Predicting cardiometabolic disease in medical students using FibroScan and 30-year Framingham risk scores.

Introduction: Metabolic dysfunction-associated steatotic liver disease (MASLD) has become a major cause of end-stage hepatic disease worldwide requiring liver transplantation, whereas cardiovascular disease (CVD) remains the leading cause of morbidity and mortality globally. Development of MASLD and CVD among young adults is understudied. This study aimed to assess CVD risk in healthy young medical university students using lipid-based and body mass index (BMI)-based 30-year Framingham risk scores (FS30) and to evaluate disease burden for asymptomatic patients with MASLD by performing FibroScan. Methods: We included medical university students aged 18-30 years without any known medical conditions. All participants underwent physical and anthropometric measurements, and completed a questionnaire. Blood samples were collected for the analysis of glycosylated haemoglobin levels, renal and liver function, biomarker analysis to calculate liver fibrosis risk, and subclinical atherosclerosis biomarkers. Liver stiffness measurements (LSM) and controlled attenuation parameter (CAP) values were measured using FibroScan 430 mini to calculate liver fibrosis and steatosis, respectively. FS30 based on body mass index (FS30-BMI) and lipid levels (FS30-Lipid) were also calculated. Results: Overall, 138 medical students participated in this study after providing informed consent. Using FS30-Lipid and FS30-BMI, CVD risk was identified in two (1.5%; n = 138) and 23 (17.6%; n = 132) individuals, respectively. MASLD fibrosis was identified based on FibroScan LSMs >7.0 kPa in 12 medical students (9.4%, n = 128; 95% CI, 4.7-14.8%). Consumption of coffee and sugary soft drinks were predictive of liver fibrosis. In total, 36 students (28.6%; n = 128) were found to have hepatic steatosis based on FibroScan CAP values >236 dB, and the predictive factors included increased body fat percentage, male sex, and lack of physical activity. Levels of inflammatory biomarkers, such as C-reactive protein and lipids were not elevated in participants with MASLD. Discussion: CVD risk was identified in >17% of young medical students. The frequency of liver fibrosis and steatosis was also high among the participants, indicating that liver damage starts at a relatively early age. Early intervention is needed among young adults via health promotion and lifestyle changes.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Integrating Phenotypic Information of Obstructive Sleep Apnea and Deep Representation of Sleep-Event Sequences for Cardiovascular Risk Prediction.

Background: Advances in mobile, wearable and machine learning (ML) technologies for gathering and analyzing long-term health data have opened up new possibilities for predicting and preventing cardiovascular diseases (CVDs). Meanwhile, the association between obstructive sleep apnea (OSA) and CV risk has been well-recognized. This study seeks to explore effective strategies of incorporating OSA phenotypic information and overnight physiological information for precise CV risk prediction in the general population. Methods: 1,874 participants without a history of CVDs from the MESA dataset were included for the 5-year CV risk prediction. Four OSA phenotypes were first identified by the K-mean clustering based on static polysomnographic (PSG) features. Then several phenotype-agnostic and phenotype-specific ML models, along with deep learning (DL) models that integrate deep representations of overnight sleep-event feature sequences, were built for CV risk prediction. Finally, feature importance analysis was conducted by calculating SHapley Additive exPlanations (SHAP) values for all features across the four phenotypes to provide model interpretability. Results: All ML models showed improved performance after incorporating the OSA phenotypic information. The DL model trained with the proposed phenotype-contrastive training strategy performed the best, achieving an area under the Receiver Operating Characteristic (ROC) curve of 0.877. Moreover, PSG and FOOD FREQUENCY features were recognized as significant CV risk factors across all phenotypes, with each phenotype emphasizing unique features. Conclusion: Models that are aware of OSA phenotypes are preferred, and lifestyle factors should be a greater focus for precise CV prevention and risk management in the general population.

Comparison of three creatinine-based equations to predict adverse outcome in a cardiovascular high-risk cohort: an investigation using the SPRINT research materials.

