Laboratory and non-laboratory-based risk prediction models for secondary prevention of cardiovascular disease: the LIPID study

Comparison of laboratory-based and non-laboratory-based WHO cardiovascular disease risk charts: a population-based study

Background

Determining the risk of Cardiovascular Disease (CVD) is a necessity for timely preventive interventions in high-risk groups. However, laboratory testing may be impractical in countries with limited resources. This study aimed at comparison and assessment of the agreement between laboratory-based and non-laboratory-based WHO risk charts models.

Methods

This study was performed using the baseline data of 8138 participants in the pars cohort study who had no history of CVD and stroke. The updated 2019 WHO model was used to determine the 10-year fatal and non-fatal CVD risks. In general, there are two types of new WHO risk prediction models for CVD. The scores were determined based on age, sex, smoking status, diabetes, Systolic Blood Pressure (SBP), and total cholesterol for the laboratory-based model and age, sex, smoking status, SBP, and Body Mass Index (BMI) for the non-laboratory-based model. The agreement of these two models was determined via kappa statistics for the classified risk (low: < 10%, moderate: 10–< 20%, high: ≥ 20%). Correlation coefficients (r) and scatter plots was used for correlation between scores.

Results

The results revealed very strong correlation coefficients for all sex and age groups (r = 0.84 for males < 60 years old, 0.93 for males ≥ 60 years old, 0.85 for females < 60 years old, and 0.88 for females ≥ 60 years old). In the laboratory-based model, low, moderate, and high risks were 76.10%, 18.17%, and 5.73%, respectively. These measures were respectively obtained as 77.00%, 18.08%, and 4.92% in the non-laboratory-based model. Based on risk classification, the agreement was substantial for males < 60 years old and for both males and females aged ≥ 60 years (kappa values: 0.79 for males < 60 years old, 0.65 for males ≥ 60 years old, and 0.66 for females ≥ 60 years old) and moderate for females < 60 years old (kappa = 0.46).

Conclusions

The non-laboratory-based risk prediction model, which is simple, inexpensive, and non-invasive, classifies individuals almost identically to the laboratory-based model. Therefore, in countries with limited resources, these two models can be used interchangeably.

An office-based cardiovascular prediction model developed and validated in cohort studies of a middle-income country: Developing and validating an office-based model to predict cardiovascular mortality using cohorts of a middle-income country

OBJECTIVE

Prediction models for cardiovascular disease (CVD) mortality come from high-income countries, comprising laboratory measurements not suitable for resource-limited countries. This study aims to develop and validate a non-laboratory model to predict CVD mortality in a middle-income setting.

STUDY DESIGN AND SETTING

We used data of population aged 40-80 years from three cohort studies: Tehran Lipid and Glucose Study (n=5160), Isfahan Cohort Study (n=4350), and Golestan Cohort Study (n=45,500). Using Cox proportional hazard models, we developed prediction models for men and women, separately. Cross-validation and bootstrapping procedures were applied. The models' discrimination and calibration were assessed by concordance statistic (C-index) and calibration plot, respectively. We calculated the models' sensitivity, specificity and net benefit fraction in a threshold probability of 5%.

RESULTS

The 10-year CVD mortality risks were 5.1% (95%CI: 4.8-5.5) in men and 3.1% (95%CI: 2.9%-3.3%) in women. The optimism-corrected performance of the model was c=0.774 in men and c=0.798 in women. The models showed good calibration in both sexes, with a predicted-to-observed ratio of 1.07 in men and 1.09 in women. The sensitivity was 0.76 in men and 0.66 in women. The net benefit fraction was higher in men compared to women (0.46 vs. 0.35).

CONCLUSION

A low-cost model can discriminate well between low- and high-risk individuals, and can be used for screening in low-middle income countries.

Developing and validating a cardiovascular risk score for patients in the community with prior cardiovascular disease

OBJECTIVE

Patients with atherosclerotic cardiovascular disease (CVD) vary significantly in their risk of future CVD events; yet few clinical scores are available to aid assessment of risk. We sought to develop a score for use in primary care that estimates short-term CVD risk in these patients.

METHODS

Adults aged <80 years with prior CVD were identified from a New Zealand primary care cohort study (PREDICT), and linked to national mortality, hospitalisation and dispensing databases. A Cox model with an outcome of myocardial infarction, stroke or CVD death within 2 years was developed. External validation was performed in a cohort from the UK.

