Our study shows that a non-laboratory-based risk method that uses information easily obtained in one outpatient visit can predict cardiovascular disease outcomes as accurately as one that requires laboratory testing. Our values of predictive discrimination of 0·83 (women) and 0·78 (men) for the non-laboratory-based model are no different than the corresponding values in the laboratory-based model. Further, this study showed that the prediction method in the NHEFS cohort using these easily obtainable risk factors (age, systolic blood pressure, smoking status, blood pressure treatment status, history of diabetes mellitus, and body-mass index) was as useful as other methods recommended for screening for cardiovascular disease6,8,21–23
with c statistics between 0·64 and 0·86.
WHO has released guidelines for the prevention of cardiovascular disease24
that included two sets of risk assessment charts. The first WHO charts used the same risk factors we used in our laboratory-based model. The second set of charts removed cholesterol and retained the remaining risk factors. This step was a positive move towards a simplified screening process in resource-poor settings; however, given that the WHO charts were not based on a cohort with prospective outcomes of cardiovascular disease, the investigators were not able to assess the predictive discrimination of either of their charts or make any comparisons between them. Further, until now, this approach has not been validated in any of the WHO regions or countries where it is intended to be used. The present study provides strong support for the WHO approach.
The next step in the process of moving towards wider use of simplified scoring mechanism is further validation and calibration in individual countries, which can be done in one of two ways. Countries could develop their own charts using their own cohort data. This was the process used to design the SCORE system7
for high-risk and low-risk regions of Europe. We would recommend that any country with prospective cohort data create their own non-laboratory-based charts and assess their discrimination compared with a laboratory-based method. Alternatively, countries or regions without cohort data could calibrate the risk-factors using national or regional data for cardiovascular morbidity and mortality rates. This process has been done in New Zealand25
using the Framingham risk equations. Until these country or region-specific scores are available, health providers could consider using the WHO non-laboratory-based charts or the chart shown in and .
Risk prediction chart for cardiovascular disease using non-laboratory-based measures (women)
Risk prediction chart for cardiovascular disease using non-laboratory-based measures (men)
Our chart differs from the WHO chart in that it includes body-mass index and is the only chart that is based on a model compared formally with a laboratory-based model using a prospectively followed cohort. Nonetheless, countries should recognise that the WHO charts, or our charts, might overestimate or underestimate absolute risk. The potential error in estimation would need to be weighed against the health losses associated with no screening at all. There is a similar challenge whenever data from one country are generalised to another, whether it is for risk prediction or intervention trials. The results of the INTERHEART27
study suggest, however, that the relative risks are similar across most countries, so the ranking of individuals according to relative risk is preserved even if absolute risk is overestimated or underestimated. Resources can still be efficiently allocated to those at highest risk by adjusting treatment thresholds according to an individual country’s priorities.
In addition to the overall predictive discrimination, there are other reasons for focusing on a non-laboratory screening method in developing countries. 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. The non-laboratory-based model correctly classified most men and women at the 10% and 20% 5-year risk thresholds and was not inferior to the laboratory-based model. At most, the rates of correct classification differed by less than 1% and none of the differences were significant ().
In practical terms, if a patient has difficulty returning for a fasting blood test (if even available) on a different day, then the opportunity to assist in prevention is lost. The advantage of a non-laboratory-based method of risk prediction is that it can be applied in one clinic visit with minimum equipment needed—tape measure, scale, and sphygmamometer or automated blood pressure machine. A risk prediction value can be ascertained and a treatment decision can be made within the same 5–10 min visit without the cost or the time needed to wait for laboratory results. Others have suggested that waist-to-hip ratio is also easily measured and could add greater predictive discrimination over body-mass index. However, whether the measurement of waist-to-hip ratio in routine practice is as easy to accurately measure as body-mass index is not clear. If it is better in practice, this method would even strengthen the non-laboratory-based model. Unfortunately, waist-to-hip ratio measurements were not available in the NHEFS baseline dataset to confirm this suggestion.
