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Current Diabetes Reviews

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ISSN (Print): 1573-3998
ISSN (Online): 1875-6417

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Review Article

Cardiovascular Risk Calculators and their Applicability to South Asians

Author(s): Manish Bansal*, Shraddha Ranjan and Ravi R. Kasliwal

Volume 17, Issue 9, 2021

Published on: 01 October, 2020

Article ID: e100120186497 Pages: 17

DOI: 10.2174/1573399816999201001204020

Price: $65

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Abstract

Background: Estimation of absolute cardiovascular disease (CVD) risk and tailoring therapies according to the estimated risk is a fundamental concept in the primary prevention of CVD is assessed in this study. Numerous CVD risk scores are currently available for use in various populations but unfortunately, none exist for South Asians who have much higher CVD risk as compared to their western counterparts.

Methods: A literature search was done using PubMed and Google search engines to prepare a narrative review on this topic.

Results: Various currently available CVD risk scores and their pros and cons are summarized. The studies performed in native as well as migrant South Asians evaluating the accuracy of these risk scores for estimation of CVD risk are also summarized. The findings of these studies have generally been inconsistent, but it appears that the British risk scores (e.g. QRISK versions) may be more accurate because of inclusion of migrant South Asians in the derivation of these risk scores. However, the lack of any prospective study precludes our ability to draw any firm conclusions. Finally, the potential solution to these challenges, including the role of recalibration and subclinical atherosclerosis imaging, is also discussed.

Conclusion: This review highlights the need to develop large, representative, prospectively followed databases of South Asians providing information on various CVD risk factors and their contribution to incident CVD. Such databases will not only allow the development of validated CVD risk scores for South Asians but will also enable application of machine-learning approaches to provide personalized solutions to CVD risk assessment and management in these populations.

Keywords: Atherosclerotic cardiovascular disease, primary prevention, secondary prevention, cardiovascular risk, lifetime risk, subclinical atherosclerosis.

