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\begin{document}

\title{Place of cardiovascular risk prediction models in South Asians;
agreement between Framingham risk score and WHO/ISH risk charts}



\author[1]{Chamila Mettananda}%
\author[1]{Nadun Gunasekera}%
\author[1]{Ruth Thampoe}%
\author[1]{Sumudu Madhurangi}%
\author[1]{Arunasalam  Pathmeswaran}%
\affil[1]{University of Kelaniya Faculty of Medicine}%


\vspace{-1em}



  
  \date{June 13, 2024}


\begingroup
\let\center\flushleft
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\endgroup





\selectlanguage{english}
\begin{abstract}
Introduction and Objectives There are no cardiovascular risk prediction
models developed in South Asian cohorts. Therefore, different risk
models not validated in South Asians are being used. We aimed to compare
cardiovascular risk predictions of Framingham risk score (FRS) and World
health organization/International society of hypertension (WHO/ISH)
charts for agreement in a sample of South Asians. Methods 10-year
cardiovascular risk predictions of patients without previous
cardiovascular diseases attending a non-communicable disease clinic were
calculated using FRS (with BMI and with cholesterol) and WHO/ISH charts
(with and without cholesterol). Patients were categorized into
low(\textless{}20\% ) and high([?]20\%) cardiovascular risk groups on risk
predictions. Agreement in risk categorisation with different prediction
models was compared using Cohen's kappa coefficient(\selectlanguage{greek}κ\selectlanguage{english}). Results 169
patients (females 130(81.1\%)) mean age 65 \selectlanguage{ngerman}±6.9 years were studied.
80(47.3\%), 62(36.7\%), 18(10.7\%), and 16(9.5\%) were predicted
high-risk by FRS BMI-based, FRS cholesterol-based, WHO/ISH
without-cholesterol and WHO/ISH with-cholesterol models, respectively.
Agreement between the two FRS models (\selectlanguage{greek}κ \selectlanguage{english}= 0.736, p\textless{}0.0001) and
the two WHO/ISH models (\selectlanguage{greek}κ \selectlanguage{english}= 0.804, p\textless{}0.0001) in stratifying
patients into high and low-risk groups, were ``good''. However, the
agreements between, FRS BMI-based and WHO/ISH without-cholesterol models
(\selectlanguage{greek}κ \selectlanguage{english}= 0.234, p\textless{}0.0001) and FRS cholesterol-based and WHO/ISH
with-cholesterol models (\selectlanguage{greek}κ \selectlanguage{english}= 0.306, p\textless{}0.0001) were only
``fair''. Conclusion Cardiovascular risk predictions of FRS were higher
than WHO/ISH charts and the agreement in risk stratification was not
satisfactory in Sri Lankans. Therefore, different cardiovascular risk
prediction models should not be used interchangeably in the follow-up of
South Asians.%
\end{abstract}%



\sloppy


\textbf{What is already known about this topic?}

Framingham score predicts 10-year cardiovascular risk in White
Caucasians. WHO/ISH charts were made to risk predict patients of
different WHO Region. Asians have different genetics and high prevalence
of cardiovascular diseases than white Caucasians. However, there are no
risk prediction models developed in South Asians. Therefore, different
risk prediction models are being used for risk stratification of South
Asians living worldwide.

\textbf{What does this article add?'}

We compared risk predictions of Framingham score and WHO/ISH risk charts
for agreement in risk prediction of Sri Lankan and found that it is not
satisfactory.

Therefore, different cardiovascular risk prediction models should not be
used interchangeably in the follow-up of South Asians.



\textbf{Introduction and Objectives}

One-fourth of the world's population is in South Asia\textsuperscript{1}
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
\textsuperscript{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
prevention\textsuperscript{3}. 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)\textsuperscript{4} or developed by
modelling approach; e.g. World health organization/International society
of hypertension (WHO/ISH) risk charts \textsuperscript{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 \textsuperscript{6}.

