Pooled Cohort Equations Research Articles

Deep learning-based prediction for significant coronary artery stenosis on coronary computed tomography angiography in asymptomatic populations.

Although coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates regarding its use as a screening tool for the asymptomatic population. Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the individuals who would benefit from undergoing CCTA among apparently healthy asymptomatic adults. We retrospectively reviewed 11,180 individuals who underwent CCTA as part of routine health check-ups between 2012 and 2019. The main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. We developed a prediction model using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores. In the cohort of 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) presented with significant coronary artery stenosis on CCTA. Among the ML methods employed, a neural network with multi-task learning (19 selected features), one of the DL methods, was selected due to its superior performance, with an area under the curve (AUC) of 0.782 and a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were also included as important features of the model. We successfully developed the neural network with multi-task learning for the detection of CCTA-derived stenosis of ≥70% in asymptomatic populations. Our findings suggest that this model may provide more precise indications for the use of CCTA as a screening tool to identify individuals at a higher risk, even in asymptomatic populations, in clinical practice.

Determining Percentiles of Atherosclerotic Cardiovascular Risk According to Sex and Age in a Healthy Brazilian Population.

Expressing the risk of atherosclerotic cardiovascular disease (ASCVD) as percentiles of the distribution according to sex and age may provide a better perception of the risk. To determine percentiles of the 10-year ASCVD risk distribution according to sex and age in a sample of the Brazilian population; to characterize individuals at low 10-year risk but high risk percentile. We analyzed individuals aged 40 to 75 years who underwent routine health evaluations from 2010 to 2020. Persons with known clinical ASCVD, diabetes mellitus, chronic kidney disease, or LDL-cholesterol ≥ 190 mg/dL were excluded. The 10-year ASCVD risk was calculated by the ACC/AHA pooled cohort equations. Local polynomial regression was used to determine risk percentiles. Two-sided p-values < 0.050 were considered statistically significant. Our sample comprised 54,145 visits (72% male, median age [interquartile range] 48 [43, 53] years). We constructed sex-specific graphs plotting age against ASCVD risk corresponding to the 10th, 25th, 50th, 75th, and 90th percentiles. Most males up to 47 years and females up to 59 years above the 75th percentile had a 10-year risk < 5%. Individuals at low 10-year risk and risk percentile ≥ 75th had a high prevalence of excess weight and median (interquartile range) LDL-cholesterol levels 136 (109, 158) mg/dL (males) and 126 (105, 147) mg/dL (females). We established ASCVD risk percentiles according to sex and age in a large sample of the Brazilian population. This approach may increase risk awareness and help identify younger persons at low 10-year risk who may benefit from more aggressive risk factor control.

Association between socio-economic status and estimated atherosclerotic cardiovascular disease risk: results from a middle-aged population-based study

ObjectivesThe association between cardiovascular disease (CVD) risk and socio-economic status (SES) remains poorly studied. The purpose of this study was to investigate the relationship between SES and estimated 10-year atherosclerotic cardiovascular disease (ASCVD) risk among the general UK Biobank population. Study designThis was a population-based study. MethodsAmong 311,928 volunteers (47.7% men) of the UK Biobank population, SES was assessed by a questionnaire, and ASCVD risk was calculated using pooled cohort equation models. Associations between SES and ASCVD risk were estimated using multiple gender-specific regressions. ResultsThe findings from this study showed that men had higher estimated 10-year ASCVD risk than women (8.6% vs 2.7%; P < 0.001), higher education level (38.3% vs 36.2%; P < 0.001), higher income level (31.0% vs 25.1%; P < 0.001), higher levels of employment (65.4% vs 60.5%; P < 0.001) and higher scores of Townsend deprivation (P < 0.001). Using the multiple logistic regression model, a decreased 10-year ASCVD risk in men was associated with high income level (odds ratio [OR] = 0.64 [95% confidence interval {CI} 0.61–0.68]; P < 0.001), high educational level (OR = 0.71 [95% CI 0.68–0.74]; P < 0.001), higher Townsend deprivation quintile (OR = 0.81 [95% CI 0.78–0.85]; P < 0.001) and employed status (OR = 0.74 [95% CI 0.69–0.80]; P < 0.001). The same results were observed in women, with high income level (OR = 0.68 [95% CI 0.55–0.68]; P < 0.001), high educational level (OR = 0.87 [95% CI 0.82–0.93]; P < 0.001), higher Townsend deprivation quintile (OR = 0.74 [95% CI 0.69–0.80]; P < 0.001) and employed status (OR = 0.53 [95% CI 0.45–0.63]; P < 0.001) being associated with a lower 10-year ASCVD risk. When considering the false discovery rate logworth analysis, SES factors presented a similar contribution to CVD risk as lifestyle factors. ConclusionsHealth policies should consider the SES factors identified in this study, in addition to traditional risk factors, when designing prevention campaigns for CVD. Further research is required to improve the ASCVD risk prediction models among different SES variables.

