional ensemble MLP method and other traditional machine learning methods on the task of predicting risk events in the ACS treatment process.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Usefulness of Cardiac Computed Tomography in Coronary Risk Prediction: A Five-Year Follow-Up of the SPICA Study (Secure Prevention with Imaging of the Coronary Arteries).

Accurate identification of individuals at high coronary risk would reduce acute coronary syndrome incidence and morbi-mortality. We analyzed the effect on coronary risk prediction of adding coronary artery calcification (CAC) and Segment Involvement Score (SIS) to cardiovascular risk factors. This was a prospective cohort study of asymptomatic patients recruited between 2013–2017. All participants underwent a coronary computed tomography angiography to determine CAC and SIS. The cohort was followed-up for a composite endpoint of myocardial infarction, coronary angiography and/or revascularization (median = five years). Discrimination and reclassification of the REGICOR function with CAC/SIS were examined with the Sommer’s D index and with the Net reclassification index (NRI). Nine of the 251 individuals included had an event. Of the included participants, 94 had a CAC = 0 and 85 a SIS = 0, none of them had an event. The addition of SIS or of SIS and CAC to the REGICOR risk function significantly increased the discrimination capacity from 0.74 to 0.89. Reclassification improved significantly when SIS or both scores were included. CAC and SIS were associated with five-year coronary event incidence, independently of cardiovascular risk factors. Discrimination and reclassification of the REGICOR risk function were significantly improved by both indexes, but SIS overrode the effect of CAC.

Coronary artery calcium and risk prediction in type 2 diabetics

Abstract Aims The increased risk for cardiovascular events in diabetics is heterogeneous and contemporary clinical risk score calculators have limited predictive value. We therefore examined the additional value of coronary artery calcium score (CACS) in outcome prediction in type 2 diabetics without clinical coronary artery disease (CAD). Methods and results The study examined a prospective population-based cohort of type 2 diabetics (n=735) aged 55–74 years, recruited between 2006–2008. Patients had at least one additional risk factor and no history or symptoms of CAD. Risk assessment tools included Pooled Cohort Equations (PCE) and Multi-Ethnic Study of Atherosclerosis (MESA) 10-year risk score calculators and CACS. The occurrence of MI, stroke or cardiovascular death (MACE) was assessed over 10-years. Risk score calculators predicted MACE and MI and cardiovascular death individually but not stroke. Increasing levels of CACS predicted MACE and its components independently of clinical risk scores, glycated hemoglobin and other baseline variables: hazard ratio (95% confidence interval) 2.92 (1.06–7.86), 6.53 (2.47–17.29) and 8.3 (3.28–21) for CACS of 1–100, 101–300 and >300 Agatston units respectively, compared to CACS=0. Addition of CACS to PCE improved discrimination of MACE [AUC of PCE 0.615 (0.555–0.676) vs PCE + CACS 0.696 (0.642–0.749); p=0.0024[. Coronary artery calcium was absent in 24% of the study population and was associated with very low event rates even in those with high estimated risk scores. Conclusions CACS in asymptomatic type 2 diabetics provides additional prognostic information beyond that obtained from clinical risk scores alone leading to better discrimination between risk categories. Funding Acknowledgement Type of funding sources: None.

Comparison of two recent ceramide-based coronary risk prediction scores: CERT AND CERT-2

Background and Aims: The Coronary Event Risk Test (CERT) is a validated cardiovascular risk predictor that uses circulating ceramide concentrations to allocate patients into one of four risk categories. This test has recently been updated (CERT-2), now additionally including phosphatidylcholine concentrations.

Improvement in the Prediction of Coronary Heart Disease Risk by Using Artificial Neural Networks.

Cardiovascular diseases, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. Although CHD cannot be entirely predicted by classic risk factors, it is preventable. Therefore, predicting CHD risk is crucial to clinical cardiology research, and the development of innovative methods for predicting CHD risk is of great practical interest. The Framingham risk score (FRS) is one of the most frequently implemented risk models. However, recent advances in the field of analytics may enhance the prediction of CHD risk beyond the FRS. Here, we propose a model based on an artificial neural network (ANN) for predicting CHD risk with respect to the Framingham Heart Study (FHS) dataset. The performance of this model was compared to that of the FRS. A sample of 3066 subjects from the FHS offspring cohort was subjected to an ANN. A multilayer perceptron ANN architecture was used and the lift, gains, receiver operating characteristic (ROC), and precision-recall predicted by the ANN were compared with those of the FRS. The lift and gain curves of the ANN model outperformed those of the FRS model in terms of top percentiles. The ROC curve showed that, for higher risk scores, the ANN model had higher sensitivity and higher specificity than those of the FRS model, although its area under the curve (AUC) was lower. For the precision-recall measures, the ANN generated significantly better results than the FRS with a higher AUC. The findings suggest that the ANN model is a promising approach for predicting CHD risk and a good screening procedure to identify high-risk subjects.

Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT

We aimed to assess relationship between single-click, whole heart radiomics from low-dose computed tomography (LDCT) for lung cancer screening with coronary artery calcification and stenosis. The institutional review board-approved, retrospective study included all 106 patients (68 men, 38 women, mean age 64 ± 7 years) who underwent both LDCT for lung cancer screening and had calcium scoring and coronary computed tomography angiography in our institution. We recorded the clinical variables including patients' demographics, smoking history, family history, and lipid profiles. Coronary calcium scores and grading of coronary stenosis were recorded from the radiology information system. We calculated the multiethnic scores for atherosclerosis risk scores to obtain 10-year coronary heart disease (MESA 10-Y CHD) risk of cardiovascular disease for all patients. Deidentified LDCT exams were exported to a Radiomics prototype for automatic heart segmentation, and derivation of radiomics. Data were analyzed using multiple logistic regression and kernel Fisher discriminant analyses. Whole heart radiomics were better than the clinical variables for differentiating subjects with different Agatston scores (≤400 and >400) (area under the curve [AUC] 0.92 vs 0.69). Prediction of coronary stenosis and MESA 10-Y CHD risk was better on whole heart radiomics (AUC:0.86-0.87) than with clinical variables (AUC:0.69-0.79). Addition of clinical variables or visual assessment of coronary calcification from LDCT to whole heart radiomics resulted in a modest change in the AUC. Single-click, whole heart radiomics obtained from LDCT for lung cancer screening can differentiate patients with different Agatston and MESA risk scores for cardiovascular diseases.

Medical Prior Knowledge Guided Automatic Detection of Coronary Arteries Calcified Plaque with Cardiac CT

Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery disease progression. At present, the Convolutional Neural Network (CNN) is exceedingly popular in natural images’ object detection field. Therefore, CNN in the object detection field of medical images also has a wide range of applications. However, many current calcified plaque detection methods in medical images are based on improving the CNN model algorithm, not on the characteristics of medical images. In response, we propose an automatic calcified plaque detection method in non-contrast-enhanced cardiac CT by adding medical prior knowledge. The training data merging with medical prior knowledge through data augmentation makes the object detection algorithm achieve a better detection result. In terms of algorithm, we employ a deep learning tool knows as Faster R-CNN in our method for locating calcified plaque in coronary arteries. To reduce the generation of redundant anchor boxes, Region Proposal Networks is replaced with guided anchoring. Experimental results show that the proposed method achieved a decent detection performance.

Abstract 17065: Performance of Cardiovascular Disease Risk Scores to Identify Coronary Artery Calcium Burden in Retired National Football League Players

Introduction: Coronary artery calcium (CAC) is a robust predictor of coronary events in asymptomatic individuals with latent atherosclerotic cardiovascular disease (CVD). While evidence suggests CAC scoring may augment traditional CVD risk scores in clinical decision making, evidence is limited on the compared ability of CVD risk scores to identify the degree of coronary atherosclerosis as quantified by absolute CAC, particularly in former elite athlete populations. We investigated this in a cohort of retired National Football League (NFL) players. Methods: We analyzed data on 752 retired NFL players (aged 55.2 ± 9.0 years, 53.7% African-American] that underwent health screening and CAC scoring with the NFL Player Care Foundation. Three 10-year CVD risk scores were compared: Framingham Coronary Heart Disease (FCHD), Framingham CVD (FCVD), and Atherosclerotic CVD Risk Pooled Cohort Equations (PCE). Receiver operating characteristic curves were fitted in 3 models: FCHD (Model 1), FCVD (Model 2), and PCE (Model 3, used as reference based on 2013 AHA guidelines). Contrast analyses identified the model with highest discriminative ability (c statistic) versus CAC = 0 for each CAC score category: >0 and <100, 100-400, and >400. Results: Compared to PCE , FCVD exhibited the highest discriminative ability for CAC > 0 and < 100 ( c statistic 0.7071 vs 0.6706, p<0.0001), while FCHD had the lowest for both CAC 100-400 ( c statistic 0.7198 vs 0.7664, p=0.0165) and CAC >400 ( c statistic 0.7728 vs 0.8460, p<0.0001). No additional differences were identified (Figure 1). Conclusion: Traditional CVD risk scores differ in performance to predict absolute CAC among retired NFL players, underscoring a need for refinement of coronary event risk prediction models to enhance the ability of such models to identify, specifically, low CAC, as even low CAC burden confers increased risk compared to CAC absence. This may include accounting for elite athlete-specific characteristics.