South Asians have a high risk of CVD compared to other Asians and white
Caucasians \textsuperscript{7}. They have a different CVD profile; high
risk of CVD than Whites in the UK \textsuperscript{8,9} and
America\textsuperscript{10}, a rising trend of CVDs despite CVDs having
a declining trend in the west \textsuperscript{11}, more strokes than
coronary heart diseases and CVDs at younger ages\textsuperscript{12,13}.
Furthermore, they have different genetics and have high prevalences of
vascular risk factors like diabetes mellitus and metabolic syndrome than
white Caucasians \textsuperscript{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 \textsuperscript{18} and WHO/ISH charts meant for
South-East Asian Region- B (SEAR- B) \textsuperscript{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,
criteria\textsuperscript{20}. 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
criteria\textsuperscript{21}. Past history of hyperlipidaemia was
defined as someone with physician-diagnosed hyperlipidaemia in medical
records on the National Cholesterol Education Program III
criteria\textsuperscript{22}. Patients were categorised into two risk
groups using risk estimates; low risk (\textless{}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 \textless{}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~(\selectlanguage{greek}\emph{κ} \selectlanguage{english}). The strength of agreement was interpreted
as,\selectlanguage{greek}\emph{κ} : \selectlanguage{english}[?] \selectlanguage{english}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 \textsuperscript{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.

\textbf{Results}

169 patients without previous CVDs; 130(81.1\%) females, mean 65 \selectlanguage{ngerman}±6.9
years (range 47-78 years) were studied. Baseline characteristics, risk
factors, and medication use of the sample are shown in Table 1. Of the
total population, 56.2\% were 65 years or older. The majority, 98.8\%
were Singhalese. Risk factor distribution among the study participants
was, hypertension 66.9\%, hyperlipidemia 89.9\%, diabetes mellitus
46.7\%, smoking 2.4\%, and obesity 8.3\%. Patients were on medications;
antihypertensives 65.7\%, lipid-lowering medications 89.9\%,
anti-diabetic medications 46.2\%, and antiplatelet medications 15.5\%
among the total population. Men in comparison to women were older ( 68 ±
4.79 and 64 ± 7.03 years, p \textless{}0.0001 respectively), smoked more
(12.5\%, 0.0\%, p \textless{}0.0001) and were less likely to be on
lipid-lowering medications (77.4\%, 92.7\%, p=0.019).

Table 1 \textbf{Baseline characteristics of the study population}

Comparison of risk factors used in the calculation of Framingham and
WHO/ISH scores and mean FRS of men and women are shown in Table 2. There
was no significant difference in the history of diabetes mellitus, use
of anti-hypertensive medications, and measured risk factors like BMI,
SBP, TC and HDL levels between men and women. The two groups were only
different from age and smoking status. However, the mean FRS of men were
significantly higher than that of females with both BMI-based (male
28.94 ± 3.17, female 17.10 ±8,62) and cholesterol-based (male 26.47 ±
4.99, female 13.86 ± 8.25) models.

Table 2 \textbf{CV-risk factors used in risk calculations and mean
Framingham risk scores by sex}

Patients were categorised into low(\textless{}20\% ) and
high(\(\geq\)20\%) CV-risk groups on risk predictions (Table
3). 80(47.3\%), 62 (36.7\%), 18 (10.7\%), 16 (9.5\%), of the
participants were predicted high risk by FRS BMI-based, FRS
cholesterol-based, WHO/ISH without-cholesterol and WHO/ISH
with-cholesterol models, respectively. Agreement between different risk
models in categorizing patients into low and high-risk groups was
studied using Cohen's kappa statistics (Table 3).

Table 3 \textbf{10-year CV-Risk stratification of the sample with
different risk models and inter-rater agreement}

The two versions of FRS models; BMI-based and cholesterol-based were in
good agreement in stratifying patients into high and low-risk groups, \selectlanguage{greek}κ
\selectlanguage{english}= 0.736, p\textless{}0.0001. Similarly, the two versions of WHO/ISH
models without-cholesterol and with-cholesterol were also in good
agreement in stratifying patients into high and low-risk groups; \selectlanguage{greek}κ \selectlanguage{english}=
0.804, p\textless{}0.0001. However, the agreement between, FRS BMI-based
model and WHO/ISH without-cholesterol model in stratifying patients into
high and low-risk groups was fair; \selectlanguage{greek}κ \selectlanguage{english}= 0.234, p\textless{}0.0001 and FRS
BMI-based risk estimates were higher than WHO/ISH without-cholesterol
estimates. Furthermore, the agreement between, FRS cholesterol-based
model and WHO/ISH with-cholesterol model in stratifying patients into
high and low-risk groups was also fair; \selectlanguage{greek}κ \selectlanguage{english}= 0.306, p\textless{}0.0001
and FRS cholesterol-based risk estimates were higher than WHO/ISH
with-cholesterol estimates.

