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Inflammation, Cholesterol, Lipoprotein(a), and 30-Year Cardiovascular Outcomes in Women. The New England journal of medicine BACKGROUND:High-sensitivity C-reactive protein (CRP), low-density lipoprotein (LDL) cholesterol, and lipoprotein(a) levels contribute to 5-year and 10-year predictions of cardiovascular risk and represent distinct pathways for pharmacologic intervention. More information about the usefulness of these biomarkers for predicting cardiovascular risk over longer periods of time in women is needed because early-life intervention represents an important risk-reduction method. METHODS:We measured high-sensitivity CRP, LDL cholesterol, and lipoprotein(a) levels at baseline in 27,939 initially healthy U.S. women who were subsequently followed for 30 years. The primary end point was a first major adverse cardiovascular event, which was a composite of myocardial infarction, coronary revascularization, stroke, or death from cardiovascular causes. We calculated the adjusted hazard ratios and 95% confidence intervals across quintiles of each biomarker, along with 30-year cumulative incidence curves adjusted for age and competing risks. RESULTS:The mean age of the participants at baseline was 54.7 years. During the 30-year follow-up, 3662 first major cardiovascular events occurred. Quintiles of increasing baseline levels of high-sensitivity CRP, LDL cholesterol, and lipoprotein(a) all predicted 30-year risks. Covariable-adjusted hazard ratios for the primary end point in a comparison of the top with the bottom quintile were 1.70 (95% confidence interval [CI], 1.52 to 1.90) for high-sensitivity CRP, 1.36 (95% CI, 1.23 to 1.52) for LDL cholesterol, and 1.33 (95% CI, 1.21 to 1.47) for lipoprotein(a). Findings for coronary heart disease and stroke appeared to be consistent with those for the primary end point. Each biomarker showed independent contributions to overall risk. The greatest spread for risk was obtained in models that incorporated all three biomarkers. CONCLUSIONS:A single combined measure of high-sensitivity CRP, LDL cholesterol, and lipoprotein(a) levels among initially healthy U.S. women was predictive of incident cardiovascular events during a 30-year period. These data support efforts to extend strategies for the primary prevention of atherosclerotic events beyond traditional 10-year estimates of risk. (Funded by the National Institutes of Health; Women's Health Study ClinicalTrials.gov number, NCT00000479.). 10.1056/NEJMoa2405182
Comparison of triglyceride glucose index and modified triglyceride glucose indices in prediction of cardiovascular diseases in middle aged and older Chinese adults. Cardiovascular diabetology BACKGROUND:Triglyceride and glucose (TyG) index, a surrogate marker of insulin resistance, has been validated as a predictor of cardiovascular disease. However, effects of TyG-related indices combined with obesity markers on cardiovascular diseases remained unknown. We aimed to investigate the associations between TyG index and modified TyG indices with new-onset cardiovascular disease and the time-dependent predictive capacity using a national representative cohort. METHODS:This study is a retrospective observational cohort study using data from China Health and Retirement Longitudinal Study (CHARLS) of 7 115 participants. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. The modified TyG indices were developed combining TyG with body mass index (BMI), waist circumference (WC) and waist-to-height ratio (WHtR). We used adjusted Cox proportional hazards regression to analyze the association and predictive capacity based on hazard ratio (HR) and Harrell's C-index. RESULTS:Over a 7-year follow-up period, 2136 participants developed cardiovascular disease, including 1633 cases of coronary heart disease and 719 cases of stroke. Compared with the lowest tertile group, the adjusted HR (95% CI) for new-onset cardiovascular disease in the highest tertile for TyG, TyG-BMI, TyG-WC, and TyG-WHtR were 1.215 (1.088-1.356), 1.073 (0.967-1.191), 1.078 (0.970-1.198), and 1.112 (1.002-1.235), respectively. The C-indices of TyG index for cardiovascular disease onset were higher than other modified TyG indices. Similar results were observed for coronary heart disease and stroke. CONCLUSION:TyG and TyG-WhtR were significantly associated with new-onset cardiovascular diseases, and TyG outperformed the modified TyG indices to identify individuals at risk of incident cardiovascular event. 10.1186/s12933-024-02278-z