Validation and comparison of cardiovascular risk prediction equations in Chinese patients with Type 2 diabetes.

For patients with diabetes, the European guidelines updated the cardiovascular disease (CVD) risk prediction recommendations using diabetes-specific models with age-specific cut-offs, whereas American guidelines still advise models derived from the general population. We aimed to compare the performance of four cardiovascular risk models in diabetes populations. Patients with diabetes from the CHERRY study, an electronic health records-based cohort study in China, were identified. Five-year CVD risk was calculated using original and recalibrated diabetes-specific models [Action in Diabetes and Vascular disease: PreterAx and diamicroN-MR Controlled Evaluation (ADVANCE) and the Hong Kong cardiovascular risk model (HK)] and general population-based models [Pooled Cohort Equations (PCE) and Prediction for Atherosclerotic cardiovascular disease Risk in China (China-PAR)]. During a median 5.8-year follow-up, 46 558 patients had 2605 CVD events. C-statistics were 0.711 [95% confidence interval: 0.693-0.729] for ADVANCE and 0.701 (0.683-0.719) for HK in men, and 0.742 (0.725-0.759) and 0.732 (0.718-0.747) in women. C-statistics were worse in two general population-based models. Recalibrated ADVANCE underestimated risk by 1.2% and 16.8% in men and women, whereas PCE underestimated risk by 41.9% and 24.2% in men and women. With the age-specific cut-offs, the overlap of the high-risk patients selected by every model pair ranged from only 22.6% to 51.2%. When utilizing the fixed cut-off at 5%, the recalibrated ADVANCE selected similar high-risk patients in men (7400) as compared to the age-specific cut-offs (7102), whereas age-specific cut-offs exhibited a reduction in the selection of high-risk patients in women (2646 under age-specific cut-offs vs. 3647 under fixed cut-off). Diabetes-specific CVD risk prediction models showed better discrimination for patients with diabetes. High-risk patients selected by different models varied significantly. Age-specific cut-offs selected fewer patients at high CVD risk especially in women.

Comparison of the European and US guidelines for lipid-lowering therapy in primary prevention of cardiovascular disease.

Population-wide impacts of new guidelines in the primary prevention of atherosclerotic cardiovascular disease (ASCVD) should be explored in independent cohorts. Assess and compare the lipid-lowering therapy eligibility and predictive classification performance of 2016 and 2021 European Society of Cardiology (ESC), 2019 American Heart Association/American College of Cardiology (AHA/ACC), and 2022 US Preventive Services Task Force (USPSTF) guidelines. Participants from the CoLaus|PsyCoLaus study, without ASCVD and not taking lipid-lowering therapy at baseline. Derivation of 10-year risk for ASCVD using Systematic COronary Risk Evaluation (SCORE1), SCORE2 [including SCORE2-Older Persons (SCORE2-OP)], and pooled cohort equation. Computation of the number of people eligible for lipid-lowering therapy based on each guideline and assessment of discrimination and calibration metrics of the risk models using first incident ASCVD as an outcome. Among 4,092 individuals, 158 (3.9%) experienced an incident ASCVD during a median follow-up of 9 years (interquartile range, 1.1). Lipid-lowering therapy was recommended or considered in 40.2% (95% confidence interval, 38.2-42.2), 26.4% (24.6-28.2), 28.6% (26.7-30.5), and 22.6% (20.9-24.4) of women and in 62.1% (59.8-64.3), 58.7% (56.4-61.0), 52.6% (50.3-54.9), and 48.4% (46.1-50.7) of men according to the 2016 ESC, 2021 ESC, 2019 AHA/ACC, and 2022 USPSTF guidelines, respectively. 43.3 and 46.7% of women facing an incident ASCVD were not eligible for lipid-lowering therapy at baseline according to the 2021 ESC and 2022 USPSTF, compared with 21.7 and 38.3% using the 2016 ESC and 2019 AHA/ACC, respectively. Both the 2022 USPSTF and 2021 ESC guidelines particularly reduced lipid-lowering therapy eligibility in women. Nearly half of women who faced an incident ASCVD were not eligible for lipid-lowering therapy.