Background: Novel creatinine-based equations have recently been proposed but their predictive performance for cardiovascular outcomes in participants at high cardiovascular risk in comparison to the established CKD-EPI 2009 equation is unknown. Method: In 9361 participants from the United States included in the randomized controlled SPRINT trial, we calculated baseline estimated glomerular filtration rate (eGFR) using the CKD-EPI 2009, CKD-EPI 2021, and EKFC equations and compared their predictive value of cardiovascular events. The statistical metric used is the net reclassification improvement (NRI) presented separately for those with and those without events. Results: During a mean follow-up of 3.1 ± 0.9 years, the primary endpoint occurred in 559 participants (6.0%). When using the CKD-EPI 2009, the CKD-EPI 2021, and the EKFC equations, the prevalence of CKD (eGFR <60 ml/min/1.73 m2 or >60 ml/min/1.73 m2 with an ACR ≥30 mg/g) was 37% vs. 35.3% (P = 0.02) vs. 46.4% (P < 0.001), respectively. The corresponding mean eGFR was 72.5 ± 20.1 ml/min/1.73 m2 vs. 73.2 ± 19.4 ml/min/1.73 m2 (P < 0.001) vs. 64.6 ± 17.4 ml/min/1.73 m2 (P < 0.001). Neither reclassification according to the CKD-EPI 2021 equation [CKD-EPI 2021 vs. CKD-EPI 2009: NRIevents: -9.5% (95% confidence interval (CI) -13.0% to -5.9%); NRInonevents: 4.8% (95% CI 3.9% to 5.7%)], nor reclassification according to the EKFC equation allowed better prediction of cardiovascular events compared to the CKD-EPI 2009 equation (EKFC vs. CKD-EPI 2009: NRIevents: 31.2% (95% CI 27.5% to 35.0%); NRInonevents: -31.1% (95% CI -32.1% to -30.1%)). Conclusion: Substituting the CKD-EPI 2009 with the CKD-EPI 2021 or the EKFC equation for calculation of eGFR in participants with high cardiovascular risk without diabetes changed the prevalence of CKD but was not associated with improved risk prediction of cardiovascular events for both those with and without the event.

Ceramides improve cardiovascular risk prediction beyond low-density lipoprotein cholesterol.

Aims: Low-density lipoprotein cholesterol (LDL-C) is the best documented cardiovascular risk predictor and at the same time serves as a target for lipid-lowering therapy. However, the power of LDL-C to predict risk is biased by advanced age, comorbidities, and medical treatment, all known to impact cholesterol levels. Consequently, such biased patient cohorts often feature a U-shaped or inverse association between LDL-C and cardiovascular or overall mortality. It is not clear whether these constraints for risk prediction may likewise apply to other lipid risk markers in particular to ceramides and phosphatidylcholines. Methods and results: In this observational cohort study, we recorded cardiovascular mortality in 1195 patients over a period of up to 16 years, comprising a total of 12 262 patient-years. The median age of patients at baseline was 67 years. All participants were either consecutively referred to elective coronary angiography or diagnosed with peripheral artery disease, indicating a high cardiovascular risk. At baseline, 51% of the patients were under statin therapy. We found a U-shaped association between LDL-C and cardiovascular mortality with a trough level of around 150 mg/dL of LDL-C. Cox regression analyses revealed that LDL-C and other cholesterol species failed to predict cardiovascular risk. In contrast, no U-shaped but linear association was found for ceramide- and phosphatidylcholine-containing markers and these markers were able to significantly predict the cardiovascular risk even after multivariate adjustment. Conclusion: We thus suggest that ceramides- and phosphatidylcholine-based predictors rather than LDL-C may be used for a more accurate cardiovascular risk prediction in high-risk patients.

Performance of Cardiovascular Risk Prediction Models in Korean Patients With New-Onset Rheumatoid Arthritis: National Cohort Study.