\textbf{Discussion}

This is the first study comparing 10-year cardiovascular risk
predictions of Framingham and WHO/ISH risk models for agreement among
Sri Lankans and adds to the limited literature from South Asia and
South-East Asian~Region (SEAR) -- B. We observed that FRS and WHO/ISH
(SEAR-B) models were not in good agreement in predicting high-risk
patients. Therefore, the FRS and WHO/ISH risk charts should not be used
interchangeably in risk stratification of individual patients during
follow-ups and the same risk model should be used in serial follow-ups
of individual patients of South Asia and South-East Asian~Region(SEAR).

Furthermore, we observed that the low information risk models, which do
not need cholesterol values in risk estimation (e.g. FRS with BMI-and
WHO/ISH without-cholesterol) were, good in agreement with standard risk
models using cholesterol values (e.g. FRS with-cholesterol and WHO/ISH
with-cholesterol) in risk stratification of Sri Lankans into low and
high-risk groups. This was also observed in a study of South Asians
living in Canada (except in men aged 60-74 years) where they compared
FRS BMI-based and cholesterol-based models \textsuperscript{26}.
Therefore, the low information models can be used in the risk
stratification of patients in poor resource settings where laboratory
facilities are space to measurer cholesterol levels among Sri Lankans
and South Asians. However, the dissimilarities reported need be
interpreted cautiously as FRS predicts the risk of all fatal and
non-fatal CVDs including coronary, cerebrovascular, and peripheral
arterial disease and heart failure while WHO/ISH score predicts only
fatal and non-fatal myocardial infarctions and strokes and therefore the
two tools are not directly comparable.

Ranawaka et al. \textsuperscript{27} studied cardiovascular risk
estimates of a Sri Lankan community, in 2007 and observed 8.2\%
prevalence of high-risk patients using the WHO/ISH model in a cohort of
patients with and without previous CVDs. We studied a cohort of Sri
Lankans without previous CVDs in 2019 and observed 9.5\% of them being
at high-risk the with WHO/ISH(-with cholesterol) model. Therefore, our
findings seem consistent with previous literature. Ranawaka et al.
compared risk predictions of WHO/ISH, National Cholesterol Education
Program - Adult Treatment Panel III (NCEP-ATP III) and Systematic
Coronary Risk~Evaluation (SCORE) models and observed a difference
between the predictions of the three models; 8.2\%, 25.4\%, and 11.8\%
respectively being categorised as high risk. NCEP-ATP III model, which
is a derivative of FRS, predicted 25.4\% of the Sri Lankan being at high
risk, compared to 8.4\% predictions with WHO/ISH model, which was much
higher than the prediction of NCEP-ATP III model. We also observed that
the predictions of FRS were higher than that of WHO/ISH models.

Results of other Asian studies comparing of Framingham score and WHO/ISH
charts also reports the two being different but the literature on best
risk estimates for South Asians is not consistent. Several studies
identified WHO/ISH score underestimating the risk of Asians compared to
FRS \textsuperscript{28-30}. It was reported that The FRS and SCORE-high
models, but not the WHO/ISH model can be used to identify high
cardiovascular risk in Malaysians \textsuperscript{28}.~The same was
reported by a few other Asian studies of Cambodia, Mongolia, Malaysia,
and Jamaica \textsuperscript{31,32}. Few Indian studies reported the FRS
CV risk assessment model has performed the best to identify patients at
high CVD risk while WHO and ASCVD calculators were the
worst~\textsuperscript{29,30,33}. In contrast, Asia Pacific Cohort
Studies Collaboration observed that FRS overestimated CV-risk in Chinese
but was adequately predictive when it was recalibrated using
contemporary data. However, this study did not assess CV-risk with
WHO/ISH r charts\textsuperscript{12}. Further to that, some reported
that the Framingham and British scores underestimate CVD risk in Asian
Indians and the need for developing specific models for them
\textsuperscript{34}.

