Pooled Cohort Risk Equations Research Articles

Genome-Wide Polygenic Score, Clinical Risk Factors, and Long-Term Trajectories of Coronary Artery Disease.

To determine the relationship of a genome-wide polygenic score for coronary artery disease (GPSCAD) with lifetime trajectories of CAD risk, directly compare its predictive capacity to traditional risk factors, and assess its interplay with the Pooled Cohort Equations (PCE) clinical risk estimator. Approach and Results: We studied GPSCAD in 28 556 middle-aged participants of the Malmö Diet and Cancer Study, of whom 4122 (14.4%) developed CAD over a median follow-up of 21.3 years. A pronounced gradient in lifetime risk of CAD was observed-16% for those in the lowest GPSCAD decile to 48% in the highest. We evaluated the discriminative capacity of the GPSCAD-as assessed by change in the C-statistic from a baseline model including age and sex-among 5685 individuals with PCE risk estimates available. The increment for the GPSCAD (+0.045, P<0.001) was higher than for any of 11 traditional risk factors (range +0.007 to +0.032). Minimal correlation was observed between GPSCAD and 10-year risk defined by the PCE (r=0.03), and addition of GPSCAD improved the C-statistic of the PCE model by 0.026. A significant gradient in lifetime risk was observed for the GPSCAD, even among individuals within a given PCE clinical risk stratum. We replicated key findings-noting strikingly consistent results-in 325 003 participants of the UK Biobank. GPSCAD-a risk estimator available from birth-stratifies individuals into varying trajectories of clinical risk for CAD. Implementation of GPSCAD may enable identification of high-risk individuals early in life, decades in advance of manifest risk factors or disease.

Association of Habitual Physical Activity With Cardiovascular Disease Risk

A sedentary lifestyle is associated with increased risk for cardiovascular disease (CVD). Smartwatches enable accurate daily activity monitoring for physical activity measurement and intervention. Few studies, however, have examined physical activity measures from smartwatches in relation to traditional risk factors associated with future risk for CVD. To investigate the association of habitual physical activity measured by smartwatch with predicted CVD risk in adults. We enrolled consenting FHS (Framingham Heart Study) participants in an ongoing eFHS (electronic Framingham Heart Study) at the time of their FHS research center examination. We provided participants with a smartwatch (Apple Watch Series 0) and instructed them to wear it daily, which measured their habitual physical activity as the average daily step count. We estimated the 10-year predicted risk of CVD using the American College of Cardiology/American Heart Association 2013 pooled cohort risk equation. We estimated the association between physical activity and predicted risk of CVD using linear mixed effects models adjusting for age, sex, wear time, and familial structure. Our study included 903 eFHS participants (mean age 53±9 years, 61% women, 9% non-White) who wore the smartwatch ≥5 hours per day for ≥30 days. Median daily step count was similar among men (7202 with interquartile range 3619) and women (7260 with interquartile range 3068; P=0.52). Average 10-year predicted CVD risk was 4.5% (interquartile range, 6.1%) for men and 1.2% (interquartile range, 2.2%) for women (P=1.3×10-26). Every 1000 steps higher habitual physical activity was associated with 0.18% lower predicted CVD risk (P=3.2×10-4). The association was attenuated but remained significant after further adjustment for body mass index (P=0.01). In this community-based sample of adults, higher daily physical activity measured by a study smartwatch was associated with lower predicted risk of CVD. Future research should examine the longitudinal association of prospectively measured daily activity and incident CVD.

Using Predicted Atherosclerotic Cardiovascular Disease Risk for Discrimination of Awake or Nocturnal Hypertension.

Several atherosclerotic cardiovascular disease (ASCVD) risk factors are associated with awake and nocturnal hypertension. We assessed the association between a composite ASCVD risk score and awake or nocturnal hypertension using data from participants aged 40-79 years who completed ambulatory blood pressure monitoring at the Year 30 Coronary Artery Risk Development in Young Adults study exam in 2015-2016 (n = 716) and the baseline Jackson Heart Study exam in 2000-2004 (n = 770). Ten-year predicted ASCVD risk was calculated using the Pooled Cohort risk equations. Awake hypertension was defined as mean awake systolic blood pressure (SBP) ≥135 mm Hg or diastolic blood pressure (DBP) ≥85 mm Hg and nocturnal hypertension was defined as mean asleep SBP ≥120 mm Hg or DBP ≥70 mm Hg. Among participants with a 10-year predicted ASCVD risk <5%, 5% to <7.5%, 7.5% to <10%, and ≥10%, the prevalence of awake or nocturnal hypertension as a composite outcome was 29.5%, 47.8%, 62.2%, and 69.7%, respectively. After multivariable adjustment, higher ASCVD risk was associated with higher prevalence ratios for awake or nocturnal hypertension among participants with clinic-measured SBP/DBP <130/85 mm Hg but not ≥130/85 mm Hg. The C-statistic for discriminating between participants with vs. without awake or nocturnal hypertension was 0.012 (95% confidence interval 0.003, 0.016) higher when comparing a model with ASCVD risk and clinic-measured blood pressure (BP) together vs. clinic-measured BP without ASCVD risk. Using 10-year predicted ASCVD risk in conjunction with clinic BP improves discrimination between individuals with and without awake or nocturnal hypertension.