Machine learning reveals sex-specific associations between cardiovascular risk factors and incident atherosclerotic cardiovascular disease

We aimed to investigate sex-specific associations between cardiovascular risk factors and atherosclerotic cardiovascular disease (ASCVD) risk using machine learning. We studied 258,279 individuals (132,505 [51.3%] men and 125,774 [48.7%] women) without documented ASCVD who underwent national health screening. A random forest model was developed using 16 variables to predict the 10-year ASCVD in each sex. The association between cardiovascular risk factors and 10-year ASCVD probabilities was examined using partial dependency plots. During the 10-year follow-up, 12,319 (4.8%) individuals developed ASCVD, with a higher incidence in men than in women (5.3% vs. 4.2%, P < 0.001). The performance of the random forest model was similar to that of the pooled cohort equations (area under the receiver operating characteristic curve, men: 0.733 vs. 0.727; women: 0.769 vs. 0.762). Age and body mass index were the two most important predictors in the random forest model for both sexes. In partial dependency plots, advanced age and increased waist circumference were more strongly associated with higher probabilities of ASCVD in women. In contrast, ASCVD probabilities increased more steeply with higher total cholesterol and low-density lipoprotein (LDL) cholesterol levels in men. These sex-specific associations were verified in the conventional Cox analyses. In conclusion, there were significant sex differences in the association between cardiovascular risk factors and ASCVD events. While higher total cholesterol or LDL cholesterol levels were more strongly associated with the risk of ASCVD in men, older age and increased waist circumference were more strongly associated with the risk of ASCVD in women.

Contribution of Migraine to Cardiovascular Disease Risk Prediction

BackgroundMigraine with aura (MA) is associated with cardiovascular disease (CVD) independently from traditional vascular risk factors. However, the importance of MA on CVD occurrence relative to existing cardiovascular prediction tools remains unclear. ObjectivesIn this study, we sought to determine if adding MA status to 2 CVD risk prediction models improves risk prediction. MethodsParticipants enrolled in the Women’s Health Study self-reported MA status and were followed for incident CVD events. We included MA status as a covariable in the Reynolds Risk Score and the American Heart Association (AHA)/American College of Cardiology (ACC) pooled cohort equation and assessed discrimination (Harrell c-index), continuous and categorical net reclassification improvement (NRI), and integrated discrimination improvement (IDI). ResultsMA status was significantly associated with CVD after including covariables in the Reynolds Risk Score (HR: 2.09; 95% CI: 1.54-2.84) and the AHA/ACC score (HR: 2.10; 95% CI: 1.55-2.85). Adding information on MA status improved discrimination of the Reynolds Risk Score model (from 0.792 to 0.797; P = 0.02) and the AHA/ACC score model (from 0.793 to 0.798; P = 0.01). We observed a small but statistically significant improvement in the IDI and continuous NRI after adding MA status to both models. We did not, however, observe significant improvements in the categorical NRI. ConclusionsAdding information on MA status to commonly used CVD risk prediction algorithms enhanced model fit but did not substantially improve risk stratification among women. Despite the strong association of migraine with CVD risk, the relatively low prevalence of MA compared with other CV risk factors limits its usefulness in improving risk classification at the population level.

Development of a tool to predict the risk of incident heart failure in a general population: the HUNT for HF risk score.

Currently, no incident heart failure (HF) risk score that is in regular use in a general population is available. We aimed to develop this and compare with existing HF risk scores. Participants in the third wave (2006-08) of the population-based Trøndelag Health Study 3 (HUNT3) were included if they reported no previous HF. Any hospital diagnoses captured during follow-up (until the end of 2018) of HF, cardiomyopathy, or hypertensive heart disease were assessed by an experienced cardiologist. Valid HF events were defined as symptoms/signs of HF and objective evidence of structural/functional abnormality of the heart at rest. The model was compared with slightly modified HF risk scores (the Health Aging and Body Composition HF risk score, the Framingham HF risk score, the Pooled Cohort equations to Prevent HF risk score, and NORRISK 2). Among 36511 participants (mean±SD age of 57.9±13.3years, 55.4% female), with a mean follow-up of 10.2±1.3years, 1366 developed HF (incidence rate of 3.66 per 1000 participant years). Out of the 38 relevant clinical variables assessed, we identified 12 (atrial fibrillation being the strongest) that independently predicted an HF event. The final model demonstrated good discrimination (C statistics=0.904) and calibration, was stable in internal validation, and performed well compared with existing risk scores. The model identified that, at enrolment, 31391 (86%), 2386 (7%), 1246 (3%), and 1488 (4%) had low, low-intermediate, high-intermediate, and high 10-year HF risk, respectively. Twelve clinical variables independently predicted 10-year HF risk. The model may serve well as the foundation of a practical, online risk score for HF in general practice. ClinicalTrials.gov Identifier: NCT04648852.