CV-risk assessment should be based ideally on data of epidemiological
risk factors appropriate to the population to which it is applied
to\textsuperscript{35}. WHO/ISH charts were developed using extrapolated
data on CV risk factors in different geographical regions but have not
been systematically validated prospectively in most populations and
therefore may perform poorly \textsuperscript{5}. Framingham score may
not be reflecting CV-risk of Asians accurately due to several reasons.
Framingham risk score was developed using data of Americans in an era
when the CVD risk was very high in the USA but was less in
Asia\textsuperscript{18}. It does not take into account some important
risk factors relevant to Asians like abdominal obesity, physical
inactivity, and family history of premature CVD, which are currently
increasing in prevalence among Asians. The differential effect of ethnic
groups, environments, and genes on the risk of CVD could also play a
part\textsuperscript{36,37}. Also, Asians are relatively younger when
they develop CVDs compared to white Caucasians \textsuperscript{12,13}
while ``age'' is a very strong risk factor in the Framingham model.
Therefore, FRS may underestimate mate CV-risk of young Asian patients.
Therefore, the best model to risk stratify South Asians is still a
query.

After all, a screening tool should be able to detect all patients at
high-risk without missing high-risk patients while having an acceptable
false positive detection rate. The prevalence of high-risk patients
according to WHO/ISH (SEAR -- B) model is much lower than that with the
FRS model and therefore, maybe a lot of high-risk patients fromSEAR -- B
region are not getting adequate primary prevention measures, due to
being risk-stratified with WHO/ISH (SEAR -- B) model. We cannot be
certain until a new model is developed of a cohort of South Asians or
the existing risk scores are validated in them.

Our study has many strengths. This is a very thorough study with
complete data. We used only the patients who did not have any previous
CVDs. We have calculated risk predictions of a given patient using 4
models and compared them in pairs and therefore there is no selection
bias. However, there are a few limitations of our study as well. This is
not a random sample of Sri Lanka and is of a high-risk population and
therefore, the rates of risk factors prevalence and mean risk scores of
this cohort may not be generalizable Sri Lanka. However, the conclusion
that ``FRS and WHO/ISH charts are only in satisfactory agreement in the
prediction of high-risks Sri Lankans '' is generalizable, as we arrived
at this conclusion by comparing risk scores within each patient,
nullifying any selection bias.

\textbf{Conclusion}

The agreement between CV-risk predictions of Framingham score and
WHO/ISH charts was not satisfactory in Sri Lankans. However, the two
versions (standard and low information models) of the Framingham score
were in good agreement. Similarly, the two versions of the WHO/ISH
charts (standard and low information models) were also in good
agreement.

Therefore, in the absence of a specific or validated risk model for
South Asians, using the same risk model in serial risk calculations of
individual patients would be the best.

\textbf{Acknowledgments}

We acknowledge all the study participants of this study.

Funding - none

References

1. Worldometers. Southern Asia Population. 2020;
https://www.worldometers.info/world-population/southern-asia-population/.
Accessed 30.052020.

2. WHO. Cardiovascular diseases (CVDs). 2017;
https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
Accessed 28.05.2020, 2020.

3. Mendis S, Lindholm LH, Anderson SG, et al. Total cardiovascular risk
approach to improve efficiency of cardiovascular prevention in resource
constrain settings. \emph{Journal of clinical
epidemiology.}2011;64(12):1451-1462.

4. Framingham heart study - cardiovascular-disease-10-year-risk. 2016;
https://framinghamheartstudy.org/fhs-risk-functions/cardiovascular-disease-10-year-risk/.

5. WHO. World Health organization/International Society of Hypertension
risk prediction charts for 14 WHO epidemiological sub-regions In: WHO;
2007. Accessed 16.04.2020.

6. Gopal DP, Usher-Smith JA. Cardiovascular risk models for South Asian
populations: a systematic review. \emph{Int J Public
Health.}2016;61(5):525-534.

7. Volgman Annabelle S, Palaniappan Latha S, Aggarwal Neelum T, et al.
Atherosclerotic Cardiovascular Disease in South Asians in the United
States: Epidemiology, Risk Factors, and Treatments: A Scientific
Statement From the American Heart Association.
\emph{Circulation.}2018;138(1):e1-e34.