Abstract 264: Utilizing Risk Scoring for Prescribing Statins to Hospitalized Patients to Primarily Prevent Coronary Artery Disease

Background: Cardiovascular disease is the leading cause of mortality in the United States. Approximately 25% of total deaths in the United States are attributed to cardiovascular diseases. Modification of risk factors has been shown to reduce mortality and morbidity in people with coronary artery disease. Medications such as statins are well known for reducing risks and recent data has shown that statins are beneficial in the primary prevention of coronary artery disease. The purpose of this study is to assess whether statins are being prescribed on discharge to patients who are identified as intermediate to high risk using the ACC/AHA Pooled Cohort Equations. Methodology: We reviewed and analyzed the charts of hospitalized patient’s ages 40 to 79 years who were discharged under the service of Internal Medicine at Richmond University Medical Center from September 2018 to August 2019. Exclusion criteria included: patients that expired before discharge or were admitted to the intensive or coronary care units, pregnancy, previous diagnosis of coronary/peripheral artery disease or stroke, already on statins or lipid-lowering medications, allergic to statins, discharged on statins for coronary/peripheral artery disease or stroke, and patients with liver disease or elevated liver enzymes. We used the ACC/AHA Pooled Cohort Equations risk to calculate the 10-year coronary artery disease risk for each patient. Results: The 10-year risk is grouped as low risk (&lt;5%), borderline risk (5% to 7.4%), intermediate risk (7.5% to 19.9%) and high risk (≥20%). Among 898 patients, 10% had intermediate and high risk that were not discharged with statins. Among the 10%, about 6.6% were intermediate risk and 3.4% were high risk. Conclusions: A significant number of intermediate and high-risk patients were discharged without statins, although a CT coronary calcium may be helpful in further classifying the risk in some of them. We believe that a lipid profile should be checked in all hospitalized patients 40 years and older in order to calculate their atherosclerosis cardiovascular disease risk score and to possibly initiate statins after discussing the benefits and side effects, particularly in the intermediate risk group. The continuation of statins would be followed up by their primary care physicians. We plan to liaise with the information technology department in our facility to provide a link to the risk calculator in the electronic medical record so that the risk can be calculated and statins initiated as necessary. We will conduct a follow up review to assess for effectiveness.

Abstract P335: Using Cardiovascular Disease Risk Versus Blood Pressure To Guide The Initiation Of Antihypertensive Medication Among Us Adults

Introduction: The primary goal of initiating antihypertensive medication is to prevent cardiovascular disease (CVD). It has been hypothesized that using CVD risk to guide the decision to initiate antihypertensive medication may prevent more CVD events than treatment guided by blood pressure (BP) alone. Methods: We estimated the number of CVD and all-cause deaths that could be prevented among US adults through the initiation of antihypertensive medication based on high CVD risk versus high BP. CVD and all-cause mortality rates were calculated using data from 4,390 participants 40 to 79 years of age not taking antihypertensive medication from the 1999 to 2004 National Health and Nutrition Examination Survey (NHANES) mortality follow-up study. High BP was defined by systolic BP ≥ 140 mm Hg or diastolic BP ≥ 90 mm Hg. High CVD risk was defined by 10-year predicted CVD risk ≥10% using the Pooled Cohort risk equations or a history of CVD. Relative risks and 95% confidence limits for CVD and all-cause mortality with antihypertensive medication of 0.75 (0.57-0.98) and 0.76 (0.63-0.91), respectively, were obtained from the Blood Pressure Lowering Treatment Trialists Collaboration. Results: Among US adults not taking antihypertensive medication, 19.4% (23.5 million) had high BP and 25.5% (30.9 million) had high CVD risk. CVD mortality rates were 5.3 and 3.9 per 1,000 person-years among US adults with high CVD risk versus high BP, respectively (Table). Using high CVD risk to guide antihypertensive medication initiation is projected to prevent 403,093 deaths from CVD over 10 years compared with 224,312 deaths from CVD projected to be prevented using a BP-guided treatment approach. More all-cause deaths are projected to be prevented by using high CVD risk (2.2 million deaths) rather than high BP (1.1 million deaths) to guide the decision to initiate antihypertensive medication. Conclusions: Using predicted CVD risk instead of BP alone to guide antihypertensive medication initiation is projected to prevent more CVD and all-cause deaths.