BACKGROUND: This study aimed to compare the performance of established cardiovascular risk algorithms in Korean patients with new-onset rheumatoid arthritis. METHODS AND RESULTS: This retrospective cohort study identified patients newly diagnosed with rheumatoid arthritis without a history of cardiovascular diseases between 2013 and 2019 using the National Health Insurance Service database. The cohort was followed up until 2020 for the development of the first major adverse cardiovascular event. General cardiovascular risk prediction algorithms, such as the systematic coronary risk evaluation model, the Korean risk prediction model for atherosclerotic cardiovascular diseases, the American College of Cardiology/American Heart Association pooled equations, and the Framingham Risk Score, were used. The discrimination and calibration of cardiovascular risk prediction models were evaluated. Hazard ratios were estimated using Cox proportional hazards regression. A total of 611 patients among 24 889 patients experienced a major adverse cardiovascular event during follow-up. The median 10-year atherosclerotic cardiovascular diseases risk score was significantly higher in patients with major adverse cardiovascular events than those without. The C-statistics of risk algorithms ranged between 0.72 and 0.74. Compared with the low-risk group, the actual risk of developing major adverse cardiovascular events increased significantly in the intermediate- and high-risk groups for all algorithms. However, the risk predictions calculated from all algorithms overestimated the observed cardiovascular risk in the middle to high deciles, and only the systematic coronary risk evaluation algorithm showed comparable observed and predicted event rates in the low-intermediate deciles with the highest sensitivity. CONCLUSIONS: The systematic coronary risk evaluation model algorithm and the general risk prediction models discriminated patients with rheumatoid arthritis appropriately. However, overestimation should be considered when applying the cardiovascular risk prediction model in Korean patients.

The predictive accuracy of cardiovascular risk prediction tools in inflammatory arthritis and psoriasis: an observational validation study using the Clinical Practice Research Datalink.

OBJECTIVES: Cardiovascular risk prediction tools developed for the general population often underperform for individuals with rheumatoid arthritis (RA), and their predictive accuracy are unclear for other inflammatory conditions that also have increased cardiovascular risk. We investigated performance of QRISK-3, Framingham Risk Score (FRS) and Reynolds Risk Score (RRS) in RA, psoriatic disease (psoriatic arthritis (PsA) and psoriasis) and ankylosing spondylitis (AS). We considered osteoarthritis as a non-inflammatory comparator. METHODS: We utilised primary care records from the Clinical Practice Research Datalink (CPRD) Aurum database to identify individuals with each condition and calculated 10-year cardiovascular risk using each prediction tool. Discrimination and calibration of each tool in each disease was assessed. RESULTS: Time-dependent AUC for QRISK3 was 0.752 for RA (95% CI 0.734-0.777), 0.794 for AS (95% CI 0.764-0.812), 0.764 for PsA (95% CI 0.741-0.791),0.815 for psoriasis (95% CI 0.789-0.835), and 0.698 for osteoarthritis (95% CI 0.670-0.717) indicating reasonably good predictive performance. AUC for FRS were similar, and slightly lower for RRS. FRS was reasonably well calibrated for each condition but underpredicted risk for patients with RA. RRS tended to underpredict CVD risk, whilst QRISK3 overpredicted CVD risk, especially for the most high-risk individuals. CONCLUSIONS: CVD risk for individuals with RA, AS and psoriatic disease were generally less accurately predicted using each of the 3 CVD risk prediction tools than reported accuracies in the original publications. Individuals with osteoarthritis also had less accurate predictions suggesting inflammation is not the sole reason for underperformance. Disease specific risk prediction tools may be required.

Impact of Social Support on Cardiovascular Risk Prediction Models: A Systematic Review.