8. Cappuccio FP, Oakeshott P, Strazzullo P, Kerry SM. Application of
Framingham risk estimates to ethnic minorities in United Kingdom and
implications for primary prevention of heart disease in general
practice: cross sectional population based study.
\emph{BMJ.}2002;325(7375):1271.

9. Bellary S, O'Hare JP, Raymond NT, et al. Premature cardiovascular
events and mortality in south Asians with type 2 diabetes in the United
Kingdom Asian Diabetes Study - effect of ethnicity on risk. \emph{Curr
Med Res Opin.} 2010;26(8):1873-1879.

10. Enas EA, Garg A, Davidson MA, Nair VM, Huet BA, Yusuf S. Coronary
heart disease and its risk factors in first-generation immigrant Asian
Indians to the United States of America. \emph{Indian Heart
J.}1996;48(4):343-353.

11. Gaziano TA, Bitton A, Anand S, Abrahams-Gessel S, Murphy A. Growing
Epidemic of Coronary Heart Disease in Low- and Middle-Income
Countries.\emph{Current problems in cardiology.} 2010;35(2):72-115.

12. Barzi F, Patel A, Gu D, et al. Cardiovascular risk prediction tools
for populations in Asia. \emph{Journal of epidemiology and community
health.} 2007;61(2):115-121.

13. Ueshima H, Sekikawa A, Miura K, et al. Cardiovascular disease and
risk factors in Asia: a selected review.
\emph{Circulation.}2008;118(25):2702-2709.

14. Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills AP, Misra A.
Prevalence and trends of metabolic syndrome among adults in the
asia-pacific region: a systematic review. \emph{BMC Public
Health.}2017;17(1):101.

15. Pan WH, Yeh WT, Weng LC. Epidemiology of metabolic syndrome in
Asia.\emph{Asia Pacific journal of clinical nutrition.} 2008;17 Suppl
1:37-42.

16. Nanditha A, Ma RCW, Ramachandran A, et al. Diabetes in Asia and the
Pacific: Implications for the Global Epidemic. \emph{Diabetes
Care.}2016;39(3):472-485.

17. Ramachandran A, Snehalatha C, Shetty AS, Nanditha A. Trends in
prevalence of diabetes in Asian countries. \emph{World J
Diabetes.}2012;3(6):110-117.

18. D'Agostino RB, Sr., Vasan RS, Pencina MJ, et al. General
cardiovascular risk profile for use in primary care: the Framingham
Heart Study. \emph{Circulation.} 2008;117(6):743-753.

19. Mendis S, Lindholm LH, Mancia G, et al. World Health Organization
(WHO) and International Society of Hypertension (ISH) risk prediction
charts: assessment of cardiovascular risk for prevention and control of
cardiovascular disease in low and middle-income countries. \emph{J
Hypertens.} 2007;25(8):1578-1582.

20. WHO/IDF. \emph{Definition and diagnosis of diabetes mellitus and
intermediate hyperglycemia : report of a WHO/IDF consultation.} Geneva:
WHO; 2006.

21. Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the
joint national committee on prevention, detection, evaluation, and
treatment of high blood pressure: The JNC 7 report. \emph{Journal of the
American Medical Association} 2003;289(19):2560-2571.

22. Executive Summary of The Third Report of The National Cholesterol
Education Program (NCEP) Expert Panel on Detection, Evaluation, And
Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel
III). \emph{Journal of the American Medical
Association.}2001;285(19):2486-2497.

23. Sim J, Wright CC. The Kappa Statistic in Reliability Studies: Use,
Interpretation, and Sample Size Requirements. \emph{Physical
Therapy.}2005;85(3):257-268.

24. Landis JR, Koch GG. The measurement of observer agreement for
categorical data. \emph{Biometrics.} 1977;33(1):159-174.

25. Altman DG. \emph{Practical statistics for medical research.} London:
Chapman and Hall; 1991.

26. Jones CA, Ross L, Surani N, Dharamshi N, Karmali K. Framingham
ten-year general cardiovascular disease risk: agreement between
BMI-based and cholesterol-based estimates in a South Asian convenience
sample. \emph{PLoS One.} 2015;10(3):e0119183-e0119183.