Abstract P386: Pregnancy-related Risk Factors May Signal Additional Risk For Future Cardiovascular Disease

Introduction: Emerging data suggest that pregnancy may be a window on the future of women’s cardiovascular health. Cardiovascular disease (CVD) guidelines recommend using the Pooled Cohort Equation (PCE) to assess 10-year CVD risk based on traditional risk factors. Less is known about the role of pregnancy-related factors, such as a history of small for gestational age (SGA) infants or breastfeeding, and the risk of CVD events in addition to the PCE. Hypothesis: We hypothesize that pregnancy-related complications and breastfeeding can affect the risk of future CVD risk in addition to traditional risk factors currently accounted for by the PCE. Methods: Using NHANES 1999-2006, a weighted sample of 3,913 women (representing 27,102,057 women in the US population), ages 40-79, with a history of pregnancy, but no prior CVD, was identified. Variables for SGA infants and breastfeeding were abstracted along with traditional risk factors. Less than 5% of women were missing data on these variables. CVD outcomes were defined as a composite of (1) CVD death and (2) surrogates for CVD death, in which diabetes or hypertension were a secondary cause of death. CVD outcomes and survival time were obtained from the NHANES Linked Mortality File. The PCE was used to estimate 10-year CVD risk. Bivariate and survival analyses using Cox proportional hazards models adjusting for PCE risk score were performed. For survival analysis, the cause-specific hazard function was estimated considering the time of death as censoring for women dying from causes other than CVD outcomes, as well as the time of follow-up for women that did not present the death event. Results: Among the sample, 504 (11.8%) women had a SGA infant and 2133 (54.5%) reported a history of breastfeeding. 198 (5.1%) women had the composite CVD death outcome. CVD outcomes were lower in women with a history of breastfeeding (97 of 2133) compared to those who did not breastfeed (96 of 1629). (2.6% vs. 4.2%, p=0.002) The opposite relationship was observed for women with a history of SGA infant (4.2% (29 of 504) vs. 3.2% (161 of 3232), p=0.2). PCE scores were associated with breastfeeding and SGA, potentially confounding those effects. Survival analyses, adjusting for continuous PCE risk scores, showed an inverse association of breastfeeding and CVD outcomes (HR 0.7, 95% CI 0.5 to 1.0) and a positive association of history of SGA infant and CVD outcomes. (HR 1.4, 95% CI 0.8 to 2.2) Conclusion: Specific pregnancy-related complications and breastfeeding may provide additional, relevant information about the risk of CVD risk events beyond traditional risk factors. While further research is needed to incorporate pregnancy outcomes into risk prediction models, it may be helpful for clinicians to counsel women about the potential impact of pregnancy-related factors and breastfeeding on future cardiovascular health.

Abstract P416: An Externally Validated and Highly Sensitive Artificial Intelligence-driven Integrated Genetic-epigenetic DNA Test for Incident Coronary Heart Disease Prediction

Despite being largely preventable, every year, coronary heart disease (CHD) affects millions of Americans. Currently, primary prevention of CHD starts with an understanding of several risk factors (e.g. age, gender, diabetes) that are aggregated to derive an incident CHD risk score using risk calculators such as the Framingham Risk Score (FRS) and ASCVD Pooled Cohort Equation (PCE). Unfortunately, this approach involves multiple tests and its lack in sensitivity could result in ineffective treatment recommendations to patients for the prevention of CHD. As an alternative, we have developed and independently validated a single, simpler, AI-driven, digital PCR (dPCR)-based integrated genetic-epigenetic DNA test that can more sensitively estimate the 3-year incident CHD risk for both men and women. Our technology accounts for the inherited and acquired (lifestyle/environment) risks for CHD through three genetic (SNP) and three epigenetic (DNA methylation) biomarkers, respectively, and can be performed on DNA from blood or saliva. The incident CHD risk prediction model was developed using genome-wide DNA methylation and genotype data from the Framingham Heart Study (FHS) Offspring cohort (n=1172 in training set, n=512 in test set) and was validated in an Intermountain Healthcare (IM) cohort (n=80 in validation set, n=79 in test set). The final prediction model is an ensemble of SVM, Random Forest and Logistic Regression models, and its performance in the FHS and IM test sets is summarized in Table 1. The FRS and PCE risk calculators were implemented on all FHS and IM cohort data. A clinically implementable version of this tool as part of its translation into a Laboratory Developed Test incorporates standard Taqman assays for genotyping and custom dPCR assays for DNA methylation quantification. The strong correlation between our dPCR assay values and the Illumina array values are shown in Figure 1, indicating successful dPCR assay translation. We are also extending this tool to include more ethnically diverse cohorts.

