Laboratory and non-laboratory-based risk prediction models for secondary prevention of cardiovascular disease: the LIPID study.
ABSTRACT The aims of this study were to examine whether risk prediction models for recurrent cardiovascular disease (CVD) events have prognostic value, and to particularly examine the performance of those models based on non-laboratory data. We also aimed to construct a risk chart based on the risk factors that showed the strongest relationship with CVD.
Cox proportional hazards models were used to calculate a risk score for each recurrent event in a CVD patient who was enrolled in a very large randomized controlled clinical trial. Patients were then classified into groups according to quintiles of their risk score. These risk models were validated by calibration and discrimination analyses based on data from patients recruited in New Zealand for the same study. Non-laboratory-based risk factors, such as age, sex, body mass index, smoking status, angina grade, history of myocardial infarction, revascularization, stroke, diabetes or hypertension and treatment with pravastatin, were found to be significantly associated with the risk of developing a recurrent CVD event. Patients who were classified into the medium and high-risk groups had two-fold and four-fold the risk of developing a CVD event compared with those in the low-risk group, respectively. The risk prediction models also fitted New Zealand data well after recalibration.
A simpler non-laboratory-based risk prediction model performed equally as well as the more comprehensive laboratory-based risk prediction models. The risk chart based on the further simplified Score Model may provide a useful tool for clinical cardiologists to assess an individual patient's risk for recurrent CVD events.
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ABSTRACT: Cardiovascular disease (CVD) and premature aging have been hypothesized as new risk factors for HIV associated neurocognitive disorders (HAND) in adults with virally-suppressed HIV infection. Moreover, their significance and relation to more classical HAND biomarkers remain unclear. 92 HIV- infected (HIV+) adults stable on combined antiretroviral therapy (cART) and 30 age-comparable HIV-negative (HIV-) subjects underwent (1)H Magnetic Resonance Spectroscopy (MRS) of the frontal white matter (targeting HIV, normal aging or CVD-related neurochemical injury), caudate nucleus (targeting HIV neurochemical injury), and posterior cingulate cortex (targeting normal/pathological aging, CVD-related neurochemical changes). All also underwent standard neuropsychological (NP) testing. CVD risk scores were calculated. HIV disease biomarkers were collected and cerebrospinal fluid (CSF) neuroinflammation biomarkers were obtained in 38 HIV+ individuals. Relative to HIV- individuals, HIV+ individuals presented mild MRS alterations: in the frontal white matter: lower N-Acetyl-Aspartate (NAA) (p<.04) and higher myo-inositol (mIo) (p<.04); in the caudate: lower NAA (p = .01); and in the posterior cingulate cortex: higher mIo (p<.008- also significant when Holm-Sidak corrected) and higher Choline/NAA (p<.04). Regression models showed that an HIV*age interaction was associated with lower frontal white matter NAA. CVD risk factors were associated with lower posterior cingulate cortex and caudate NAA in both groups. Past acute CVD events in the HIV+ group were associated with increased mIo in the posterior cingulate cortex. HIV duration was associated with lower caudate NAA; greater CNS cART penetration was associated with lower mIo in the posterior cingulate cortex and the degree of immune recovery on cART was associated with higher NAA in the frontal white matter. CSF neopterin was associated with higher mIo in the posterior cingulate cortex and frontal white matter. In chronically HIV+ adults with long-term viral suppression, current CVD risk, past CVD and age are independent factors for neuronal injury and inflammation. This suggests a tripartite model of HIV, CVD and age likely driven by chronic inflammation.PLoS ONE 04/2013; 8(4):e61738. DOI:10.1371/journal.pone.0061738 · 3.53 Impact Factor
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ABSTRACT: Cardiovascular disease remains the primary cause of mortality and morbidity in the developed world. Risk scores can provide clinical risk stratification and many exist for use in cardiovascular disease prevention and treatment. Cardiovascular risk scores predict mortality, coronary heart disease and other vascular disease using risk predictors such as patient age, sex, BMI, smoking history, cholesterol level, blood pressure, glucose level or diabetes diagnosis, family history of cardiovascular disease and creatinine. While the risk scores in existence are excellent for risk stratification, actual use in a clinical environment is lagging behind the rate of new risk score creation. Future research should focus on how to utilize risk scores most effectively and efficiently in clinical practice.Future Cardiology 09/2012; 8(5):765-78. DOI:10.2217/fca.12.49
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ABSTRACT: Background/objectives:Data are limited on cardiovascular disease (CVD) risk prediction models that include dietary predictors. Using known risk factors and dietary information, we constructed and evaluated CVD risk prediction models.Subjects/methods:Data for modeling were from population-based prospective cohort studies comprised of 9026 men and women aged 40-69 years. At baseline, all were free of known CVD and cancer, and were followed up for CVD incidence during an 8-year period. We used Cox proportional hazard regression analysis to construct a traditional risk factor model, an office-based model, and two diet-containing models and evaluated these models by calculating Akaike information criterion (AIC), C-statistics, integrated discrimination improvement (IDI), net reclassification improvement (NRI) and calibration statistic.Results:We constructed diet-containing models with significant dietary predictors such as poultry, legumes, carbonated soft drinks or green tea consumption. Adding dietary predictors to the traditional model yielded a decrease in AIC (delta AIC=15), a 53% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.14, P <0.001). The simplified diet-containing model also showed a decrease in AIC (delta AIC=14), a 38% increase in relative IDI (P-value for IDI <0.001) and an increase in NRI (category-free NRI=0.08, P<0.01) compared with the office-based model. The calibration plots for risk prediction demonstrated that the inclusion of dietary predictors contributes to better agreement in persons at high risk for CVD. C-statistics for the four models were acceptable and comparable.Conclusions:We suggest that dietary information may be useful in constructing CVD risk prediction models.European Journal of Clinical Nutrition advance online publication, 14 November 2012; doi:10.1038/ejcn.2012.175.European journal of clinical nutrition 11/2012; DOI:10.1038/ejcn.2012.175 · 3.07 Impact Factor