Cardiovascular diseases (CVD) stand as the primary causes of both mortality and morbidity on a global scale. Social factors such as low social support can increase the risk of developing heart diseases and have shown poor prognosis in cardiac patients. Resources such as PubMed and Google Scholar were searched using a boolean algorithm for articles published between 2003 and 2023. Eligible articles showed an association between social support and cardiovascular risks. A systematic review was conducted using the guidance published in the Cochrane Prognosis Method Group and the PRISMA checklist, for reviews of selected articles. A total of five studies were included in our final analysis. Overall, we found that participants with low social support developed cardiovascular events, and providing a good support system can decrease the risk of readmission in patients with a history of CVD. We also found that integrating social determinants in the cardiovascular risk prediction model showed improvement in accessing the risk. Population with good social support showed low mortality and decreased rate of readmission. There are various prediction models, but the social determinants are not primarily included while calculating the algorithms. Although it has been proven in multiple studies that including the social determinants of health (SDOH) improves the accuracy of cardiovascular risk prediction models. Hence, the inclusion of SDOH should be highly encouraged.

Individual lifetime benefit from low-dose colchicine in patients with chronic coronary artery disease.

AIMS: Low-dose colchicine reduces cardiovascular risk in patients with coronary artery disease (CAD), but absolute benefits may vary between individuals. This study aimed to assess the range of individual absolute benefits from low-dose colchicine according to patient risk profile. METHODS AND RESULTS: The European Society of Cardiology (ESC) guideline-recommended SMART-REACH model was combined with the relative treatment effect of low-dose colchicine and applied to patients with CAD from the Low-Dose Colchicine 2 (LoDoCo2) trial and the Utrecht Cardiovascular Cohort-Second Manifestations of ARTerial disease (UCC-SMART) study (n = 10 830). Individual treatment benefits were expressed as 10-year absolute risk reductions (ARRs) for myocardial infarction, stroke, or cardiovascular death (MACE), and MACE-free life-years gained. Predictions were also performed for MACE plus coronary revascularization (MACE+), using a new lifetime model derived in the REduction of Atherothrombosis for Continued Health (REACH) registry. Colchicine was compared with other ESC guideline-recommended intensified (Step 2) prevention strategies, i.e. LDL cholesterol (LDL-c) reduction to 1.4 mmol/L and systolic blood pressure (SBP) reduction to 130 mmHg. The generalizability to other populations was assessed in patients with CAD from REACH North America and Western Europe (n = 25 812). The median 10-year ARR from low-dose colchicine was 4.6% [interquartile range (IQR) 3.6-6.0%] for MACE and 8.6% (IQR 7.6-9.8%) for MACE+. Lifetime benefit was 2.0 (IQR 1.6-2.5) MACE-free years, and 3.4 (IQR 2.6-4.2) MACE+-free life-years gained. For LDL-c and SBP reduction, respectively, the median 10-year ARR for MACE was 3.0% (IQR 1.5-5.1%) and 1.7% (IQR 0.0-5.7%), and the lifetime benefit was 1.2 (IQR 0.6-2.1) and 0.7 (IQR 0.0-2.3) MACE-free life-years gained. Similar results were obtained for MACE+ and in American and European patients from REACH. CONCLUSION: The absolute benefits of low-dose colchicine vary between individual patients with chronic CAD. They may be expected to be of at least similar magnitude to those of intensified LDL-c and SBP reduction in a majority of patients already on conventional lipid-lowering and blood pressure-lowering therapy.

The long-term benefits of treatment with low-dose colchicine were estimated for 36 642 individuals with coronary heart disease, and compared with those of lipid- and blood pressure­lowering therapy. On average, low-dose colchicine was estimated to lower the risk of cardiovascular disease in the next 10 years from 17.8 to 13.2% (a reduction of 4.6% points) and to afford 2.0 additional years of life without cardiovascular disease.Low-dose colchicine was estimated to be the most effective treatment in 49%, intensive blood pressure­lowering therapy in 28%, and intensive lipid-lowering therapy in 23% of patients.

A new score for improving cardiovascular risk prediction and prevention.

