Introduction and Objectives
One-fourth of the world’s population is in South Asia1 and South Asian migrants are found worldwide.
Cardiovascular diseases (CVD) are the commonest cause of death globally
and three-quarters of CVD deaths take place in low- and middle-income
countries 2. Prevention of CVD is the most
cost-effective especially for developing countries like South Asian
countries. A total risk-based approach is recommended for CVD prevention3. Cardiovascular (CV) risk prediction is an essential
entity in this approach. However, there are no CV risk prediction models
developed in South Asians or South-East Asians. Therefore, different
risk models developed in white Caucasians; e.g. Framingham risk scores
(FRS)4 or developed by modelling approach; e.g. World
health organization/International society of hypertension (WHO/ISH) risk
charts 5; are being used to risk predict South Asians.
Both the standard models (FRS with- with-cholesterol and WHO/ISH charts
with-cholesterol)and the low-information models made up without
cholesterol values in equation ( FRS with-BMI and WHO/ISH
without-cholesterol) are being used depending on the availability of
recourses. However, the best risk score in risk prediction of South
Asians is not known. Only a very few studies have looked into this
question and the literature is inconclusive and also the available
studies are difficult to be compared 6.
South Asians have a high risk of CVD compared to other Asians and white
Caucasians 7. They have a different CVD profile; high
risk of CVD than Whites in the UK 8,9 and America10, a rising trend of CVDs despite CVDs having a
declining trend in the west 11, more strokes than
coronary heart diseases and CVDs at younger ages12,13. Furthermore, they have different genetics and
have high prevalences of vascular risk factors like diabetes mellitus
and metabolic syndrome than white Caucasians 14-17.
Therefore, the risk prediction models developed of Western cohorts might
not be accurately predicting CV-risk of South Asians.
Therefore, we compared 10-year general-cardiovascular risk predictions
of four commonly used models in South Asians; Framingham BMI-based,
Framingham cholesterol-based, WHO/ISH with-cholesterol and WHO/ISH
without-cholesterol for agreement in a sample of Sri Lankans without
prior CVDs.
Methods
All consecutive adults attending a non-communicable disease clinic at
the Faculty of Medicine, University of Kelaniya, Sri Lanka were screened
in 2019 over one year. Patients with vascular risk factors and having
complete data to calculate CV-risk scores but without a past history of
CVD were enrolled in this study. Data on vascular risk factors were
collected using an interviewer-administered questionnaire and referring
clinic records. Height and weight were measured at the clinic. Two blood
pressure measurements were done in the left arm 5 minutes apart in
seated position with a mercury sphygmomanometer. 10-year CV-risk
predictions of all participants were calculated using four models; FRS
BMI-based, FRS cholesterol-based, WHO/ISH charts without and
with-cholesterol. 10-year risk predictions of developing a fatal or
non-fatal CVD were calculated using the formulas; Framingham 10-year
general CVD risk 18 and WHO/ISH charts meant for
South-East Asian Region- B (SEAR- B) 19. FRS was
calculated using age, systolic blood pressure (SBP), antihypertensive
use, current smoking status, diabetes status, and body mass index (BMI)
or total cholesterol (TC) and high-density lipoprotein (HDL) level.
WHO/ISH risk predictions were calculated using age, SBP, current smoking
status, diabetes status and additionally with total cholesterol for
WHO/ISH with-cholesterol estimate. BMI was calculated with weight and
height. The mean of the two blood pressure measurements made at the
clinic was used as the SBP. The most recent recorded TC and HDL values
within the previous year were used in risk calculations. All current
smokers and those who quit smoking less than 1 year before the
assessment were considered current smokers. Persons with self-reported
diabetes mellitus cross-checked with medical records or taking insulin
or oral hypoglycaemic drugs were considered as having diabetes mellitus
according to the World Health Organization,
criteria20. People with self-reported hypertension
cross-checked with medical records, physician-diagnosed hypertension, or
taking antihypertensive medications were defined as hypertension,
according to the Joint National Committee (JNC) VII criteria21. Past history of hyperlipidaemia was defined as
someone with physician-diagnosed hyperlipidaemia in medical records on
the National Cholesterol Education Program III criteria22. Patients were categorised into two risk groups
using risk estimates; low risk (<20% ) and high risk (≥ 20%)
risk.
IBM SPSS statistics version 22.0 was used for analysis. Continuous
variables were reported as means with standard deviation (SD) or 95%
confidence intervals, and categorical variables were reported as
percentages. The significance level was set at p <0.05. Mean
Framingham risks of BMI-based and cholesterol-based models were compared
using the paired sample Students t-test. Risk predictions of the models
were compared for agreement across risk categories with Cohen’s kappa
coefficient (κ ). The strength of agreement was interpreted as,κ : ≤ 0.20 = poor, 0.21–0.40 = fair, 0.41–0.60 = moderate,
0.61–0.80 = good and 0.81–1.00 = very good 23-25.
Ethics approval was obtained from the Ethics Review Committee, Faculty
of Medicine, University of Kelaniya, Sri Lanka. Informed consent of all
the patients was obtained.