[1]
Lowensteyn I, Joseph L, Levinton C, Abrahamowicz M, Steinert Y, Grover S. Can computerized risk profiles help patients improve their coronary risk? The results of the Coronary Health Assessment Study (CHAS). Prev Med 1998; 27(5 Pt 1): 730-7.
[http://dx.doi.org/10.1006/pmed.1998.0351] [PMID: 9808805]
[2]
Karmali KN, Brown T, Sanchez T, Long T, Persell SD. Point-of- care testing to promote cardiovascular disease risk assessment: A proof of concept study. Prev Med Rep 2017; 7: 136-9.
[http://dx.doi.org/10.1016/j.pmedr.2017.05.016] [PMID: 28660121]
[3]
Karmali KN, Persell SD, Perel P, Lloyd-Jones DM, Berendsen MA, Huffman MD. Risk scoring for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev 2017; 3: CD006887.
[http://dx.doi.org/10.1002/14651858.CD006887.pub4] [PMID: 28290160]
[4]
Hall LM, Jung RT, Leese GP. Controlled trial of effect of documented cardiovascular risk scores on prescribing. BMJ 2003; 326(7383): 251-2.
[http://dx.doi.org/10.1136/bmj.326.7383.251] [PMID: 12560273]
[5]
Montgomery AA, Fahey T, Peters TJ, MacIntosh C, Sharp DJ. Evaluation of computer based clinical decision support system and risk chart for management of hypertension in primary care: randomised controlled trial. BMJ 2000; 320(7236): 686-90.
[http://dx.doi.org/10.1136/bmj.320.7236.686] [PMID: 10710578]
[6]
Baigent C, Blackwell L, Emberson J, et al. Cholesterol Treatment Trialists’ (CTT) Collaboration. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet 2010; 376(9753): 1670-81.
[http://dx.doi.org/10.1016/S0140-6736(10)61350-5] [PMID: 21067804]
[7]
Williams B, Mancia G, Spiering W, et al. ESC Scientific Document Group. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur Heart J 2018; 39(33): 3021-104.
[http://dx.doi.org/10.1093/eurheartj/ehy339] [PMID: 30165516]
[8]
Bibbins-Domingo K. Force USPST. U.S. Preventive services task force. aspirin use for the primary prevention of cardiovascular disease and colorectal cancer: U.S. preventive services task force recommendation statement. Ann Intern Med 2016; 164(12): 836-45.
[http://dx.doi.org/10.7326/M16-0577] [PMID: 27064677]
[9]
Ying A, Arima H, Czernichow S, et al. Blood Pressure Lowering Treatment Trialists’ Collaboration. Effects of blood pressure lowering on cardiovascular risk according to baseline body-mass index: a meta-analysis of randomised trials. Lancet 2015; 385(9971): 867-74.
[http://dx.doi.org/10.1016/S0140-6736(14)61171-5] [PMID: 25468168]
[10]
Arnett DK, Blumenthal RS, Albert MA, et al. 2019 ACC/AHA Guideline on the primary prevention of cardiovascular disease: a report of the american college of cardiology/american heart association task force on clinical practice guidelines. J Am Coll Cardiol 2019; 74(10): e177-232.
[http://dx.doi.org/10.1016/j.jacc.2019.03.010] [PMID: 30894318]
[11]
Grundy SM, Stone NJ, Bailey AL, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the management of blood cholesterol: a report of the american college of cardiology/american heart association task force on clinical practice guidelines. J Am Coll Cardiol 2019; 73(24): e285-350.
[http://dx.doi.org/10.1016/j.jacc.2018.11.003] [PMID: 30423393]
[12]
Mach F, Baigent C, Catapano AL, et al. ESC Scientific Document Group. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J 2020; 41(1): 111-88.
[http://dx.doi.org/10.1093/eurheartj/ehz455] [PMID: 31504418]
[13]
Piepoli MF, Hoes AW, Agewall S, et al. ESC Scientific Document Group. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts)Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur Heart J 2016; 37(29): 2315-81.
[http://dx.doi.org/10.1093/eurheartj/ehw106] [PMID: 27222591]
[14]
Stamler J, Vaccaro O, Neaton JD, Wentworth D. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care 1993; 16(2): 434-44.
[http://dx.doi.org/10.2337/diacare.16.2.434] [PMID: 8432214]
[15]
Neaton JD, Blackburn H, Jacobs D, et al. Multiple Risk Factor intervention trial research group. serum cholesterol level and mortality findings for men screened in the multiple risk factor intervention trial. Arch Intern Med 1992; 152(7): 1490-500.
[http://dx.doi.org/10.1001/archinte.1992.00400190110021] [PMID: 1627030]
[16]
Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J 1991; 121(1 Pt 2): 293-8.
[http://dx.doi.org/10.1016/0002-8703(91)90861-B] [PMID: 1985385]
[17]
Beswick AD, Brindle P, Fahey T, Ebrahim S. A systematic review of risk scoring methods and clinical decision aids used in the primary prevention of coronary heart disease (supplement). national institute for health and clinical excellence: guidance london
[18]
McGorrian C, Yusuf S, Islam S, et al. INTERHEART Investigators. Estimating modifiable coronary heart disease risk in multiple regions of the world: the INTERHEART Modifiable Risk Score. Eur Heart J 2011; 32(5): 581-9.