Social determinants of racial health inequities

Social determinants of racial health inequities

Coronary Artery Calcium Score and Polygenic Risk Score for the Prediction of Coronary Heart Disease Events

Coronary artery calcium score and polygenic risk score have each separately been proposed as novel markers to identify risk of coronary heart disease (CHD), but no prior studies have directly compared these markers in the same cohorts. To evaluate change in CHD risk prediction when a coronary artery calcium score, a polygenic risk score, or both are added to a traditional risk factor-based model. Two observational population-based studies involving individuals aged 45 years through 79 years of European ancestry and free of clinical CHD at baseline: the Multi-Ethnic Study of Atherosclerosis (MESA) study involved 1991 participants at 6 US centers and the Rotterdam Study (RS) involved 1217 in Rotterdam, the Netherlands. Traditional risk factors were used to calculate CHD risk (eg, pooled cohort equations [PCEs]), computed tomography for the coronary artery calcium score, and genotyped samples for a validated polygenic risk score. Model discrimination, calibration, and net reclassification improvement (at the recommended risk threshold of 7.5%) for prediction of incident CHD events were assessed. The median age was 61 years in MESA and 67 years in RS. Both log (coronary artery calcium+1) and polygenic risk score were significantly associated with 10-year risk of incident CHD (hazards ratio per SD, 2.60; 95% CI, 2.08-3.26 and 1.43; 95% CI, 1.20-1.71, respectively), in MESA. The C statistic for the coronary artery calcium score was 0.76 (95% CI, 0.71-0.79) and for the polygenic risk score, 0.69 (95% CI, 0.63-0.71). The change in the C statistic when each was added to the PCEs was 0.09 (95% CI, 0.06-0.13) for the coronary artery calcium score, 0.02 (95% CI, 0.00-0.04) for the polygenic risk score, and 0.10 (95% CI, 0.07-0.14) for both. Overall categorical net reclassification improvement was significant when the coronary artery calcium score (0.19; 95% CI, 0.06-0.28) but was not significant when the polygenic risk score (0.04; 95% CI, -0.05 to 0.10) was added to the PCEs. Calibration of the PCEs and models with coronary artery calcium and/or polygenic risk scores was adequate (all χ2<20). Subgroup analysis stratified by the median age demonstrated similar findings. Similar findings were observed for 10-year risk in RS and in longer-term follow-up in MESA (median, 16.0 years). In 2 cohorts of middle-aged to older adults from the US and the Netherlands, the coronary artery calcium score had better discrimination than the polygenic risk score for risk prediction of CHD. In addition, the coronary artery calcium score but not the polygenic risk score significantly improved risk discrimination and risk reclassification for CHD when added to traditional risk factors.

Prediction of the 10-year risk of atherosclerotic cardiovascular disease in the Korean population.

Proper risk assessment is important for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, no validated risk prediction tools are currently in use in Korea. This study sought to develop a 10-year risk prediction model for incident ASCVD. Using the National Sample Cohort of Korea, 325,934 subjects aged 20-80 years without previous ASCVD were enrolled. ASCVD was defined as a composite of cardiovascular death, myocardial infarction, and stroke. The Korean atherosclerotic cardiovas cular disease risk prediction (K-CVD) model was developed separately for men and women using the development dataset and validated in the validation dataset. Furthermore, the model performance was compared with the Framingham risk score (FRS) and pooled cohort equation (PCE). Over 10 years of follow-up, 4,367 ASCVD events occurred in the overall population. The predictors of ASCVD included in the model were age, smoking status, diabetes, systolic blood pressure, lipid profiles, urine protein, and lipid-lowering and blood pressure-lowering treatment. The K-CVD model had good discrimination and strong calibration in the validation dataset (time-dependent area under the curve=0.846; 95% confidence interval, 0.828 to 0.864; calibration χ2=4.73, goodness-of-fit p=0.32). Compared with our model, both FRS and PCE showed worse calibration, overestimating ASCVD risk in the Korean population. Through a nationwide cohort, we developed a model for 10-year ASCVD risk prediction in a contemporary Korean population. The K-CVD model showed excellent discrimination and calibration in Koreans. This population-based risk prediction tool would help to appropriately identify high-risk individuals and provide preventive interventions in the Korean population.

