Efficiency Model Selection in AutoML for CVD Risk Prediction

Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population

Meta-Analysis of Comparison of Effectiveness of Lowering Apolipoprotein B Versus Low-Density Lipoprotein Cholesterol and Nonhigh-Density Lipoprotein Cholesterol for Cardiovascular Risk Reduction in Randomized Trials

Cardiovascular (CV) risk (ie, coronary heart disease [CHD] risk + stroke risk) prediction can be improved with the use of imaging modalities. Carotid intima media thickness (CIMT) and carotid plaque have been used as markers of atherosclerosis and also as predictors of incident CV events. It has been shown that adding either or both to traditional risk prediction models improves CHD risk prediction. There are limited data comparing CIMT and carotid plaque and currently it appears that combining both CIMT and carotid plaque best improves CHD risk prediction. Improvements in ultrasound technology may allow for improved assessment of CIMT, and quantifying and possibly characterizing atherosclerotic plaque in the near future. Whether quantifying plaque will be superior to CIMT measurements or if the combination of both will be the best way to improve CVD (CHD + stroke) risk prediction with ultrasound remains to be seen.

Background The rising burden of cardiovascular disease (CVD) has spurred the development of innovative, non-invasive screening methods. Advances in artificial intelligence (AI) and deep learning (DL) applied to retinal imaging offer a promising avenue, as changes in the retinal vasculature can mirror systemic vascular health. Purpose To synthesize evidence from studies using artificial intelligence (AI) algorithms trained on retinal images to predict cardiovascular disease (CVD) risk. Methods A systematic literature search was conducted on MEDLINE and Embase databases up to February 2024 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline using relevant search terms such as; "cardiovascular disease," "artificial intelligence," "deep learning," "retinal imaging," "colour fundus photography," etc. Inclusion criteria were the development of a DL model applied to any ophthalmic imaging modality for predicting CVD risk or outcome. Results Of 9880 studies which were screened, 13 studies were included. All studies included general population databases, while 7 (54%) studies used databases that included patients with pre-existing CVD risk factors. All studies used retinal fundus images as input for the DL models, and most models (92%) analysed characteristics of the retinal vasculature (e.g., vessel calibre, venular dilatation, arteriolar narrowing, microaneurysms) for their prediction. Overall, 18 different CVD risk factors were predicted through DL models, with age (n=13; 100%), sex (n=11; 85%) and smoking status (n=9; 69%) being the most common. Five (38%) studies analysed binary CVD outcomes including incident myocardial infarction, stroke, and coronary atherosclerotic disease; Four (31%) studies compared CVD risk prediction to traditional CVD risk scores (e.g., Framingham risk score, European Systematic Coronary Risk Evaluation, etc.). These studies were able to accurately stratify cumulative CVD events into low, moderate, and high-risk groups via fundus imaging, demonstrating stratification comparable to established CVD risk scores and cardiac imaging modalities. In total, 8 (62%) studies performed an external validation: the area under the receiver operating characteristic curve ranged between 68.2% and 85.9%. Accuracy, specificity and sensitivity were measured in 4 (31%) studies. Respectively, they ranged between 58.3%-82.0%, 40.4-66.0% and 81.0-89.1%. Only one AI model (Reti-CVD) was made publicly available for clinical use. Conclusions In conclusion, recent studies using DL applied to fundus imaging to predict CVD risk mostly examine retinal vasculature to make predictions and can stratify CVD risk comparably to other clinical risk scores. Though many report promising performance, the majority have not been used in real clinical settings. Additional research is required to enable their clinical implementation in a primary care context, or in an ophthalmological setting.