Childhood Risk Factors and Prediction of Adult Cardiovascular End Points. The New England journal of medicine 10.1056/NEJMe2203743
Development and validation of a new algorithm for improved cardiovascular risk prediction. Nature medicine QRISK algorithms use data from millions of people to help clinicians identify individuals at high risk of cardiovascular disease (CVD). Here, we derive and externally validate a new algorithm, which we have named QR4, that incorporates novel risk factors to estimate 10-year CVD risk separately for men and women. Health data from 9.98 million and 6.79 million adults from the United Kingdom were used for derivation and validation of the algorithm, respectively. Cause-specific Cox models were used to develop models to predict CVD risk, and the performance of QR4 was compared with version 3 of QRISK, Systematic Coronary Risk Evaluation 2 (SCORE2) and atherosclerotic cardiovascular disease (ASCVD) risk scores. We identified seven novel risk factors in models for both men and women (brain cancer, lung cancer, Down syndrome, blood cancer, chronic obstructive pulmonary disease, oral cancer and learning disability) and two additional novel risk factors in women (pre-eclampsia and postnatal depression). On external validation, QR4 had a higher C statistic than QRISK3 in both women (0.835 (95% confidence interval (CI), 0.833-0.837) and 0.831 (95% CI, 0.829-0.832) for QR4 and QRISK3, respectively) and men (0.814 (95% CI, 0.812-0.816) and 0.812 (95% CI, 0.810-0.814) for QR4 and QRISK3, respectively). QR4 was also more accurate than the ASCVD and SCORE2 risk scores in both men and women. The QR4 risk score identifies new risk groups and provides superior CVD risk prediction in the United Kingdom compared with other international scoring systems for CVD risk. 10.1038/s41591-024-02905-y
Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study. Annals of internal medicine BACKGROUND:Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are often missing, complementary approaches for opportunistic risk assessment are desirable. OBJECTIVE:To develop and test a deep-learning model (CXR CVD-Risk) that estimates 10-year risk for MACE from a routine chest radiograph (CXR) and compare its performance with that of the traditional ASCVD risk score for implications for statin eligibility. DESIGN:Risk prediction study. SETTING:Outpatients potentially eligible for primary cardiovascular prevention. PARTICIPANTS:The CXR CVD-Risk model was developed using data from a cancer screening trial. It was externally validated in 8869 outpatients with unknown ASCVD risk because of missing inputs to calculate the ASCVD risk score and in 2132 outpatients with known risk whose ASCVD risk score could be calculated. MEASUREMENTS:10-year MACE predicted by CXR CVD-Risk versus the ASCVD risk score. RESULTS:Among 8869 outpatients with unknown ASCVD risk, those with a risk of 7.5% or higher as predicted by CXR CVD-Risk had higher 10-year risk for MACE after adjustment for risk factors (adjusted hazard ratio [HR], 1.73 [95% CI, 1.47 to 2.03]). In the additional 2132 outpatients with known ASCVD risk, CXR CVD-Risk predicted MACE beyond the traditional ASCVD risk score (adjusted HR, 1.88 [CI, 1.24 to 2.85]). LIMITATION:Retrospective study design using electronic medical records. CONCLUSION:On the basis of a single CXR, CXR CVD-Risk predicts 10-year MACE beyond the clinical standard and may help identify individuals at high risk whose ASCVD risk score cannot be calculated because of missing data. PRIMARY FUNDING SOURCE:None. 10.7326/M23-1898
Cardiovascular risk prediction in healthy older people. GeroScience Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations. 10.1007/s11357-021-00486-z
