Dataset Information

Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample.

ABSTRACT: Background:Cardiovascular Disease (CVD) risk prediction models have been useful in estimating if individuals are at low, intermediate, or high risk, of experiencing a CVD event within some established time frame, usually 10 years. Central to this is the concern in Trinidad and Tobago of using pre-existing CVD risk prediction methods, based on populations in the developed world (e.g. ASSIGN, Framingham and QRISK®2), to establish risk for its multiracial/ethnic Caribbean population. The aim of this study was to determine which pre-existing CVD risk method is best suited for predicting CVD risk for individuals in this population. Method:A survey was completed by 778 participants, 526 persons with no prior CVD, and 252 who previously reported a CVD event. Lifestyle and biometric data was collected from non-CVD participants, while for CVD participants, medical records were used to collect data at the first instance of CVD. The performances of three CVD risk prediction models (ASSIGN, Framingham and QRISK®2) were evaluated using their calculated risk scores. Results:All three models (ASSIGN, Framingham and QRISK®2) identified less than 62% of cases (CVD participants) with a high proportion of false-positive predictions to true predictions as can be seen by positive predictabilities ranging from 78% (ASSIGN and Framingham) to 87% (QRISK®2). Further, for all three models, individuals whose scores fell into the misclassification range were 2X more likely to be individuals who had experienced a prior CVD event as opposed to healthy individuals. Conclusion:The ASSIGN, Framingham and QRISK®2 models should be utilised with caution on a Trinidad and Tobago population of intermediate and high risk for CVD since these models were found to have underestimated the risk for individuals with CVD up to 2.5 times more often than they overestimated the risk for healthy persons.

SUBMITTER: Hosein A

PROVIDER: S-EPMC7067186 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample.

Hosein Amalia A Stoute Valerie V Chadee Samantha S Singh Natasha Ramroop NR

PeerJ 20200309

<h4>Background</h4>Cardiovascular Disease (CVD) risk prediction models have been useful in estimating if individuals are at low, intermediate, or high risk, of experiencing a CVD event within some established time frame, usually 10 years. Central to this is the concern in Trinidad and Tobago of using pre-existing CVD risk prediction methods, based on populations in the developed world (e.g. ASSIGN, Framingham and QRISK<sup>®</sup>2), to establish risk for its multiracial/ethnic Caribbean populat ...[more]

PMID: 32195041

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Project description:BACKGROUND:Atherosclerosis and its clinical sequelae represent the leading cause of mortality among patients with nonalcoholic fatty liver disease (NAFLD). While epidemiologic data support the hepatoprotective benefits of coffee in NAFLD, whether coffee improves NAFLD-associated CVD risk is unknown. METHODS:We examined 3710 ethnically-diverse participants from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort, without history of known liver disease, and with available coffee data from a validated 120-item food frequency questionnaire. All participants underwent baseline non-contrast cardiac CT from which NAFLD was defined by liver:spleen ratio (L:S<1.0), and subclinical CVD was defined by coronary artery calcium (CAC)>0. Major CVD events were defined by the first occurrence of myocardial infarction, cardiac arrest, angina, stroke, or CVD death. We used log-binomial regression to calculate the adjusted prevalence ratio (PR) for CAC>0 by coffee intake and NAFLD status, and events were compared between groups using frequency of events within adjusted Cox proportional hazard regression models. RESULTS:Seventeen percent (N=637) of participants met criteria for NAFLD. NAFLD participants were more likely to have elevated BMI (mean 31.1±5.5kg/m2 vs. 28.0±5.2kg/m2, p<0.0001), and diabetes (22% vs. 11%, p<0.0001), but did not differ in daily coffee consumption (p=0.97). Among NAFLD participants, coffee consumption was not associated with prevalent, baseline CAC>0 (PR=1.02 [0.98-1.07]). Over 12.8years of follow-up, 93 NAFLD and 415 non-NAFLD participants experienced a CV event. However, coffee intake was not associated with incident CVD events, in either NAFLD (HR=1.05 [0.91-1.21]) or non-NAFLD participants (HR=1.03 [0.97-1.11]). CONCLUSION:In a large, population-based cohort, coffee consumption was not associated with the prevalence of subclinical CVD, nor did coffee impact the future risk of major CVD events, regardless of underlying NAFLD status.