PurposeTo explore the association between waist circumference (WC), estimated cardiopulmonary function (eCRF), and cardiovascular disease (CVD) risk in southern Xinjiang. Update the Framingham model to make it more suitable for the southern Xinjiang population.MethodsData were collected from 7705 subjects aged 30–74 years old in Tumushuke City, the 51st Regiment of Xinjiang Production and Construction Corps. CVD was defined as an individual's first diagnosis of non-fatal acute myocardial infarction, death from coronary heart disease, and fatal or non-fatal stroke. The Cox proportional hazards regression analysis was used to analyze the association between WC, eCRF and CVD risk. Restricted cubic spline plots were drawn to describe the association of the two indicators with CVD risk. We update the model by incorporating the new variables into the Framingham model and re-estimating the coefficients. The discrimination of the model is evaluated using AUC, NRI, and IDI metrics. Model calibration is evaluated using pseudo R2 values.ResultsWC was an independent risk factor for CVD (multivariate HR: 1.603 (1.323, 1.942)), eCRF was an independent protective factor for CVD (multivariate HR: 0.499 (0.369, 0.674)). There was a nonlinear relationship between WC and CVD risk (nonlinear χ2 = 12.43, P = 0.002). There was a linear association between eCRF and CVD risk (non-linear χ2 = 0.27, P = 0.6027). In the male, the best risk prediction effect was obtained when WC and eCRF were added to the model (AUC = 0.763((0.734,0.792)); pseudo R2 = 0.069). In the female, the best risk prediction effect was obtained by adding eCRF to the model (AUC = 0.757 (0.734,0.779); pseudo R2 = 0.107).ConclusionIn southern Xinjiang, WC is an independent risk factor for CVD. eCRF is an independent protective factor for CVD. We recommended adding WC and eCRF in the male model and only eCRF in the female model for better risk prediction.

Extant data on the performance of cardiovascular disease (CVD) risk score models in people living with HIV have not been synthesized. To synthesize available data on the performance of the various CVD risk scores in people living with HIV. PubMed and Embase were searched from inception through January 31, 2021. Selected studies (1) were chosen based on cohort design, (2) included adults with a diagnosis of HIV, (3) assessed CVD outcomes, and (4) had available data on a minimum of 1 CVD risk score. Relevant data related to study characteristics, CVD outcome, and risk prediction models were extracted in duplicate. Measures of calibration and discrimination are presented in tables and qualitatively summarized. Additionally, where possible, estimates of discrimination and calibration measures were combined and stratified by type of risk model. Measures of calibration and discrimination. Nine unique observational studies involving 75 304 people (weighted average age, 42 years; 59 490 male individuals [79%]) living with HIV were included. In the studies reporting these data, 86% were receiving antiretroviral therapy and had a weighted average CD4+ count of 449 cells/μL. Included in the study were current smokers (50%), patients with diabetes (5%), and patients with hypertension (25%). Ten risk prediction scores (6 in the general population and 4 in the HIV-specific population) were analyzed. Most risk scores had a moderate performance in discrimination (C statistic: 0.7-0.8), without a significant difference in performance between the risk scores of the general and HIV-specific populations. One of the HIV-specific risk models (Data Collection on Adverse Effects of Anti-HIV Drugs Cohort 2016) and 2 of the general population risk models (Framingham Risk Score [FRS] and Pooled Cohort Equation [PCE] 10 year) had the highest performance in discrimination. In general, models tended to underpredict CVD risk, except for FRS and PCE 10-year scores, which were better calibrated. There was substantial heterogeneity across the studies, with only a few studies contributing data for each risk score. Results of this systematic review and meta-analysis suggest that general population and HIV-specific CVD risk models had comparable, moderate discrimination ability in people living with HIV, with a general tendency to underpredict risk. These results reinforce the current recommendations provided by the American College of Cardiology/American Heart Association guidelines to consider HIV as a risk-enhancing factor when estimating CVD risk.

Intestinal (i.e. “gut”) permeability may be related to cardiovascular disease (CVD) risk, but biomarkers for gut permeability are limited and associations with CVD risk are unknown – particularly among older adults. This cross-sectional study aimed to determine if serum biomarkers related to gut permeability [intestinal fatty acid-binding protein (iFABP)] and bacterial toxin clearing [cluster of differentiation 14 (CD14), lipopolysaccharide binding protein (LBP)] are associated with CVD risk among older adults. Older adults (n = 38, 72.0 ± 7.1 years old) were stratified by CVD risk category (Adult Treatment Panel: moderate, high-moderate, high). One-way ANOVAs determined differences in each biomarker by risk category, and associations with risk score were evaluated via linear regression and/or Pearson correlations. LBP (p < 0.001), but not iFABP and CD14 (p’s > 0.05), was significantly different between CVD risk categories. Post-hoc tests indicated LBP was higher in the moderate risk compared to both higher risk categories (p < 0.005). LBP was a significant predictor of 10-year CVD risk (β = -0.629; p = 0.001) and evaluation of individual components in the risk score demonstrated a moderate, positive correlation of LBP with total cholesterol (r = 0.326, p = 0.046). Higher circulating concentrations of LBP were associated with lower CVD risk among older adults. Further, total cholesterol was positively associated with circulating levels of LBP. Importantly, cholesterol assists LBP in clearing bacterial toxins from circulation by transporting toxins for removal. These data suggest LBP may be a key component in reducing CVD risk in older adults.

