In this study, we found that a non-laboratory-based CVD risk score, when compared to six versions of laboratory-based Framingham, SCORE and CUORE equations, similarly ranked individuals and characterized CVD risk in all 13 cohorts studied. We observed strong agreement in risk characterization between the non-laboratory-based and laboratory-based scores in all the cohorts. This was true for the aggregate of the cohorts and in each of the cohorts, which had a wide range of overall risk suggesting the non-laboratory-based risk tool performs as well in low-risk groups as it does in high-risk populations. The greatest agreement was with the SCORE risk tool with more than 96% agreement in both men and women. More than 92% of men and 97% of women were equivalently characterized as ‘high’ or ‘low’ risk by non-laboratory-based and laboratory-based scores using a risk threshold based on a threshold commonly used in guidelines (10-year CHD risk >20 . At a risk of 5% in both the high and low risk populations, the SCORE equation is equivalent to the fatal and non-fatal threshold of 20% for Framingham CVD risk.
Correlation at the CVD risk threshold of >20% was highest for the laboratory-based CVD outcome scores, with the older Framingham CHD only score slightly lower. In general, the correlation was greater for women than for men. In men, using the 1991 Framingham CHD score, the correlations for two cohorts, AHA-FS rural and the PURE urban, were slightly lower (0.797, 0.751, respectively). This is important as there has been a movement for risk prediction tools to include both stroke and ischemic heart disease given that many low- and middle-income countries, including South Africa, have a higher proportion of stroke in the total CVD events. Further, reductions in most risk factors for ischemic heart disease also reduce stroke and, thus, a risk prediction tool including both is more in line with clinical activities. However, this level of correlation is still considered ‘high correlation’ being greater than 0.7 if not ‘very high correlation’ for correlations greater than 0.9 .
The ability to assess risk without the requirement of expensive laboratory blood tests is important in many low-income settings for multiple reasons. The first includes its ability to correctly classify patients at the thresholds that most prevention guidelines choose for initiating treatment. Other considerations include practicality, cost and feasibility, in particular providing clinicians with the opportunity to make a treatment decision during a single clinic visit. This obviates the need for a laboratory test and a second patient visit to review the results and plan management with a resultant reduction of costs and the potential for non-attendance at the follow up visit. The cost of cholesterol testing and the follow-up clinic visit in South Africa is more than US $30. Testing for cholesterol for the adult population even at a frequency of every five years would cost $110 million annually. The annual cost of treating a patient on generic simvastatin can be less than $20  (less than the cost of testing and follow-up visits) which on an annual basis for all those above 20% 10-year risk would equal nearly $60 million. Thus, money saved from excessive laboratory testing can easily offset the cost of treating those at high risk with an additional savings of nearly $50 million. In addition to these savings would be the added benefit of preventing more premature deaths and disabling myocardial infarctions and strokes. Furthermore, patients, their families or members of the public, with access to a blood pressure device, can calculate their own risk and present themselves to facilities for further evaluation. This opens up the potential for increased opportunistic screening. Furthermore, it could lead to improved risk stratification for those who may be treated so that limited medications go to those who need it most.
Using the non-laboratory-based screening tool, nearly 18% of the DHS and aggregate study populations would have a 10-year CVD death risk greater than 20%. What is striking is that using the 1998 Demographic Health Survey population, the risk for women is only slightly lower than for men, which is not the case for most developed populations where men have a considerably higher risk, at least in the middle-aged populations. However, in the DHS and in many of the cohorts included in the analysis, while smoking rates tend to be higher for men, the opposite is true for diabetes and obesity and hypertension in the elderly. The obesity rates are almost double and the diabetes rate is nearly 50% greater for women than for men . These differences suggest that South African women, in particular, could be facing a greater burden compared to men, especially as smoking rates for men continue to decline.
One limitation of our analysis is that the risk discrimination and validation performance of the non-laboratory-based risk score has not been validated in a longitudinal South African cohort study. The lack of reliable death data for these cross-sectional cohorts makes it impossible at this time to validate any prediction score for CVD death. Having a cohort study with death data would also allow us to assess the level of correct classification and misclassification between the different risk scores. It is also possible that the non-laboratory-based risk score may over- or underestimate the true risk, but that is true for all the risk scores compared in the analysis. This is also true for most individual risk factors on which developing countries base their guidelines. Unfortunately, few low-income countries have cohort data to make these estimates. No risk score has been validated in South Africa nor, for that matter, in most populations in low- and middle-income countries, except China . However, studies to validate the risk score are ongoing in South Africa but this data will not be available for nearly five years. However, the clinical need to risk stratify patients exists now. Our results suggest that using the non-laboratory-based scores will classify people similarly in the meantime as those which rely on laboratory-based information—neither of which have been validated in developing countries. Previous analysis in the US has shown that less than 7% disagreement occurs in risk classification . Further, the WHO/International Society of Hypertension (ISH) risk charts have not been validated in individual country populations but are being promoted for screening. However, all the scores we evaluated will identify the same high risk people. So, if a country is using one of the laboratory-based risk tools, it could substitute a non-laboratory based tool for it and identity the same group for less cost. The WHO/ISH risk charts were not included in our analyses because the underlying risk functions are not publicly available .