Underperformance of clinical risk scores in identifying vascular ultrasound-based high cardiovascular risk in systemic lupus erythematosus.

The aim of this study was to assess the performance of eight clinical risk prediction scores to identify individuals with systemic lupus erythematosus (SLE) at high cardiovascular disease (CVD) risk, as defined by the presence of atherosclerotic plaques. CVD risk was estimated in 210 eligible SLE patients without prior CVD or diabetes mellitus (female: 93.3%, mean age: 44.8 ± 12 years) using five generic (Systematic Coronary Risk Evaluation (SCORE), Framingham Risk Score (FRS), Pooled Cohort Risk Equations (ASCVD), Globorisk, Prospective Cardiovascular Münster Study risk calculator (PROCAM)) and three 'SLE-adapted' (modified-SCORE, modified-FRS, QRESEARCH risk estimator, version 3 (QRISK3)) CVD risk scores, as well as ultrasound examination of the carotid and femoral arteries. Calibration, discrimination and classification measures to identify high CVD risk based on the presence of atherosclerotic plaques were assessed for all risk models. CVD risk reclassification was applied for all scores by incorporating ultrasound results. Moderate calibration (p-value range from 0.38 to 0.63) and discrimination (area under the curve 0.73-0.84), and low-to-moderate sensitivity (8.3-71.4%) and classification ability (Matthews correlation coefficient (MCC) 0.25-0.47) were observed for all risk models to identify patients with plaques at any arterial site as high-risk. MCC was improved for modified-FRS versus FRS (0.43 vs 0.36), but not for modified-SCORE versus SCORE (0.25 vs 0.25). Based on plaque presence, CVD risk was upgraded to high-risk in 10%, 16.1%, 20.5%, 21.5%, 24%, 28.2% and 28.6% of cases classified as non-high-risk by QRISK3, modified-FRS, Globorisk, FRS/PROCAM, ASCVD, modified-SCORE and SCORE, respectively. Most of the five generic and three 'SLE-adapted' clinical risk scores underestimated high CVD risk defined by atherosclerotic plaque presence in patients with SLE.

Underperformance of clinical risk scores in identifying vascular ultrasound-based high cardiovascular risk in systemic lupus erythematosus.

The aim of this study was to assess the performance of eight clinical risk prediction scores to identify individuals with systemic lupus erythematosus (SLE) at high cardiovascular disease (CVD) risk, as defined by the presence of atherosclerotic plaques. CVD risk was estimated in 210 eligible SLE patients without prior CVD or diabetes mellitus (female: 93.3%, mean age: 44.8 ± 12 years) using five generic (Systematic Coronary Risk Evaluation (SCORE), Framingham Risk Score (FRS), Pooled Cohort Risk Equations (ASCVD), Globorisk, Prospective Cardiovascular Münster Study risk calculator (PROCAM)) and three 'SLE-adapted' (modified-SCORE, modified-FRS, QRESEARCH risk estimator, version 3 (QRISK3)) CVD risk scores, as well as ultrasound examination of the carotid and femoral arteries. Calibration, discrimination and classification measures to identify high CVD risk based on the presence of atherosclerotic plaques were assessed for all risk models. CVD risk reclassification was applied for all scores by incorporating ultrasound results. Moderate calibration (p-value range from 0.38 to 0.63) and discrimination (area under the curve 0.73-0.84), and low-to-moderate sensitivity (8.3-71.4%) and classification ability (Matthews correlation coefficient (MCC) 0.25-0.47) were observed for all risk models to identify patients with plaques at any arterial site as high-risk. MCC was improved for modified-FRS versus FRS (0.43 vs 0.36), but not for modified-SCORE versus SCORE (0.25 vs 0.25). Based on plaque presence, CVD risk was upgraded to high-risk in 10%, 16.1%, 20.5%, 21.5%, 24%, 28.2% and 28.6% of cases classified as non-high-risk by QRISK3, modified-FRS, Globorisk, FRS/PROCAM, ASCVD, modified-SCORE and SCORE, respectively. Most of the five generic and three 'SLE-adapted' clinical risk scores underestimated high CVD risk defined by atherosclerotic plaque presence in patients with SLE.

Association between Blood Heavy Metal Levels and Predicted 10-Year Risk for A First Atherosclerosis Cardiovascular Disease in the General Korean Population.