27. Ranawaka U, Wijekoon N, Pathmeswaran P, et al. Risk estimates of
cardiovascular diseases in a Sri Lankan community. \emph{The Ceylon
medical journal.} 2016;61:11.

28. Selvarajah S, Kaur G, Haniff J, et al. Comparison of the Framingham
Risk Score, SCORE and WHO/ISH cardiovascular risk prediction models in
an Asian population. \emph{International Journal of
Cardiology.}2014;176(1):211-218.

29. Bansal M, Kasliwal RR, Trehan N. Relationship between different
cardiovascular risk scores and measures of subclinical atherosclerosis
in an Indian population. \emph{Indian Heart Journal.}2015;67(4):332-340.

30. Bansal M, Kasliwal RR, Trehan N. Comparative accuracy of different
risk scores in assessing cardiovascular risk in Indians: A study in
patients with first myocardial infarction. \emph{Indian Heart
Journal.}2014;66(6):580-586.

31. Otgontuya D, Oum S, Buckley BS, Bonita R. Assessment of total
cardiovascular risk using WHO/ISH risk prediction charts in three low
and middle income countries in Asia. \emph{BMC Public
Health.}2013;13(1):539.

32. Tulloch-Reid MK, Younger NO, Ferguson TS, et al. Excess
Cardiovascular Risk Burden in Jamaican Women Does Not Influence
Predicted 10-Year CVD Risk Profiles of Jamaica Adults: An Analysis of
the 2007/08 Jamaica Health and Lifestyle Survey. \emph{PLoS
One.}2013;8(6):e66625.

33. Garg NM, Subrat K., Kapoor A, Tewari S, Kumar S, Khanna R, Goel PK.
Comparison of different cardiovascular risk score calculators for
cardiovascular risk prediction and guideline recommended statin
uses.\emph{Indian Heart Journal.} 2017;69(4):458-463.

34. Kanjilal S, Rao VS, Mukherjee M, et al. Application of
cardiovascular disease risk prediction models and the relevance of novel
biomarkers to risk stratification in Asian Indians. \emph{Vasc Health
Risk Manag.} 2008;4(1):199-211.

35. Smith Sidney C, Jackson R, Pearson Thomas A, et al. Principles for
National and Regional Guidelines on Cardiovascular Disease
Prevention.\emph{Circulation.} 2004;109(25):3112-3121.

36. D'Agostino RB, Sr, Grundy S, Sullivan LM, Wilson P, for the CHDRPG.
Validation of the framingham coronary heart disease prediction scores:
Results of a multiple ethnic groups investigation. \emph{Journal of the
American Medical Association.} 2001;286(2):180-187.

37. Marrugat J, D'Agostino R, Sullivan L, et al. An adaptation of the
Framingham coronary heart disease risk function to European
Mediterranean areas. \emph{Journal of epidemiology and community
health.} 2003;57(8):634-638.

\textbf{Tables}

Table~1~Baseline characteristics of the study participants\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Male\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Female\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Total\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
~P*\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
n=32\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
n=137\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
n=169\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age, y, mean (SD)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
68.06(4.79)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
63.68(7.03)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
65 \selectlanguage{ngerman}± 6.9\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Age [?] 60 y,~~n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
30(93.8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
94(68.6)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
124(73.4)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.003\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Level of education}+, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.059\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Up to grade 5\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0(0.0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6(5.1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6 (4.1)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Up to grade 8\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
10(33.3)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
55(46.6)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
65 (43.9)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Up to grade 10\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
12(40.0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
47(39.8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
59 (39.9)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Up to grade 12\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
6(20.0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
8(6.8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
14 (9.5)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Graduate/ postgraduate\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2 (6.7)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2(1.7)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
4 (2.7)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Ethnicity}, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Ethnicity}, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Sinhalese\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
32(100)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
135(98.8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
167(98.8)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Tamils\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
0 (0.0)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2(1.2)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
2(1.2)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Past medical history}, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Past medical history}, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Past medical history}, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Past medical history}, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Past medical history}, n (\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Hypertension\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
25(78.1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
88(64.2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
113(66.9)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.150\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Hyperlipidemia\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
25(78.1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
127(92.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
152(89.9)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.220\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Diabetes mellitus\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
20(62.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
59(43.1)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
79(46.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.520\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Current smoking\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4(12.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0 (0.0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
4(2.4)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Obesity (BMI\textgreater{}30)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2(6.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
12(8.8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
14(8.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Premorbid medications,} n (\%) :\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Premorbid medications,} n (\%) :\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Premorbid medications,} n (\%) :\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Premorbid medications,} n (\%) :\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textbf{Premorbid medications,} n (\%) :\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Anti-hypertensive medications\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
~24(75.0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
87(63.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
111(65.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.301\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Lipid-lowering medications ++\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
24(77.4)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
127(92.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
151(89.9)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Anti-diabetic medication\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
18(56.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
60(43.8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
78(46.2)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.239\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Antiplatelet medication \selectlanguage{ngerman}§\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3(10.0)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
23(16.8)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26(15.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.577\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