BACKGROUND AND AIMS: The ultrasonographic detection of subclinical atherosclerosis (scATS) at carotid and femoral vascular sites using the atherosclerosis burden score (ABS) improves the risk stratification for atherosclerotic cardiovascular disease beyond traditional cardiovascular (CV) risk factors. However, its predictive value should be further enhanced. We hypothesize that combining the ABS and the Framingham risk score (FHRS) to create a new score called the FHRABS will improve CV risk prediction and prevention. We aim to investigate if incorporating the ABS into the FHRS improved CV risk prediction in a primary prevention setting. METHODS AND RESULTS: 1024 patients were included in this prospective observational cohort study. Carotid and femoral plaques were ultra-sonographic detected. Major incident cardiovascular events (MACEs) were collected. The receiver operating characteristic curve (ROC-AUC) and Youden's index (Ysi) were used to compare the incremental contributions of each marker to predict MACEs. After a median follow-up of 6.0 ± 3.3 years, 60 primary MACEs (5.8%) occurred. The ROC-AUC for MACEs prediction was significantly higher for the FHRABS (0.74, p < 0.024) and for the ABS (0.71, p < 0.013) compared to the FHRS alone (0.71, p < 0.46). Ysi or the FHRABS (42%, p < 0.001) and ABS (37%, p < 0.001) than for the FHRS (31%). Cox proportional-hazard models showed that the CV predictive performance of FHRS was significantly enhanced by the ABS (10.8 vs. 5.5, p < 0.001) and FHRABS (HR 23.30 vs. 5.50, p < 0.001). CONCLUSIONS: FHRABS is a useful score for improving CV risk stratification and detecting patients at high risk of future MACEs. FHRABS offers a simple-to-use, and radiation-free score with which to detect scATS in order to promote personalized CV prevention.

Nondipping blood pressure pattern predicts cardiovascular events and mortality in patients with atherosclerotic peripheral vascular disease.

BACKGROUND: Patients with peripheral vascular disease (PVD) are often underdiagnosed and undertreated. Nocturnal nondipping blood pressure (BP) pattern, as diagnosed by ambulatory BP monitoring (ABPM), is associated with increased cardiovascular risk, but has not been studied in patients with PVD. We aimed to investigate if a nondipping BP pattern predicts cardiovascular events or all-cause death in outpatients with PVD. METHODS: Consecutive outpatients with carotid or lower-extremity PVD were examined with 24-hour ABPM (n = 396). Nondipping was defined as a < 10% fall in systolic BP level during night-time. We used Cox regression models adjusting for potential confounders. We also evaluated the incremental prognostic value of dipping status in the COPART risk score. Our primary composite outcome was cardiovascular events or all-cause death. RESULTS: In the cohort (mean age 70; 40% women), 137 events occurred during a 5.1-year median follow-up; incident rate of 7.35 events per 100 person-years. Nondipping was significantly associated with outcome (hazard ratio 1.55, 95% CI 1.07-2.26, p = 0.021) in a fully adjusted model. When adding nondipping to the risk markers in the COPART risk score, the model fit significantly improved (χ2 7.91, p < 0.005) and the C-statistic increased from 0.65 to 0.67. CONCLUSION: In a cohort of outpatients with PVD, nondipping was an independent risk factor for future cardiovascular events or mortality and seemed to be a strong predictor in patients with carotid artery disease but not in lower-extremity PVD. Additional studies are needed to evaluate the clinical utility of ABPM for improved prevention in these high-risk patients. (ClinicalTrials.gov Identifier: NCT01452165).

Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort.

Background: Cardiovascular disease (CVD) screening entails precise event prediction to orient risk stratification, resource allocation, and insurance policy. We used random survival forests (RSF) to identify markers of incident CVD among Japanese adults enrolled in an employer-mandated screening program. Methods and Results: We examined biomarker, health history, medication use, and lifestyle data from 155,108 adults aged ≥40 years. The occurrence of coronary artery disease (CAD) or atherosclerotic CVD (ASCVD) events was examined over 6 years of follow-up. The analysis used RSF to identify predictors, then investigated simplified RSF models with fewer predictors for individual-level risk prediction. Data were split into training (70%) and test (30%) datasets. At baseline, the median patient age was 47 years (interquartile range 41-56 years), with 65% males. In all, 1,642 CAD and 2,164 ASCVD events were observed. RSF identified history of heart disease, age, self-reported blood pressure medication, HbA1c, fasting blood sugar, and high-density lipoprotein as important markers of both endpoints. RSF analyses with only the top 20 predictors demonstrated good performance, with areas under the curve of >84% for CAD and >82% for ASCVD in test data across 6 years. Conclusions: We present a machine learning technique for accurate assessment of cardiovascular risk using employer-mandated annual health checkup information. The algorithm produces individual-level risk curves over time, empowering clinicians to efficiently implement prevention strategies in a low-risk population.