Background: Atherosclerotic cardiovascular disease (ASCVD) is a preventable type of disease, thus, specifying factors that increase the occurrence of this type of disease is needed. Heavy metals such as cadmium (Cd), lead (Pb), and mercury (Hg) have been suggested as possible factors influencing the development of cardiovascular disease. We aimed to link blood heavy metal levels (Cd, Pb, Hg) with 10-year ASCVD risk scores. Methods: A population of 993 men and 1431 women who participated in the Korea National Health and Nutrition Examination Survey (KNHANES) were included. The 2013 American College of Cardiology/American Heart Association (ACC/AHA) pooled cohort equations risk prediction model and Korean Risk Prediction Model (KRPM) were used as means for risk prediction. Following multivariate adjustment; blood Cd; Pb; and Hg levels were divided into quartiles for analysis using linear trends estimation and multiple regression models. Results: There was an overall positive trend between blood Cd, Pb, and Hg levels and 10-year ASCVD risk scores; KRPM risk score increasing by quartile for blood Cd (men p < 0.0001, women p = 0.0024), Pb (men p = 0.0097, women p = 0.0330), Hg (men p = 0.0096, women p = 0.0030) rates and pooled cohort equations risk score increasing by quartile for Cd (men p < 0.0001, women p = 0.0034) and Hg (men p = 0.0099, women p = 0.0010) with linear trends. Urban population showed a stronger relationship between blood Cd, Pb, and Hg levels and 10-year ASCVD risk score especially among men with multiple regression analysis. Conclusion: Blood Cd, Pb, and Hg levels are associated with ASCVD risk. Thus, they should be considered while developing preventive measures for ASCVD.

A cross-sectional validation study comparing the accuracy of different risk scores in assessing the risk of acute coronary syndrome among patients in a tertiary care hospital in Kerala

BackgroundThere are many cardiovascular disease (CVD) risk score calculators in practice, which are not based on Indian population data. ObjectivesTo identify the best CVD risk score calculator applicable in the Indian population. Materials and methodsA total of 1000 patients presenting with acute coronary syndrome (ACS) were included in the study and their CVD risk score, had they presented before the event, was calculated. The Framingham risk score (FRS–body mass index [BMI], FRS–fasting lipid profile [FLP]), the American College of Cardiology/American Heart Association pooled cohort equation risk calculator (ACC/AHA PCE), Joint British Society risk calculator 3 (JBS3) and the World Health Organization (WHO) risk prediction charts (WHO TC and WHO without TC [WHO NO TC]) were used. ResultsIt was seen that among the 1000 people included in the study, the FRS-BMI (59.2%), FRS-FLP (61.5%), ACC/AHA (70.1%) and the JBS3 (62.5%) identified a majority as having a risk of ≥20%, whereas both the WHO TC (65.3%) and the WHO NO TC (64.5%) identified a majority of the ACS patients as having a risk of <20%. The sensitivity was highest for the ACC/AHA (87.8%), FRS-FLP (85.1%) and then JBS3 (80.1%), whereas the specificity was highest for the WHO TC (83.6%) and the WHO NO TC (82.1%). When looking at the accuracy, the FRS-FLP was the most accurate with 80.1%, whereas the ACC/AHA and the JBS3 followed at 74.7% and 73.1%, respectively. ConclusionThe ACC/AHA seems to be an acceptable risk prediction system to be used in the Indian population and is also relatively easy and cheap to use.

Pooled cohort risk equation and subclinical cerebrovascular diseases.

In 2013, the American College of Cardiology/American Heart Association (ACC/AHA) introduced a novel pooled cohort risk (PCR) model for atherosclerotic cardiovascular disease. In this study, we evaluated the relationship between the PCR score and cerebral large- and small-vessel diseases (cLVD and cSVD) in a healthy population, METHODS: We assessed consecutive health check-up volunteers from 2006 to 2013. We calculated the estimated 10-year atherosclerotic cardiovascular disease risk as the PCR score based on the 2013 ACC/AHA guidelines. We evaluated both cSVD/cLVD, including the prevalence of cLVD, lacunes and cerebral microbleed (CMB), and the volume of white matter hyperintensity (WMH). In addition to PCR score, the risk factors that were associated with outcome variables at P<0.10 in univariate analysis were included for further multivariable linear or regression analyses. A total of 2720 participants were evaluated (mean age, 57years, male sex, 54%). In multivariable analysis, PCR score was associated with WMH volume [β=0.361; 95% confidence interval (CI), 0.320-0.402, P<0.001], cLVD [adjusted odds ratio (aOR), 1.66; 95% CI, 1.29-2.16, P<0.001], lacunes (aOR, 1.80; 95% CI, 1.52-2.14, P<0.001) and CMBs (aOR, 1.75; 95% CI, 1.40-2.19, P<0.001). Furthermore, PCR score also showed dose-response tendencies according to the burden of cLVD, WMH, lacunes and CMB. A higher PCR score based on the ACC/AHA guidelines is closely associated with a higher prevalence and burden of cLVD and cSVD.