* p-value between male and female

Missing data; ~+~21,~++~1,~\selectlanguage{ngerman}§~1

Table~2\textbf{~CV-risk factors used in risk calculations and mean
Framingham risk predictions by sex}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Mean(SD) or n(\%) & Male & Female & p\tabularnewline
\midrule
\endhead
Age (years), mean(SD) & 68.06(4.79) & 63.68(7.03) &
\textless{}0.0001\tabularnewline
Diabetes mellitus, n (\%) & 20(62.5) & 59(43.1) & 0.520\tabularnewline
Current smoking, n (\%) & 4(12.5) & 0 (0.0) &
\textless{}0.0001\tabularnewline
On~antihypertensive medications, n (\%) & 24(75.0) & 87(63.5) &
0.301\tabularnewline
BMI (kg/m\textsuperscript{2}), mean(SD) & 24.71(3.85) & 24.86(3.80) &
0.85\tabularnewline
Systolic~blood pressure (mmHg), mean(SD) & 136.38(22.92) & 131.91(18.81)
& 0.25\tabularnewline
Total cholesterol (mg/dl), mean(SD) & 174.66(27.41) & 183.79(33.04) &
0.15\tabularnewline
High density lipoprotein (mg/dl), mean(SD) & 48.58(9.58) & 53.07(16.64)
& 0.145\tabularnewline
\textbf{FRS (BMI-based), mean(SD)} & 28.94(3.17) & 17.10(8.62) &
\textless{}0.0001\tabularnewline
\textbf{FRS (cholesterol-based), mean(SD)} & 26.47(4.99) & 13.86(8.25) &
\textless{}0.0001\tabularnewline
\bottomrule
\end{longtable}

FRS=Framingham risk score

Table~3 ~\textbf{10-year CV-Risk stratification of the sample with
different risk models and inter-rater agreement}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
10-year CV risk\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
10-year CV risk\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Agreement\strut
\end{minipage} & \begin{minipage}[b]{0.17\columnwidth}\raggedright\strut
Agreement\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Low risk (risk \textless{}20\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
High risk (risk [?] 20\%)\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
\selectlanguage{greek}\emph{κ} \selectlanguage{english}\textsuperscript{a}\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
P \textsuperscript{b}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Framingham (BMI-based),~n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
89(52.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
80(47.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.736\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Framingham (cholesterol-based), n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
107(63.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
62(36.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
WHO/ISH~(without-cholesterol), n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
151(89.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
18(10.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.804\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
WHO/ISH~(with-cholesterol),~~n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
153(90.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
16(9.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Framingham (BMI-based),~n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
89(52.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
80(47.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.234\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
WHO/ISH~(without-cholesterol), n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
151(89.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
18(10.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Framingham (cholesterol-based), n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
107(63.3)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
62(36.7)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.306\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\textless{}0.0001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
WHO/ISH~(with-cholesterol),~~n(\%)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
153(90.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
16(9.5)\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Cohen's kappa coefficient~(\selectlanguage{greek}\emph{κ} \selectlanguage{english})

\textbf{Hosted file}

\verb`Tables_IJCP.docx`  available at \url{https://authorea.com/users/337028/articles/462693-place-of-cardiovascular-risk-prediction-models-in-south-asians-agreement-between-framingham-risk-score-and-who-ish-risk-charts}

\selectlanguage{english}
\FloatBarrier
\end{document}