Cardiology Encounters for Underrepresented Racial and Ethnic Groups with Human Immunodeficiency Virus and Borderline Cardiovascular Disease Risk.

Underrepresented racial and ethnic groups (UREGs) with HIV have a higher risk of cardiovascular disease (CVD) compared with the general population. Referral to a cardiovascular specialist improves CVD risk factor management in high-risk individuals. However, patient and provider factors impacting the likelihood of UREGs with HIV to have an encounter with a cardiologist are unknown. We evaluated a cohort of UREGs with HIV and borderline CVD risk (10-year risk ≥ 5% by the pooled cohort equations or ≥ 7.5% by Framingham risk score). Participants received HIV-related care from 2014-2020 at four academic medical centers in the United States (U.S.). Adjusted Cox proportional hazards regression was used to estimate the association of patient and provider characteristics with time to first ambulatory cardiology encounter. A total of 2,039 people with HIV (PWH) and borderline CVD risk were identified. The median age was 45years (IQR: 36-50); 52% were female; and 94% were Black. Of these participants, 283 (14%) had an ambulatory visit with a cardiologist (17% of women vs. 11% of men, p < .001). In fully adjusted models, older age, higher body mass index (BMI), atrial fibrillation, multimorbidity, urban residence, and no recent insurance were associated with a greater likelihood of an encounter with a cardiologist. In UREGs with HIV and borderline CVD risk, the strongest determinants of a cardiology encounter were diagnosed CVD, insurance type, and urban residence. Future research is needed to determine the extent to which these encounters impact CVD care practices and outcomes in this population. ClinicalTrials.gov Identifier: NCT04025125.

Epidemiology of Diabetes and Atherosclerotic Cardiovascular Disease Among Asian American Adults: Implications, Management, and Future Directions: A Scientific Statement From the American Heart Association.

Asian American individuals make up the fastest growing racial and ethnic group in the United States. Despite the substantial variability that exists in type 2 diabetes and atherosclerotic cardiovascular disease risk among the different subgroups of Asian Americans, the current literature, when available, often fails to examine these subgroups individually. The purpose of this scientific statement is to summarize the latest disaggregated data, when possible, on Asian American demographics, prevalence, biological mechanisms, genetics, health behaviors, acculturation and lifestyle interventions, pharmacological therapy, complementary alternative interventions, and their impact on type 2 diabetes and atherosclerotic cardiovascular disease. On the basis of available evidence to date, we noted that the prevalences of type 2 diabetes and stroke mortality are higher in all Asian American subgroups compared with non-Hispanic White adults. Data also showed that atherosclerotic cardiovascular disease risk is highest among South Asian and Filipino adults but lowest among Chinese, Japanese, and Korean adults. This scientific statement discusses the biological pathway of type 2 diabetes and the possible role of genetics in type 2 diabetes and atherosclerotic cardiovascular disease among Asian American adults. Challenges to provide evidence-based recommendations included the limited data on Asian American adults in risk prediction models, national surveillance surveys, and clinical trials, leading to significant research disparities in this population. The large disparity within this population is a call for action to the public health and clinical health care community, for whom opportunities for the inclusion of the Asian American subgroups should be a priority. Future studies of atherosclerotic cardiovascular disease risk in Asian American adults need to be adequately powered, to incorporate multiple Asian ancestries, and to include multigenerational cohorts. With advances in epidemiology and data analysis and the availability of larger, representative cohorts, furthering refining the Pooled Cohort Equations, in addition to enhancers, would allow better risk estimation in segments of the population. Last, this scientific statement provides individual- and community-level intervention suggestions for health care professionals who interact with the Asian American population.

Performance of the pooled cohort equations in cancer survivors: the Atherosclerosis Risk in Communities study.