RESULTS

24 927 patients, 63% men, 63% European, median age 65 years (IQR 58-72 years), experienced 1480 CVD events within 2 years after a CVD risk assessment. A risk score including ethnicity, comorbidities, body mass index, creatine creatinine and treatment, in addition to established risk factors used in primary prevention, predicted a median 2-year CVD risk of 5.0% (IQR 3.5%-8.3%). A plot of actual against predicted event rates showed very good calibration throughout the risk range. The score performed well in the UK cohort but overestimated risk for those at highest risk, who were predominantly patients defined as having heart failure.

CONCLUSIONS

The PREDICT-CVD secondary prevention score uses routine measurements from clinical practice that enable it to be implemented in a primary care setting. The score will facilitate risk communication between primary care practitioners and patients with prior CVD, particularly as a resource to show the benefit of risk factor modification.

Periodontal conditions and incidence of new cardiovascular events among patients with coronary vascular disease

AIM

We wanted to investigate whether periodontal conditions and/or oral care habits are associated with new cardiovascular events among patients with coronary vascular disease (CVD).

MATERIALS AND METHODS

In this longitudinal cohort study, 1002 inpatients with CVD were included. They were examined regarding prevalence of severe periodontitis, bleeding upon probing (BOP), number of missing teeth and oral care habits. The combined endpoint was defined as myocardial infarction, stroke/transient ischaemic attack, cardiovascular death and death caused by stroke. Survival analyses were carried out after a 3-year follow-up period. Hazard ratios (HRs) were adjusted for known cardiac risk factors using Cox regression.

RESULTS

Nine hundred and fifty-three patients completed the 3-year follow-up. The overall incidence of the combined endpoint was 16.4%. Significant HRs for BOP (HR = 2, 95% CI: 1.2-3.3), severe tooth loss (HR = 1.8, 95% CI: 1.3-2.5), brushing teeth more than once a day (HR = 0.6, 95% CI: 0.5-1.0) and use of floss/inter-dental brushes (HR = 0.5, 95% CI: 0.3-0.9) were evaluated only in univariate but not in multivariate survival analyses. Patients with severe periodontitis achieved the combined endpoint more often (18.9% versus 14.2%), but the result was not statistically significant after both univariate and multivariate survival analyses.

CONCLUSIONS

Periodontal conditions and oral care habits are not independent indicators for further adverse events in patients with CVD.

Traditional Risk Factors Versus Biomarkers for Prediction of Secondary Events in Patients With Stable Coronary Heart Disease: From the Heart and Soul Study

BACKGROUND

Patients with stable coronary heart disease (CHD) have widely varying prognoses and treatment options. Validated models for risk stratification of patients with CHD are needed. We sought to evaluate traditional and novel risk factors as predictors of secondary cardiovascular (CV) events, and to develop a prediction model that could be used to risk stratify patients with stable CHD.

METHODS AND RESULTS

We used independent derivation (912 participants in the Heart and Soul Study) and validation (2876 participants in the PEACE trial) cohorts of patients with stable CHD to develop a risk prediction model using Cox proportional hazards models. The outcome was CV events, defined as myocardial infarction, stroke, or CV death. The annual rate of CV events was 3.4% in the derivation cohort and 2.2% in the validation cohort. With the exception of smoking, traditional risk factors (including age, sex, body mass index, hypertension, dyslipidemia, and diabetes) did not emerge as the top predictors of secondary CV events. The top 4 predictors of secondary events were the following: N-terminal pro-type brain natriuretic peptide, high-sensitivity cardiac troponin T, urinary albumin:creatinine ratio, and current smoking. The 5-year C-index for this 4-predictor model was 0.73 in the derivation cohort and 0.65 in the validation cohort. As compared with variables in the Framingham secondary events model, the Heart and Soul risk model resulted in net reclassification improvement of 0.47 (95% CI 0.25 to 0.73) in the derivation cohort and 0.18 (95% CI 0.01 to 0.40) in the validation cohort.

CONCLUSIONS

Novel risk factors are superior to traditional risk factors for predicting 5-year risk of secondary events in patients with stable CHD.

HIV, vascular and aging injuries in the brain of clinically stable HIV-infected adults: a (1)H MRS study

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Dietary information improves cardiovascular disease risk prediction models

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.

Panoptic total cardiovascular risk prediction using all predictors versus optimized risk assessment using variable subsets

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.

Overview of risk prediction models in cardiovascular disease research

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.