Abstract 14210: Comparison of Two Recent Ceramide-based Coronary Risk Prediction Scores: CERT and CERT-2

Introduction: The Coronary Event Risk Test (CERT) is a validated cardiovascular risk predictor that uses circulating ceramide concentrations to allocate patients into one of four risk categories. This test has recently been updated (CERT-2), now additionally including phosphatidylcholine concentrations. Hypothesis: We hypothesize that CERT and CERT-2 have the power to predict cardiovascular mortality in patients with cardiovascular disease (CVD). Methods: We investigated a patient cohort of 999 patients with established CVD. Results: Overall, comparing survival curves (figure) for over 12 years of follow up and the predictive power of survival models using net reclassification improvement (NRI), CERT-2 was the best predictor of cardiovascular mortality, surpassing CERT (NRI=0.456; p=0.01) and also the 2019 ESC-SCORE (NRI=0.163; p=0.04). Patients in the highest risk category of CERT as compared to the lowest category had a HR of 3.63 [2.09-6.30] for cardiovascular death; for CERT-2 the corresponding HR was 6.02 [2.47-14.64]. Among patients with T2DM (n=322), the HR for cardiovascular death was 3.00 [1.44-6.23] using CERT and 7.06 [1.64-30.50] using CERT-2; the corresponding HRs among non-diabetic subjects were 2.99 [1.20-7.46] and 3.43 [1.03-11.43], respectively. Conclusions: We conclude that both, CERT and CERT-2 scores are powerful predictors of cardiovascular mortality in CVD patients, especially in those patients with T2D, performance is even higher with CERT-2.

Coronary Artery Disease Risk Assessment in Sedentary and Active Patients: Medical Risk, Behavioral Psychology, and the Standard of Care

Objective: A practical approach to determining risk in sedentary and active patients for coronary artery disease. Identify how personal/family history, atherogenic risk factors, and coronary calcium are essential to determining and predicting risk potential. Materials/Methods: Review of the medical, behavioral psychology, and standard of care literature to identify how human psychology, statistical risk of coronary disease, and coronary calcium shape risk prediction. Results: A comprehensive personal/family history, risk factor assessment and comprehensive physical examination are the foundation of risk assessment. Understanding the cognitive process of risk potential is critical to management strategies. Stress testing and coronary calcium scoring are useful adjuncts when initial screening is suggestive of intermediate atherogenic risk. Conclusion: Comprehensive personal/family history, risk factor assessment, comprehensive physical examination, cognitive processing of risk potential, stress testing and calcium scoring all have a role in risk assessment of sedentary and active patients.

Comparison of two recent ceramide-based coronary risk prediction scores: CERT and CERT-2

Abstract The Coronary Event Risk Test (CERT) is a validated cardiovascular risk predictor that uses circulating ceramide concentrations to allocate patients into one of four risk categories. This test has recently been updated (CERT-2), now additionally including phosphatidylcholine concentrations. The purpose of this study was to investigate the power of CERT and CERT-2 to predict cardiovascular mortality in patients with cardiovascular disease (CVD). We investigated a cohort of 999 patients with established CVD. Overall, comparing survival curves (figure) for over 12 years of follow up and the predictive power of survival models using net reclassification improvement (NRI), CERT-2 was the best predictor of cardiovascular mortality, surpassing CERT (NRI=0.456; p=0.01) and also the 2019 ESC-SCORE (NRI=0.163; p=0.04). Patients in the highest risk category of CERT as compared to the lowest category had a HR of 3.63 [2.09–6.30] for cardiovascular death; for CERT-2 the corresponding HR was 6.02 [2.47–14.64]. Among patients with T2DM (n=322), the HR for cardiovascular death was 3.00 [1.44–6.23] using CERT and 7.06 [1.64–30.50] using CERT-2; the corresponding HRs among non-diabetic subjects were 2.99 [1.20–7.46] and 3.43 [1.03–11.43], respectively. We conclude that both, CERT and CERT-2 scores are powerful predictors of cardiovascular mortality in CVD patients, especially in those patients with T2D. Performance is even higher with CERT-2. Funding Acknowledgement Type of funding source: None