Carotid Plaque Score for Stroke and Cardiovascular Risk Prediction in a Middle-Aged Cohort From the General Population. Journal of the American Heart Association Background We aimed to explore the predictive value of the carotid plaque score, compared with the Systematic Coronary Risk Evaluation 2 (SCORE2) risk prediction algorithm, on incident ischemic stroke and major adverse cardiovascular events and establish a prognostic cutoff of the carotid plaque score. Methods and Results In the prospective ACE 1950 (Akershus Cardiac Examination 1950 study), carotid plaque score was calculated with ultrasonography at inclusion in 2012 to 2015. The largest plaque diameter in each extracranial segment of the carotid artery on both sides was scored from 0 to 3 points. The sum of points in all segments provided the carotid plaque score. The cohort was followed up by linkage to national registries for incident ischemic stroke and major adverse cardiovascular events (nonfatal ischemic stroke, nonfatal myocardial infarction, and cardiovascular death) throughout 2020. Carotid plaque score was available in 3650 (98.5%) participants, with mean±SD age of 63.9±0.64 years at inclusion. Only 462 (12.7%) participants were free of plaque, and and 970 (26.6%) had a carotid plaque score of >3. Carotid plaque score predicted ischemic stroke (hazard ratio [HR], 1.25 [95% CI, 1.15-1.36]) and major adverse cardiovascular events (HR, 1.21 [95% CI, 1.14-1.27]) after adjustment for SCORE2 and provided strong incremental prognostic information to SCORE2. The best cutoff value of carotid plaque score for ischemic stroke was >3, with positive predictive value of 2.5% and negative predictive value of 99.3%. Conclusions The carotid plaque score is a strong predictor of ischemic stroke and major adverse cardiovascular events, and it provides incremental prognostic information to SCORE2 for risk prediction. A cutoff score of >3 seems to be suitable to discriminate high-risk subjects. Registration Information clinicaltrials.gov. Identifier: NCT01555411. 10.1161/JAHA.123.030739
Managing Atherosclerotic Cardiovascular Risk in Young Adults: JACC State-of-the-Art Review. Journal of the American College of Cardiology There is a need to identify high-risk features that predict early-onset atherosclerotic cardiovascular disease (ASCVD). The authors provide insights to help clinicians identify and address high-risk conditions in the 20- to 39-year age range (young adults). These include tobacco use, elevated blood pressure/hypertension, family history of premature ASCVD, primary severe hypercholesterolemia such as familial hypercholesterolemia, diabetes with diabetes-specific risk-enhancing factors, or the presence of multiple other risk-enhancing factors, including in females, a history of pre-eclampsia or menopause under age 40. The authors update current thinking on lipid risk factors such as triglycerides, non-high-density lipoprotein cholesterol, apolipoprotein B, or lipoprotein (a) that are useful in understanding an individual's long-term ASCVD risk. The authors review emerging strategies, such as coronary artery calcium and polygenic risk scores in this age group, that have potential clinical utility, but whose best use remains uncertain. Finally, the authors discuss both the obstacles and opportunities for addressing prevention in early adulthood. 10.1016/j.jacc.2021.12.016
Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches. Current atherosclerosis reports PURPOSE OF REVIEW:In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health. RECENT FINDINGS:Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice. 10.1007/s11883-023-01174-3
Cardiovascular Risk Prediction Scores in CKD: What Are We Missing? Journal of the American Society of Nephrology : JASN 10.1681/ASN.2022010039
SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe. European heart journal AIMS:To develop and validate a recalibrated prediction model (SCORE2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe. METHODS AND RESULTS:SCORE2-Diabetes was developed by extending SCORE2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors (i.e. age, smoking, systolic blood pressure, total, and HDL-cholesterol), as well as diabetes-related variables (i.e. age at diabetes diagnosis, glycated haemoglobin [HbA1c] and creatinine-based estimated glomerular filtration rate [eGFR]). Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE2-Diabetes risk predictions varied several-fold, depending on individuals' levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m2, and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m2, and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8% and 13%, respectively. CONCLUSION:SCORE2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe. 10.1093/eurheartj/ehad260