The aim of this review was to perform a critical appraisal of serum inflammatory biomarkers used for the prediction of cardiovascular disease (CVD) risk. We conducted a systematic review of studies listed on MEDLINE and Scopus from January 2000 to December 2023, focused on the prognostic value of serum inflammatory biomarkers [i.e., C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)] in individuals without known CVD. Eligible studies used a multivariate prediction model and included discrimination or risk reclassification analysis. The Quality in Prognostic Studies (QUIPS) tool was used to evaluate study quality and potential bias. Thirty-five studies (i.e., total 208,897 participants) that evaluated the added prognostic value of CRP, IL-6, TNF-α on CVD risk prediction were retrieved. Significant improvements in CVD risk model's predictive ability were observed in 7 out of 32 studies relating CRP and 1 out of 8 studies relating IL-6 with CVD risk. The single study found no added prognostic value of TNF-α use in CVD risk model. The integration of serum inflammatory biomarkers into CVD risk prediction models does not appear to improve risk discrimination models, suggesting that these biomarkers may act as surrogate markers, but not as predictors of atherosclerotic CVD.

Hypothyroidism resulting from a deficiency of thyroid hormones may be associated with dyslipidaemia. Since altered lipid profile is quite important in predicting CVD risk, therefore the present study aimed to investigate any possible association between the levels of certain lipid indices with thyroid profile and their diagnostic ability of cardiometabolic syndrome in patients with hypothyroidism. A cohort of 40 female patients diagnosed with hypothyroidism was included as cases and thirty age and gender matched healthy females were recruited as controls. Fasting blood samples were collected for analysis of thyroid hormones, cholesterol, triglycerides and high density lipoprotein cholesterol levels by Chemiluminescence immuno-assay, and enzymatic colorimetric methods respectively. Differences in all between groups were tested using parametric independent sample T test. Pearson’s correlation or Spearman rank correlation analysis was done to study the correlations among the parameters as appropriate. Receiver operative characteristic (ROC) curve analysis was performed to study diagnostic utility of parameters.Lipid indices were found to be significantly increased in patients with hypothyroidism when compared to the controls. Castelli risk index-I (CRI-I), CRI-II, atherogenic coefficient (AC) and atherogenic index of plasma (AIP), were found to be significant positively correlated with low density lipoprotein cholesterol (LDL) (r=0.923, p=0.0001, r=0.935, p=0.0001,r=0.970, p=0.0001 and r=0.382, p=0.0001 respectively). The ROC curve analysis of lipid indices for cardiometabolic risk reveals that AUC for CRI-I and AC was high and statistically significant than other lipid parameters.The present study suggests that in addition to routine lipid investigations the inclusion of lipid Indices especially CRI-I, AC, were better indices for screening of early detection of cardiometabolic risk in female patients with hypothyroidism.

This study investigates the relationship between the Cardiometabolic Index (CMI) and cardiovascular disease (CVD) risk in diabetic populations using data from the National Health and Nutrition Examination Survey (NHANES) and the China Health and Retirement Longitudinal Study (CHARLS). Understanding the predictive role of CMI in assessing CVD risk is essential for enhancing preventive strategies in diabetic patients. A cross-sectional analysis was conducted on diabetic participants from NHANES (2005-2018) and CHARLS (2011). CMI was calculated based on the waist-to-height ratio and the triglyceride-to-HDL-C ratio. Multivariable logistic regression models and restricted cubic spline analyses were utilised to evaluate the associations between CMI and CVD risk, adjusting for demographic and clinical covariates. In the NHANES cohort (n=2044), a higher CMI was significantly associated with an increased risk of CVD after adjusting for confounding factors (OR=2.01, p=0.0074). Similarly, in the CHARLS cohort (n=3964), a higher CMI was linked to an elevated CVD risk (OR=1.45, p=0.009). Subgroup analyses demonstrated consistent results across various age, gender and health status subgroups. The restricted cubic spline analysis revealed significant non-linear trends between CMI and CVD risk in both cohorts (p<0.05). CMI is a robust and independent predictor of CVD risk among diabetic individuals across different populations. These findings highlight the potential clinical value of incorporating CMI into routine assessments to identify high-risk diabetic patients. Future longitudinal studies are needed to further validate these findings and explore the underlying mechanisms.