High-Sensitivity C-Reactive Protein Discordance With Atherogenic Lipid Measures and Incidence of Atherosclerotic Cardiovascular Disease in Primary Prevention: The ARIC Study.

BackgroundInflammation is an independent causal risk factor for atherosclerotic cardiovascular diseases (ASCVDs). However, whether hsCRP (high‐sensitivity C‐reactive protein) is prognostic across various levels of atherogenic lipid measures such as low‐density lipoprotein cholesterol, non–high‐density lipoprotein cholesterol, apolipoprotein B and total cholesterol/high‐density lipoprotein cholesterol in primary prevention is unknown.Methods and ResultsWe studied 9748 ARIC (Atherosclerosis Risk in Communities) study participants who were free of ASCVD at baseline (visit 4, 1996–1998) and had measurements of lipids, apolipoprotein B, and hsCRP. We used multivariable adjusted Cox models to estimate the risk of incident ASCVD events associated with hsCRP levels (less than/greater than or equal to median) in individuals where triple lipid measures combined (low‐density lipoprotein cholesterol + non–high‐density lipoprotein cholesterol + apolipoprotein B) or quadruple measures combined [triple + total cholesterol/high‐density lipoprotein cholesterol] were less than versus greater than or equal to median cut points. Mean age of participants was 62.6±5.6 years; 59% women, 22% black. There were 1574 ASCVD events over median (interquartile range) follow‐up of 18.4 (12.8–19.5) years, and discordance between hsCRP and lipid measures was prevalent in 50% of the population. hsCRP greater than or equal to median (2.4 mg/L), compared with less than median, was associated with an increased risk of ASCVD in individuals with less than median levels of the triple (adjusted hazard ratio, 1.33; 95% CI, 1.09–1.60) and quadruple (adjusted hazard ratio,1.47; 95% CI, 1.18–1.85) lipid measures. Such increased risk was consistent among individuals with low (<7.5%) or high (≥7.5%) estimated risk by the pooled cohort equation. There were no interactions by sex, diabetes mellitus, or statin use.ConclusionsOur findings suggest that inflammation is independently associated with ASCVD regardless of atherogenic lipid levels and pooled cohort equation risk score in individuals without known ASCVD.

Agreement between cardiovascular disease risk assessment tools: An application to the United Arab Emirates population.

IntroductionEvidence regarding the performance of cardiovascular disease (CVD) risk assessment tools is limited in the United Arab Emirates (UAE). Therefore, we assessed the agreement between various externally validated CVD risk assessment tools in the UAE.MethodsA secondary analysis of the Abu Dhabi Screening Program for Cardiovascular Risk Markers (AD-SALAMA) data, a large population-based cross-sectional survey conducted in Abu Dhabi, UAE during the period 2009 until 2015, was performed in July 2019. The analysis included 2,621 participants without type 2 Diabetes and without history of cardiovascular diseases. The CVD risk assessment tools included in the analysis were the World Health Organization for Middle East and North Africa Region (WHO-MENA), the systematic coronary risk evaluation for high risk countries (SCORE-H), the pooled cohort risk equations for white (PCRE-W) and African Americans (PCRE-AA), the national cholesterol education program Framingham risk score (FRAM-ATP), and the laboratory Framingham risk score (FRAM-LAB).ResultsThe overall concordance coefficient was 0.50. The agreement between SCORE-H and PCRE-W, PCRE-AA, FRAM-LAB, FRAM-ATP and WHO-MENA, were 0.47, 0.39, 0.0.25, 0.42 and 0.18, respectively. PCRE-AA classified the highest proportion of participants into high-risk category of CVD (16.4%), followed by PCRE-W (13.6%), FRAM-LAB (6.9%), SCORE-H (4.5%), FRAM-ATP (2.7%), and WHO-MENA (0.4%).ConclusionsWe found a poor agreement between various externally validated CVD risk assessment tools when applied to a large data collected in the UAE. This poses a challenge to choose any of these tools for clinical decision-making regarding the primary prevention of CVD in the country.

Estimation of Cardiovascular Risk from Self-Reported Knowledge of Risk Factors: Insights from the Minnesota Heart Survey.