A variant in the fibronectin (FN1) gene, rs1250229-T, is associated with decreased risk of coronary artery disease in familial hypercholesterolaemia.

BACKGROUND: Increased risk of coronary artery disease (CAD) in familial hypercholesterolaemia (FH) is modified by factors beyond defects in the low-density lipoprotein receptor pathway. The rs1250229-T single nucleotide polymorphism (SNP) in the FN1 gene is associated with CAD in genome-wide association studies and is in linkage disequilibrium with another SNP (rs1250259-T) in FN1 that is associated with decrease fibronectin secretion. OBJECTIVE: We investigated whether rs1250229-T was also associated with prevalent CAD in patients with genetically confirmed FH. METHODS: We collected clinical data from 256 patients with genetically confirmed FH. The FN1 rs1250229 SNP was genotyped on a SEQUENOM platform. The association between rs1250229-T and prevalent CAD was assessed using simple and multiple regression analyses. RESULTS: In patients with FH, the FN1 rs1250229-T (minor) allele was a significant negative predictor of prevalent CAD (odds ratio [OR] 0.353; 95% confidence interval [CI] 0.193 - 0.647; P = 0.001). FN1 rs1250229-T remained a significant predictor of prevalent CAD after adjusting for age, sex, obesity, hypertension, smoking status and lipoprotein(a) concentration (OR 0.200; 95% CI 0.091 - 0.441; P < 0.001). CONCLUSION: The FN1 rs1250229-T allele is inversely associated with CAD in patients with genetically confirmed FH, independently of traditional risk factors. While this finding requires replication, it suggests that the biology of fibronectin may contribute to variation in the risk of CAD in FH.

Use of Multi-Modal Data and Machine Learning to Improve Cardiovascular Disease Care.

Today's digital health revolution aims to improve the efficiency of healthcare delivery and make care more personalized and timely. Sources of data for digital health tools include multiple modalities such as electronic medical records (EMR), radiology images, and genetic repositories, to name a few. While historically, these data were utilized in silos, new machine learning (ML) and deep learning (DL) technologies enable the integration of these data sources to produce multi-modal insights. Data fusion, which integrates data from multiple modalities using ML and DL techniques, has been of growing interest in its application to medicine. In this paper, we review the state-of-the-art research that focuses on how the latest techniques in data fusion are providing scientific and clinical insights specific to the field of cardiovascular medicine. With these new data fusion capabilities, clinicians and researchers alike will advance the diagnosis and treatment of cardiovascular diseases (CVD) to deliver more timely, accurate, and precise patient care.

Population median imputation was noninferior to complex approaches for imputing missing values in cardiovascular prediction models in clinical practice.

OBJECTIVES: To compare the validity and robustness of five methods for handling missing characteristics when using cardiovascular disease risk prediction models for individual patients in a real-world clinical setting. STUDY DESIGN AND SETTING: The performance of the missing data methods was assessed using data from the Swedish National Diabetes Registry (n = 419,533) with external validation using the Scottish Care Information - diabetes database (n = 226,953). Five methods for handling missing data were compared. Two methods using submodels for each combination of available data, two imputation methods: conditional imputation and median imputation, and one alternative modeling method, called the naïve approach, based on hazard ratios and populations statistics of known risk factors only. The validity was compared using calibration plots and c-statistics. RESULTS: C-statistics were similar across methods in both development and validation data sets, that is, 0.82 (95% CI 0.82-0.83) in the Swedish National Diabetes Registry and 0.74 (95% CI 0.74-0.75) in Scottish Care Information-diabetes database. Differences were only observed after random introduction of missing data in the most important predictor variable (i.e., age). CONCLUSION: Validity and robustness of median imputation was not dissimilar to more complex methods for handling missing values, provided that the most important predictor variables, such as age, are not missing.

