In this report we present a simple way to estimate 30-year risk of hard CVD based on risk factors routinely measured during an office visit. The results are based on over 30 years of rigorous follow-up and ascertainment of CVD incidence and death. Our algorithm allows for risk assessment for individuals with any combination of continuous and categorical risk factors. It also accounts for the competing risk of non-cardiovascular death.
Our approach is based on advanced statistical techniques that allow avoiding bias in the assessment of true absolute risk. Ignoring the competing risk of death inflates the estimates by an average of 1–2% on the absolute scale (or 10% on the relative scale), which leads to inferior calibration as demonstrated in the chi-square statistics. On the other hand, simpler approaches which try to make 30-year inference on the basis of a 10-year risk model are inadequate and may lead to under- or overestimation of the true risk burden.
The need for long-term risk prediction tools has been articulated for many years14
as a complement for the shorter-term calculators. There are several reasons why it is necessary. As Sniderman and Furberg28
point out, studies with shorter follow-up miss cases that would come out if the duration was extended and thus “restrict our appreciation of the true importance of the modifiable factors that cause vascular disease”. As seen here, 30-year risk cannot be adequately replaced by different combinations of 10-year risks. Blumenthal et al.27
raise the issue of high lifetime risk of CVD in women to underscore the need for long-term risk prediction algorithms. This point finds a striking illustration in our data, where adverse risk factor profile leads to high 30-year risk despite young age, an effect that is entirely missed by 10-year risk model. Moreover, most contemporary cohorts are confounded by treatment effects which significantly influence short-term prediction. The use of 30-year instruments might partially overcome this problem with its long-term focus. Effective risk communication is another reason why 30-year risk might be helpful. Individuals might be more likely to adopt necessary lifestyle changes upon hearing that their 30-year risk of CVD is 1 in 8 (75th
percentile of the 30-year risk in men below age 40) than when they are told it is 1 in 50 in 10 years (75th
percentile of the 10-year risk in men below age 40). Moreover, the potent impact of accumulation of risk factors as presented in – may serve as an effective risk communication tool.
We have shown that established CVD risk factors which are significant in models based on shorter follow-up duration3, 4, 12
are also significantly related to hard CVD incidence in 30 years. The impact of risk factors measured only at baseline is similar to that of risk factors regularly updated at follow-up. The same is not true for BMI which loses its independent impact when other covariates are time-updated. No significant effect modifications by sex were detected, despite differences in hard CVD composition with strokes comprising 40% of all first events in women and 25% in men.
As indicated earlier, this is the first report to our knowledge that presents a risk score for incidence of hard CVD in the 30-year horizon. In their recent publications researchers from the Chicago Heart Association Detection Project in Industry calculate the remaining lifetime risk until age 85 adjusted for the competing risk of death for participants aged 40–59 with 0–5 elevated CVD risk factors24
and quantify the effect of these standard risk factors on the 30-year risk of CVD, coronary and all-cause mortality in women aged 18–3925
. While their reports offer valuable insights into the effect of risk factors on the long-term risk of cardiovascular disease they were not designed for individual-specific risk prediction in a clinical setting.
The analysis of changes in impact on the risk of coronary heart disease and death of baseline risk factors during long-term follow-up undertaken by the Chicago Heart Association authors24, 25, 26
as well as Menotti and Lanti49
revealed that single-occasion measurement risk factors remain strong predictors even in the long-term models. This finding has been confirmed in our setting.
A few limitations of our study need to be acknowledged. First, our results and the risk score were derived based on a Caucasian cohort, which potentially limits the generalizability to other ethnic groups. Appropriate recalibration (cf. 29
) may correct differences in baseline survival between ethnicities but further investigation is warranted to determine the impact of relative risks for CVD and the competing risk of non-CVD mortality differing between ethnicities. Second, in the assessment of model performance we accounted for over-optimism introduced by evaluating the model on the same data on which it was developed using five-fold cross-validation and internal validation. While these techniques are well suited for this purpose, they cannot be equated with the preferred method of validation in an outside cohort. Third, we considered only standard risk factors and did not investigate the effect of novel biomarkers on the risk of hard CVD as they were not available at the baseline examination in the early 1970s. Wilson et al.50
have shown recently that c-reactive protein measured on the Framingham cohort between late 1970s and early 1980s might contribute to improvement in risk reclassification, a finding postulated before by Ridker et al.10, 13
based on other cohorts. It is plausible that the use of novel biomarkers could help reduce the amount of risk underestimation with 10-year models as compared to 30-year models. Further research is needed to investigate this issue. Fourth, the effect and interplay of risk factors might be different now than it was 30 years ago. However, no other way of constructing a 30-year risk score is possible given the length of time horizon. Fifth, the nature of our design did not account for changes in risk factor levels that can take place during the course follow-up. For example, smoking cessation between early 1970s and the present time period might have reduced the true absolute risk leading to an underestimation of the effect of continued smoking. However, we have shown that regardless of the absence or presence of risk factor adjustment on follow-up, they remain strong independent predictors of hard CVD. Finally, we did not attempt to predict the 30-year risk of death from non-cardiovascular causes; however this risk was implicitly factored into the calculations as the competing cause. Further research is needed to provide estimators of long-term risks of all-cause and non-cardiovascular mortality that would complement the predictions available in this report and allow for patient-specific treatment strategies.
We hope that the simple way of quantifying 30-year risk of hard CVD based on a combination of standard risk factors and additional insights to the nature of their effect presented in this report will complement the currently available 10-year risk algorithms and serve as useful tools in the clinical and public health settings and provide a useful framework for future research.
The impact of standard risk factors (male sex, age, blood pressure and antihypertensive treatment, cholesterol levels, smoking and diabetes) on 10-year risk of coronary or cardiovascular disease (CVD) has been extensively studied and reliable algorithms exist for risk prediction. In the present investigation, we evaluated the impact of standard risk factors on CVD incidence on long-term follow-up, i.e, over 30 years. Our observations suggest that standard risk factors remain highly predictive of CVD risk over a 30-year follow-up period and their impact is substantial even if levels are not updated. We also quantified the impact of body mass index on 30-year risk of CVD and observed that its association with increased CVD risk is mediated partly through promoting higher levels of standard risk factors over a long-term follow-up. Additionally, we have formulated a 30-year CVD risk prediction algorithm that adjusts for the competing risk of death on long-term follow-up. Our observations suggest that different applications of 10-year risk prediction functions for estimating 30-year CVD risk may be suboptimal, especially when applied to younger women and men who have an adverse risk factor profile(over 10% for women and almost 20% in men who smoke, have hypertension and adverse lipid profile). We also have constructed a simple calculator for estimating 30-year risk of CVD that is based on standard risk factors (with and without knowledge of laboratory values) and which could be easily implemented in primary care settings.