Cancer survivors may have elevated atherosclerotic cardiovascular disease (ASCVD) risk. Therefore, we tested how accurately the American College of Cardiology/American Heart Association 2013 pooled cohort equations (PCEs) predict 10-year ASCVD risk in cancer survivors. To estimate the calibration and discrimination ofthe PCEs in cancer survivors compared to non-cancer participants in the Atherosclerosis Risk in Communities (ARIC) study. We evaluated the PCEs' performance among 1244 cancer survivors and 3849 cancer-free participants who were free of ASCVD at the start of follow-up. Each cancer survivor was incidence-density matched with up to five controls by age, race, sex, and study center. Follow-up began at the first study visit at least 1year after the diagnosis date of the cancer survivor and finished at the ASCVD event, death, or end of follow-up. Calibration and discrimination were assessed and compared between cancer survivors and cancer-free participants. Cancer survivors had higher PCE-predicted risk, at 26.1%, compared with 23.1% for cancer-free participants. There were 110 ASCVD events in cancer survivors and 332 ASCVD events in cancer-free participants. The PCEs overestimated ASCVD risk in cancer survivors and cancer-free participants by 45.6% and 47.4%, respectively, with poor discrimination in both groups (C-statistic for cancer survivors = 0.623; for cancer-free participants, C = 0.671). The PCEs overestimated ASCVD risk in all participants. The performance of the PCEs was similar in cancer survivors and cancer-free participants. Our findings suggest that ASCVD risk prediction tools tailored to survivors of adult cancers may not be needed.

Association of cardiovascular health and risk prediction algorithms with subclinical atherosclerosis identified by carotid ultrasound

The requirement for laboratory tests to assess conventional cardiovascular disease (CVD) risk may be a barrier to the early detection and management of atherosclerosis in some population groups. A simpler risk assessment could facilitate detection of CVD. The association of the Fuster-BEWAT Score (FBS), Framingham Risk Score (FRS), and Pooled Cohort Equation (PCE) with the presence of carotid plaque was investigated, with the intention of developing a stepped screening process for the primary prevention of CVD. Asymptomatic participants with a family history of premature CVD had an absolute cardiovascular disease risk (ACVDR) score calculated using the FBS, FRS, and PCE risk equations. This risk classification was compared with the presence or absence of carotid plaque on ultrasound. Prediction of carotid plaque presence by risk scores and risk factors was assessed by logistic regression and area under the curve (AUC) for discrimination and diagnostic performance. A classification and regression-tree (CART) model was obtained for stratification of risk assessment. Risk score calculation and ultrasound scanning were performed in 1031 participants, of whom 51 had carotid plaques. Participants with plaque and male sex showed higher risk (higher PCE and FRS and lower FBS, as higher scores of FBS indicate better cardiovascular health). Participants ≤50 years of age showed the FBS was a significant predictor; there was a reduced likelihood of plaque presence with a higher score (OR 0.54, 95% CI 0.39-0.75, P < .01). Higher ACVDR (evidenced by higher PCE and FRS scores and lower FBS score) was associated with an increased likelihood of carotid plaque; however, the FBS and the addition of risk factors not included in the equation showed the highest AUC (AUC = 0.76, P < .001). CART modeling showed that participants with FBS between 6 and 9 would be recommended for further risk stratification using the PCE, whereupon a PCE score ≥5% conferred an increased risk and greater possibility for plaque. Validation of the model using a different cohort showed similar risk stratification for plaque presence according to level of risk by CART analysis. FBS was able to identify the presence of carotid plaque in asymptomatic individuals. Its use for initial risk delineation might improve the selection of patients for more specific and complex assessment, reducing cost and time.

Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment.

Lower kidney function is known to enhance cardiovascular disease (CVD) risk. It is unclear which estimated glomerular filtration rate (eGFR) equation best predict an increased CVD risk and if prediction can be improved by integration of multiple kidney function markers. We performed structural equation modeling (SEM) of kidney markers and compared the performance of the resulting pooled indexes with established eGFR equations to predict CVD risk in a 10-year longitudinal population-based design. We split the study sample into a set of participants with only baseline data (n = 647; model-building set) and a set with longitudinal data (n = 670; longitudinal set). In the model-building set, we fitted five SEM models based on serum creatinine or creatinine-based eGFR (eGFRcre), cystatin C or cystatin-based eGFR (eGFRcys), uric acid (UA), and blood urea nitrogen (BUN). In the longitudinal set, 10-year incident CVD risk was defined as a Framingham risk score (FRS)>5% and a pooled cohort equation (PCE)>5%. Predictive performances of the different kidney function indexes were compared using the C-statistic and the DeLong test. In the longitudinal set, a SEM-based estimate of latent kidney function based on eGFRcre, eGFRcys, UA, and BUN showed better prediction performance for both FRS>5% (C-statistic: 0.70; 95% CI: 0.65-0.74) and PCE>5% (C-statistic: 0.75; 95%CI: 0.71-0.79) than other SEM models and different eGFR formulas (DeLong test p-values<3.21×10-6 for FRS>5% and <1.49×10-9 for PCE>5%, respectively). However, the new derived marker could not outperform eGFRcys (DeLong test p-values = 0.88 for FRS>5% and 0.20 for PCE>5%, respectively). SEM is a promising approach to identify latent kidney function signatures. However, for incident CVD risk prediction, eGFRcys could still be preferrable given its simpler derivation.