Background; Due to the complicated etiology of cardiovascular illnesses, a thorough risk assessment is necessary for screening reasons. Many published studies relate the pregnancy complications and future cardiovascular disease (CVD) risk. Objective; Investigate the association between risk factors of the laboratory measures and adverse pregnancy outcomes (APOs) with level of cardiovascular disorders risk. Methods; Adult women were enrolled in a cross-sectional study, and they were divided into 2 groups according to whether they had a history of adverse pregnancy outcomes or not. Laboratory and clinical measurements were carried out, and The CVD risk was calculated according to Framingham risk score. Results; All women enrolled were over 40 years age, mostly obese, had predominantly A+ve and O+ve blood group phenotypes. As compared to the low risk category, women with a positive history of pregnancy-induced hypertension and preeclampsia were 7.5 times more likely to be in the intermediate group while those with a positive history of stillbirth were 17.2 times more likely to be in the high-risk group. Conclusion; With reference to the low risk category, a positive history of pregnancy-induced hypertension and preeclampsia was predictor for intermediate CVD risk, while a positive history of stillbirth was predictor for high CVD risk.

IntroductionCardiovascular diseases are the leading cause of morbidity and mortality after kidney transplantation. Physical inactivity is an important factor for the development of cardiovascular disease (CVD) risk.AimTo evaluate CVD risk and its association with accelerometer-based physical activity (PA) parameters in kidney transplant recipients (KTRs).Material and methodsThis cross-sectional study included 43 KTRs. Number of steps, total energy expenditure, average sleep and lying times, average metabolic equivalent (MET), and PA duration were assessed with SenseWear Armband. CVD risk was predicted using a web-based interactive tool (HeartScore program).ResultsCVD risk was negatively correlated with number of steps, average MET and PA duration. Average MET and PA duration were significantly higher in KTRs with low CVD risk compared to KTRs with moderate CVD risk (p = 0.004 and p = 0.007, respectively). Average MET, PA duration and number of steps were significantly higher in KTRs with low CVD risk compared to KTRs with high CVD risk (p < 0.001, p < 0.001 and p = 0.009, respectively). Number of steps was higher in KTRs with moderate CVD risk compared to KTRs with high CVD risk (p = 0.010). The linear regression analysis revealed that average MET was a predictor of CVD risk, accounting for 15.9% of the variance.ConclusionsCVD risk is associated with accelerometer-based PA parameters and average MET is a significant predictor of CVD risk after kidney transplantation in KTRs. Wearable technologies can be used to objectively measure PA parameters in order to determine CVD risk and to monitor the efficiency of PA interventions after kidney transplantation.

ObjectiveTo assess the performance of cardiovascular disease (CVD) risk equations in Indigenous Australians.MethodsWe conducted an individual participant meta-analysis using longitudinal data of 3618 Indigenous Australians (55% women) aged 30–74 years...

BackgroundNo routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of...

Aims:To examine whether electrocardiography (ECG) could provide additional values to the traditional risk factors for cardiovascular disease (CVD) risk prediction among different cardiovascular risk subgroups.Methods:A total of 7,872 community residents aged ≥40 years were followed up for a median of 4.5 years. A 12-lead resting ECG was examined for participants at baseline. CVD events including myocardial infarction, stroke and cardiovascular mortality were collected. Cox proportional hazards models were used and models of traditional risk factors with and without ECG were compared.Results:At baseline, 2,470 participants (31.3%) had ECG abnormalities. During follow-up, 464 participants developed CVD events. ECG abnormalities were associated with an increased risk of CVD after adjustment for the traditional risk factors in participants with a 10-year atherosclerotic CVD (ASCVD) risk ≥10% (hazard ratio, HR: 1.45; 95% confidence interval, CI: 1.11, 1.91). Adding ECG abnormalities to the traditional CVD risk factors improved reclassification for those who did not experience events [net reclassification index: 8.0% (95% CI: 2%, 19.5%)], discrimination (integrated discrimination improvement: 0.7% (95% CI: 0.1%, 1.9%), and calibration (goodness of fit P value from 0.600 to 0.873) in participants with a 10-year ASCVD risk ≥10%. However, no significant association and improvement were found in participants with a 10-year ASCVD risk <10%.Conclusions:ECG screening might provide a marginal improvement in CVD risk prediction in adults at high risk. However, ECG should not be recommended in adults at low risk.