BackgroundCost-effective primary prevention of cardiovascular disease (CVD) relies on accuracy of risk assessment. Current risk scores require clinical and laboratory measures, are expensive and are often difficult to apply in the population setting.ObjectiveThis study sought to estimate CVD risk from individuals’ knowledge of their own CVD risk factors and compare it to the risk calculated from measured risk factors.MethodsUsing the ACC/AHA Pooled Cohort Risk Equations (PCE), we calculated 10-year CVD risk for 9856 primary prevention individuals aged 40–79 in the Minnesota Heart Survey (MHS). Using log-linear regression models, we estimated PCE risk from the individual’s self-reported knowledge of four dichotomous risk factors: hypertension, hypercholesterolemia, diabetes, and smoking. Age was included in all models, and models were developed separately in women and men. Model performance was assessed internally using leave-one-out cross-validation.ResultsThe median measured PCE CVD risk in women was 2.1% (IQR: 0.8–5.6%), and in men was 6.3% (3.1–13.0%). Using the newly developed equations, the median estimated risk was 2.2% (0.9–5.8%) in women, and 6.9% (3.2–13.1%) in men. Using a threshold of 7.5% to categorize low and high risk, the novel risk calculation gave an accuracy of 95% for women and 87% for men compared to the measured PCE risk. The negative predictive value was 97% for women and 91% in men.ConclusionSelf-reported knowledge of risk may be useful in the identification of individuals at low risk for CV events, however, should always be followed up with measurement of risk factors if symptoms or history suggest CVD.

Encare clínico de las dislipemias

In daily clinical practice the management of patients with dyslipidemias implies knowledge of relevant clinical scientific evidence, common sense, as respect of patient preferences.There is strong evidence that treatment of dyslipidemias mainly with statins reduces morbidity and mortality in a wide group of patients with few side effects. A critical step in management of this individuals is to make the decision of whether statin treatment is indicated or not. There are patients that have a clear indication of statin use without any further cardiovascular risk calculation. Such is the case in secondary prevention, patients with extremely high low density lipoprotein cholesterol levels (190 mg/dl) and diabetics between 40 and 75 years-old. In all other patients, statin indication should start with a formal cardiovascular risk evaluation. American guidelines suggest using the Pooled Cohort Risk Equations and European guidelines prefer Heartscore. Both scoring systems stratify risk in four categories according to risk intensity. The absolute cardiovascular risk reduction obtained with treatment increases in parallel with the basal cardiovascular risk. This explains the recommendation that both treatment intensity and magnitude of low density lipoprotein cholesterol lowering should increase as the risk of the patient increases. Recommendations provided by American and European guidelines do not always coincide. Thus, besides basal cardiovascular risk estimation, potential adverse drug effects and patient preferences should always be considered in the context of a clinician-patient frank discussion.Ezetimibe first andPCSK9inhibitors eventually (currently limited by costs and availability) appear as the great allies of statins, when adequate doses are not tolerated or the target is not reached.We will tackle the subject through five cases that illustrate the main clinical situations in which physicians have to adopt specific therapeutic decisions.

The Accuracy of Cardiovascular Pooled Cohort Risk Estimates in U.S. Older Adults.

The ACC/AHA guidelines for primary prevention rely on the Pooled Cohort Risk Equations (PCE) risk estimates of atherosclerotic cardiovascular disease (ASCVD) to guide treatment decisions. In light of the PCE being derived in younger populations, their accuracy in older adults is uncertain. To evaluate the predictive accuracy and calibration of the PCE in older individuals. We estimated CVD predicted and observed risk among individuals from four large prospective cohort studies: Cardiovascular Health Study, Multiethnic Study of Atherosclerosis, Framingham Original, and Framingham Offspring. 12,527 overall individuals without ASCVD, including 9864 individuals aged 40-74years and 2663 aged ≥75years. We examined the operating characteristics of the PCE to estimate 5-year risk of stroke, MI, and CHD death overall and by age and sex strata. The associations between individual components of the PCE and cardiovascular events by age group (≥75 vs 40-74years) were also evaluated. The PCE had low discrimination for 5-year ASCVD risk in older (≥75years) (c-statistic = 0.62, 95% CI 0.60-0.65) vs. younger (40-74years) adults (c-statistic = 0.75, 95% CI 0.73-0.76). Calibration of the PCE was suboptimal in both older and younger adults, overestimating risk in the highest risk groups. Performance of the PCE in older adults was similarly poor when stratified by sex and age ≥ 80years. Since the PCE were derived from similar cohorts, though using different age groups and exams, this analysis likely overestimates the performance of the PCE. The performance of the PCE for ASCVD risk estimation in older adults is suboptimal; new models to effectively risk-stratify older adults are needed.

OBLIQUE RANDOM SURVIVAL FORESTS.