In developed countries, the added cost of cholesterol testing is about $10 for the test and an additional $20–$80 if an additional visit is needed. The rates for developing countries are $1–$3 for the test and $3–$7 for an additional visit.28
In India, which spends around $31 per person on health care each year,4
guidelines recommending cholesterol screening to risk stratify patients would need more than 10% of the entire Indian health-care budget to be devoted to this one laboratory test with little or no added benefit beyond what is already available without laboratory testing. Therefore, the added costs of cholesterol testing would make screening with laboratory-based guidelines prohibitive. Further, in many developing countries, the personal cost of being seen at a health facility can be quite high. A patient often must take a day off work to see the nurse or doctor. There are additional costs of the health-care workers’ time. Finally, most countries do not have the facilities or health-care workforce to implement such laboratory-based screening.
We could imagine a two-tiered approach to the management of those at risk for cardiovascular disease. Those who are at low risk could be given advice about a healthy lifestyle and be reassessed in 5–10 years. Our negative predictive value of around 90% for men and women at low risk suggests that this strategy is reasonable. Those who are at high risk could be provided with counselling about possible risk-factor management and possibly medical treatments such as a multidrug regimen like the polypill where resources are available. Lim and colleagues11
in the Lancet
Chronic Diseases Series recommended the use of a chart with the same non-laboratory-based risk factors to identify those eligible for a multidrug intervention. Like the WHO charts, this method has not been validated in a cohort, although the positive predictive value of 75% for those at high risk also suggests this strategy is reasonable. For people at the boundary of low risk and high risk, laboratory testing might be useful for further risk stratifying for certain individuals when available.
Several attempts to enhance the predictive discrimination of the Framingham Heart Study score by adding biomarkers have had mixed results. The analyses in the Atherosclerosis Risk in Communities (ARIC) Study29
and the Framingham Off spring Study30,31
suggested that little additional information was gained when other blood-based novel risk factors were added to the traditional risk factors. However, the Reynolds32
score for women, which added hsCRP and haemoglobin A1c
, had a marginally higher c statistic (0·808) than the Framingham covariates (0·791), but led to a reclassification of many women to higher or lower risk groups.
One limitation of our study is that it represents a population from the USA. We do not know whether the proposed risk score would be as useful in developing countries. Unfortunately, few low-income countries have cohort data to make these estimates. As a result, most countries have been using the Framingham risk equation or similar with blood-based parameters to support their guidelines. Our results suggest that this simpler method is probably no worse yet substantially cheaper and simpler to implement. Also, this cohort from the 1970s has a risk distribution and treatment levels that are similar to many developing countries that are currently going through the same stage of epidemiological transition that the USA cohort was at the time. The high prevalence of smoking, especially in men, is typical of low-income and middle-income countries (around 49% for men).33
Also, the prevalence (36%) of hypertension in the NHANES I cohort34
is similar to the rates seen in eastern Europe and Latin America.35
Rates of treatment for hypertension were only about 10% and there was no use of statins throughout most of the time of the cohort follow-up.
Another limitation is that we used total cholesterol, but did not include HDL cholesterol as is done in the updated Framingham5
scoring mechanism, since this information was not collected in NHANES I. Total cholesterol is a reasonable independent risk factor for cardiovascular disease34–38
However, the fact that a risk factor is a good independent predictor is only a necessary condition but not a sufficient condition for its inclusion in a model of multiple risk factors.39
Its added value to a list of other readily available information seems limited. The predictive discrimination of the laboratory-based model might improve if HDL was included. However, even if the model was marginally improved, whether it would be worth the added cost and inconvenience in developing countries is unclear. Certainly, cost-effectiveness analyses will need to be done to assess the additional value of HDL cholesterol to a non-laboratory-based model in other datasets.
In summary, we found that a non-laboratory-based method using blood pressure levels and treatment status, age, smoking status, body-mass index, and a history of diabetes yielded results similar to one that uses laboratory testing when predicting cardiovascular disease events. Further, this risk factor information can be obtained non-invasively in about 5–10 min. Although this method requires further validation and calibration, use of a simple non-laboratory approach, as suggested by the WHO, could have profound effects on the affordability and availability of an adequate screening programme in developing countries. Initial screening without blood testing could lead to the quick initiation of treatment without the added cost or inconvenience of laboratory testing, and would also keep any potential loss to follow-up due to the extra step in testing to a minimum.