Proteomic cardiovascular risk assessment in chronic kidney disease.

Chronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models. Elastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis. In two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.

Cardiovascular disease risk assessment using a deep-learning-based retinal biomarker: a comparison with existing risk scores.

This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD. We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively. The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools.

Cardiovascular Disease Risk Estimation for Transgender and Gender-Diverse Patients: Cross-Sectional Analysis of Baseline Data From the LITE Plus Cohort Study

Approximately 2% of the U.S. population identifies as transgender, and transgender people experience disproportionate rates of cardiovascular disease mortality. However, widely used cardiovascular disease risk estimators have not been validated in this population. This study sought to determine the impact on statin therapy recommendations using 3 different approaches to operationalizing sex in the American Health Association/American College of Cardiology Pooled Cohort Equation Risk Estimator. This is a cross-sectional analysis of baseline clinical data from LITE Plus, a prospective cohort study of Black and/or Latina transgender women with HIV. Data were collected from October 2020 to June 2022 and used to calculate Pooled Cohort Equation scores. The 102 participants had a mean age of 43 years. A total of 88% were Black, and 18% were Latina. A total of 79% were taking gender-affirming hormones. The average Pooled Cohort Equation risk score was 6% when sex assigned at birth was used and statins would be recommended for the 31% with Pooled Cohort Equation >7.5%. The average risk score was 4%, and 18% met the criteria for statin initiation when current gender was used; the mean risk score was 5%, and 22% met the criteria for statin initiation when current hormone therapy was used. Average Pooled Cohort Equation risk scores vary substantially depending on the approach to operationalizing the sex variable, suggesting that widely used cardiovascular risk estimators may be unreliable predictors of cardiovascular disease risk in transgender populations. Collection of sex, gender, and hormone use in longitudinal studies of cardiovascular health is needed to address this important limitation of current risk estimators.

Projecting the Health and Economic Burden of Cardiovascular Disease Among People with Type 2 Diabetes, 2022-2031.

The aim was to project the health and economic outcomes of cardiovascular disease (CVD) among people with type 2 diabetes from Australian public healthcare and societal perspectives over the next decade. A dynamic multistate model with yearly cycles was developed to project cardiovascular events among Australians with type 2 diabetes aged 40-89 years from 2022 to 2031. CVD risk (myocardial infarction [MI] and stroke) in the type 2 diabetes population was estimated using the 2013 pooled cohort equation, and recurrent cardiovascular event rates in the type 2 diabetes with established CVD population were obtained from the global Reduction of Atherothrombosis for Continued Health (REACH) registry. Costs and utilities were derived from published sources. Outcomes included fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years (QALYs), total healthcare costs, and total productivity losses. The annual discount rate was 5%, applied to outcomes and costs. Between 2022 and 2031, a total of 83,618 non-fatal MIs (95% uncertainty interval [UI] 83,170-84,053) and 58,774 non-fatal strokes (95% UI 58,458-59,013) were projected. Total years of life lived and QALYs (discounted) were projected to be 9,549,487 (95% UI 9,416,423-9,654,043) and 6,632,897 (95% UI 5,065,606-7,591,679), respectively. Total healthcare costs and total lost productivity costs (discounted) were projected to be 9.59 billion Australian dollars (AU$) (95% UI 1.90-30.45 billion) and AU$9.07 billion (95% UI 663.53 million-33.19 billion), respectively. CVD in people with type 2 diabetes will substantially impact the Australian healthcare system and society over the next decade. Future work to investigate different strategies to optimize the control of risk factors for the prevention and treatment of CVD in type 2 diabetes in Australia is warranted.