We introduce and evaluate the oblique random survival forest (ORSF). The ORSF is an ensemble method for right-censored survival data that uses linear combinations of input variables to recursively partition a set of training data. Regularized Cox proportional hazard models are used to identify linear combinations of input variables in each recursive partitioning step. Benchmark results using simulated and real data indicate that the ORSF's predicted risk function has high prognostic value in comparison to random survival forests, conditional inference forests, regression, and boosting. In an application to data from the Jackson Heart Study, we demonstrate variable and partial dependence using the ORSF and highlight characteristics of its 10-year predicted risk function for atherosclerotic cardiovascular disease events (ASCVD; stroke, coronary heart disease). We present visualizations comparing variable and partial effect estimation according to the ORSF, the conditional inference forest, and the Pooled Cohort Risk equations. The obliqueRSF R package, which provides functions to fit the ORSF and create variable and partial dependence plots, is available on the comprehensive R archive network (CRAN).

Detecting time-evolving phenotypic topics via tensor factorization on electronic health records: Cardiovascular disease case study.

Discovering subphenotypes of complex diseases can help characterize disease cohorts for investigative studies aimed at developing better diagnoses and treatments. Recent advances in unsupervised machine learning on electronic health record (EHR) data have enabled researchers to discover phenotypes without input from domain experts. However, most existing studies have ignored time and modeled diseases as discrete events. Uncovering the evolution of phenotypes - how they emerge, evolve and contribute to health outcomes - is essential to define more precise phenotypes and refine the understanding of disease progression. Our objective was to assess the benefits of an unsupervised approach that incorporates time to model diseases as dynamic processes in phenotype discovery. In this study, we applied a constrained non-negative tensor-factorization approach to characterize the complexity of cardiovascular disease (CVD) patient cohort based on longitudinal EHR data. Through tensor-factorization, we identified a set of phenotypic topics (i.e., subphenotypes) that these patients established over the 10 years prior to the diagnosis of CVD, and showed the progress pattern. For each identified subphenotype, we examined its association with the risk for adverse cardiovascular outcomes estimated by the American College of Cardiology/American Heart Association Pooled Cohort Risk Equations, a conventional CVD-risk assessment tool frequently used in clinical practice. Furthermore, we compared the subsequent myocardial infarction (MI) rates among the six most prevalent subphenotypes using survival analysis. From a cohort of 12,380 adult CVD individuals with 1068 unique PheCodes, we successfully identified 14 subphenotypes. Through the association analysis with estimated CVD risk for each subtype, we found some phenotypic topics such as Vitamin D deficiency and depression, Urinary infections cannot be explained by the conventional risk factors. Through a survival analysis, we found markedly different risks of subsequent MI following the diagnosis of CVD among the six most prevalent topics (p < 0.0001), indicating these topics may capture clinically meaningful subphenotypes of CVD. This study demonstrates the potential benefits of using tensor-decomposition to model diseases as dynamic processes from longitudinal EHR data. Our results suggest that this data-driven approach may potentially help researchers identify complex and chronic disease subphenotypes in precision medicine research.

Association Between Cardiovascular Risk and Perceived Fatigability in Mid-to-Late Life.

BackgroundCardiovascular disease (CVD) and fatigue commonly co‐occur in older adults, yet the subjective nature of fatigue and its situational dependence leave the true magnitude of this association undefined.Methods and ResultsSix‐hundred and twenty‐five participants with no history of CVD (aged 68.1+12.0 years), from the Baltimore Longitudinal Study of Aging who underwent ≥2 clinic visits between 2007 and 2015 were classified according to sex‐specific predicted 10‐year CVD risk scores using the Framingham CVD risk score (Framingham) and the Pooled Cohort Equation at baseline. Perceived fatigability was assessed using the Borg rating of perceived exertion scale after a 5‐minute treadmill walk (0.67 m/s, 0% grade). Linear models were used to assess the association between baseline CVD risk and perceived fatigability an average of 4.5 years later, adjusted for demographics, behaviors, and medical history. In final models, a 5% higher baseline Pooled Cohort Equation score was associated with greater perceived fatigability at follow‐up (β=0.13 rating of perceived exertion, P=0.008). Stratified analyses suggested this association was stronger among those aged ≤70 years and those with obesity. Of the individual CVD risk score components, older age was most strongly associated with perceived fatigability (β=0.48, P<0.001), followed by women (β=0.11, P=0.002), and treated hypertension (β=0.11, P=0.003). There was no association with the Framingham risk score.ConclusionsPerceived fatigability was higher among participants with greater CVD risk measured using the Pooled Cohort Equation risk score. The strong associations with hypertension and obesity suggest prevention and promotion of cardiovascular health may also lower perceived fatigability, particularly among those aged ≤70 years or living with obesity.