Comparison of recent ceramide-based coronary risk prediction scores in cardiovascular disease patients.

AIM: Cholesterol-based risk prediction is often insufficient in cardiovascular disease (CVD) patients. Ceramides are a new kind of biomarkers for CVD. The Coronary Event Risk Test (CERT) is a validated cardiovascular risk predictor that uses only circulating ceramide levels, determined by coupled liquid chromatography-mass spectrometry, to allocate patients into one of four risk categories. This test has recently been modified (CERT2) by additionally including phosphatidylcholine levels. METHODS AND RESULTS: In this observational cohort study, we have recruited 999 Austrian patients with CVD and followed them for up to 13 years. We found that CERT and CERT2 both predicted cardiovascular events, cardiovascular mortality, and overall mortality. CERT2 had the higher performance compared to CERT and also to the recent cardiovascular risk score of the ESC/EAS guidelines (Systematic COronary Risk Evaluation (SCORE)) for low-risk European countries. Combining CERT2 with the ESC/EAS-SCORE, predictive capacity was further increased leading to a hazard ratio of 3.58 (2.02-6.36; P < 0.001) for cardiovascular events, 11.60 (2.72-49.56; P = 0.001) for cardiovascular mortality, and 9.86 (4.23-22.99; P < 0.001) for overall mortality when patients with very high risk (category 4) were compared to those with low risk (category 1). The use of the combined score instead of the ESC/EAS-SCORE significantly improved the predictive power according to the integrated discrimination improvement index (P = 0.004). CONCLUSION: We conclude that CERT and CERT2 are powerful predictors of cardiovascular events, cardiovascular mortality, and overall mortality in CVD patients. Including phosphatidylcholine to a ceramide-based score increases the predictive performance and is best in combination with classical risk factors as used in the ESC/EAS-SCORE.

The Predictive Value of Carotid Ultrasonography With Cardiovascular Risk Factors-A "SPIDER" Promoting Atherosclerosis.

Insufficient recommendations do not support the clinical use of carotid ultrasonography for further risk stratification in moderate-to-high risk patients with cardiovascular disease (CVD). A literature review was performed to assess six aspects of the research progress and limitations of carotid ultrasonography and carotid atherosclerosis-related risk factors: (1) structures of the carotid intima and media; (2) plaques; (3) inflammation; (4) dynamics of carotid blood flow; (5) early detection and intervention; and (6) risk factors for CVD. Although carotid intima-media thickness and carotid plaques are well-acknowledged independent predictors of CVD risk, normative and cut-off values are difficult to define due to the heterogeneous measurements reported in previous studies. Plaque properties, including location, number, density, and size, become more important risk predictors for cardiovascular disease, but a better approach for clinical use needs to be further established. Three-dimensional ultrasound and contrast-enhanced ultrasound are promising for promoting risk stratification with more details on plaque morphology. Moreover, inflammatory diseases and biomarkers should be evaluated for a full assessment of the inflammatory burden for atherosclerosis. Carotid flow velocity is not only an indicator for stenosis but also a potential risk predictor. Carotid atherosclerosis should be detected and treated early, and additional clinical trials are needed to determine the efficacy of these measures in reducing CVD risk. Cardiovascular risk factors tend to affect carotid plaques, and early treat-to-target therapy might yield clinical benefits. Based on the aforementioned six aspects, we consider that these six important factors act like a "SPIDER" spinning the web of atherosclerosis; a timely comprehensive assessment and intervention may halt the progression to CVD. Carotid ultrasound results should be combined with other atherosclerotic factors, and a comprehensive risk assessment may help to guide cardiovascular prevention decisions.