Project description:ImportanceThe interplay of self-rated health (SRH), coronary artery calcium (CAC) scores, and cardiovascular risk is poorly described.ObjectivesTo assess the degree of correlation between SRH and CAC, to determine whether these measures are complementary for risk prediction, and to assess the incremental value of the addition of SRH to established risk tools.Design, setting, and participantsThe Multi-Ethnic Study of Atherosclerosis (MESA) is a large population-based prospective cohort study of adults aged 45 to 84 years who were recruited from 6 US communities. A total of 6764 participants without baseline cardiovascular disease (CVD) were included in the analysis. Data were collected from July 2000 through August 2002. Follow-up was completed by December 2013, and data were analyzed from October 2018 to December 2018.ExposuresThe EVGGFP (excellent, very good, good, fair, and poor) self-assessment of overall health (assessed before the baseline study examination) and CAC score. The EVGGFP rating was categorized as poor/fair, good, very good, or excellent.Main outcomes and measuresHard coronary heart disease (CHD) events, hard CVD events, and all-cause mortality during a median follow-up of 13.2 years (interquartile range, 12.7-13.7 years).ResultsAmong the study population of 6764 participants, the mean (SD) age was 62.1 (10.2) years, and 52.9% were women. The EVGGFP rating was strongly associated with age, sex, race/ethnicity, educational and income levels, healthy diet and physical activity, and cardiovascular risk factors. Despite encapsulating many risk variables, no correlation (r = -0.007; P = .57) or association between EVGGFP and the presence (χ2 = 0.84; P = .84) or severity (χ2 = 4.64; P = .86) of CAC was found. During follow-up, 1161 deaths, 637 hard CVD events, and 405 hard CHD events were recorded. In models adjusted for age, sex, race/ethnicity, and CAC, participants who reported excellent health had a 45% lower risk of CVD (hazard ratio [HR], 0.55; 95% CI, 0.39-0.77) and a 42% lower risk of CHD (HR, 0.58; 95% CI, 0.37-0.90) compared with those who reported poor/fair health. Participants in the excellent SRH category who had any CAC had markedly elevated risk of hard CHD (HR, 6.19; 95% CI, 2.1-18.3) and CVD (HR, 6.50; 95% CI, 2.7-15.6) events compared with those with a CAC score of 0. The addition of the EVGGFP rating to CAC improved the area under the curve (C statistic) for CHD events (0.725 vs 0.734; P = .007), CVD events (0.693 vs 0.706; P < .001), and all-cause mortality (0.685 vs 0.707; P < .001). However, the addition of the EVGGFP rating to the combination of CAC and atherosclerotic CVD risk score did not significantly improve C statistics for CHD events (0.751 vs 0.753; P = .39), CVD events (0.739 vs 0.741; P = .18), or all-cause mortality (0.779 vs 0.781; P = .13).Conclusions and relevanceAlthough SRH and CAC integrate many risk variables, this study suggests that they are poorly correlated and have complementary predictive utility. A perception of excellent health does not obviate the need for definitive assessment of CVD risk, whereas fair/poor perceived health may serve as a risk enhancer, arguing for advanced risk assessment in selected clinical scenarios.

Project description:BackgroundStability of risk estimates from prediction models may be highly dependent on the sample size of the dataset available for model derivation. In this paper, we evaluate the stability of cardiovascular disease risk scores for individual patients when using different sample sizes for model derivation; such sample sizes include those similar to models recommended in the national guidelines, and those based on recently published sample size formula for prediction models.MethodsWe mimicked the process of sampling N patients from a population to develop a risk prediction model by sampling patients from the Clinical Practice Research Datalink. A cardiovascular disease risk prediction model was developed on this sample and used to generate risk scores for an independent cohort of patients. This process was repeated 1000 times, giving a distribution of risks for each patient. N = 100,000, 50,000, 10,000, N min (derived from sample size formula) and N epv10 (meets 10 events per predictor rule) were considered. The 5-95th percentile range of risks across these models was used to evaluate instability. Patients were grouped by a risk derived from a model developed on the entire population (population-derived risk) to summarise results.ResultsFor a sample size of 100,000, the median 5-95th percentile range of risks for patients across the 1000 models was 0.77%, 1.60%, 2.42% and 3.22% for patients with population-derived risks of 4-5%, 9-10%, 14-15% and 19-20% respectively; for N = 10,000, it was 2.49%, 5.23%, 7.92% and 10.59%, and for N using the formula-derived sample size, it was 6.79%, 14.41%, 21.89% and 29.21%. Restricting this analysis to models with high discrimination, good calibration or small mean absolute prediction error reduced the percentile range, but high levels of instability remained.ConclusionsWidely used cardiovascular disease risk prediction models suffer from high levels of instability induced by sampling variation. Many models will also suffer from overfitting (a closely linked concept), but at acceptable levels of overfitting, there may still be high levels of instability in individual risk. Stability of risk estimates should be a criterion when determining the minimum sample size to develop models.

Dataset Information

Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample.

Publications

Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample.

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