[http://dx.doi.org/10.1093/eurheartj/ehq448] [PMID: 21177699]
[19]
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998; 97(18): 1837-47.
[http://dx.doi.org/10.1161/01.CIR.97.18.1837] [PMID: 9603539]
[20]
National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). 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) final report. Circulation 2002; 106(25): 3143-421.
[http://dx.doi.org/10.1161/circ.106.25.3143] [PMID: 12485966]
[21]
D’Agostino RB Sr, Vasan RS, Pencina MJ, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008; 117(6): 743-53.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.107.699579] [PMID: 18212285]
[22]
Chia YC. Review of tools of cardiovascular disease risk stratification: interpretation, customisation and application in clinical practice. Singapore Med J 2011; 52(2): 116-23.
[PMID: 21373738]
[23]
Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. American college of cardiology/american heart association task force on practice guidelines. 2013 acc/aha guideline on the assessment of cardiovascular risk: a report of the american college of cardiology/american heart association task force on practice guidelines. Circulation 2014; 129(25)(Suppl. 2): S49-73.
[http://dx.doi.org/10.1161/01.cir.0000437741.48606.98] [PMID: 24222018]
[24]
Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham Study. Ann N Y Acad Sci 1963; 107: 539-56.
[http://dx.doi.org/10.1111/j.1749-6632.1963.tb13299.x] [PMID: 14025561]
[25]
Kannel WB, Feinleib M, McNamara PM, Garrison RJ, Castelli WP. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol 1979; 110(3): 281-90.
[http://dx.doi.org/10.1093/oxfordjournals.aje.a112813] [PMID: 474565]
[26]
The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol 1989; 129(4): 687-702.
[http://dx.doi.org/10.1093/oxfordjournals.aje.a115184] [PMID: 2646917]
[27]
Fried LP, Borhani NO, Enright P, et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol 1991; 1(3): 263-76.
[http://dx.doi.org/10.1016/1047-2797(91)90005-W] [PMID: 1669507]
[28]
Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol 1988; 41(11): 1105-16.
[http://dx.doi.org/10.1016/0895-4356(88)90080-7] [PMID: 3204420]
[29]
10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes-2020. Diabetes Care 2020; 43(Suppl. 1): S111-34.
[http://dx.doi.org/10.2337/dc20-S010] [PMID: 31862753]
[30]
Whelton PK, Carey RM, Aronow WS, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the american college of cardiology/american heart association task force on clinical practice guidelines. J Am Coll Cardiol 2018; 71(19): e127-248.
[http://dx.doi.org/10.1016/j.jacc.2017.11.006] [PMID: 29146535]
[31]
Lloyd-Jones DM, Braun LT, Ndumele CE, et al. Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the american heart association and american college of cardiology. J Am Coll Cardiol 2019; 73(24): 3153-67.
[http://dx.doi.org/10.1016/j.jacc.2018.11.005] [PMID: 30423392]
[32]
Conroy RM, Pyörälä K, Fitzgerald AP, et al. SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 2003; 24(11): 987-1003.
[http://dx.doi.org/10.1016/S0195-668X(03)00114-3] [PMID: 12788299]
[33]
Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 2007; 335(7611): 136.
[http://dx.doi.org/10.1136/bmj.39261.471806.55] [PMID: 17615182]
[34]
Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ 2008; 336(7659): 1475-82.
[http://dx.doi.org/10.1136/bmj.39609.449676.25] [PMID: 18573856]
[35]
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ 2017; 357: j2099.
[http://dx.doi.org/10.1136/bmj.j2099] [PMID: 28536104]
[36]
Hippisley-Cox J, Coupland C, Robson J, Brindle P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ 2010; 341: c6624.
[http://dx.doi.org/10.1136/bmj.c6624] [PMID: 21148212]
[37]
Hippisley-Cox J, Coupland C, Brindle P. The performance of seven QPrediction risk scores in an independent external sample of patients from general practice: a validation study. BMJ Open 2014; 4(8): e005809.
[http://dx.doi.org/10.1136/bmjopen-2014-005809] [PMID: 25168040]
[38]
Pike MM, Decker PA, Larson NB, et al. Improvement in cardiovascular risk prediction with electronic health records. J Cardiovasc Transl Res 2016; 9(3): 214-22.
[http://dx.doi.org/10.1007/s12265-016-9687-z] [PMID: 26960568]
[39]
Cardiovascular risk assessment and lipid modification. The national institute for health and care excellence https://www.nice.org.uk/guidance/qs100/chapter/Quality-statement- 1-Full-formal-risk-assessment-using-QRISK22020.
[40]
Putting Prevention First. NHS health check: vascular risk assessment and management. https://www.theisn.org/images/taskforce/United%20Kingdom/Putting%20Prevention%20First_NHS%20Health%20Check_Vascular%20Risk%20Assessments%20and%20Management%20Best%20Practice.pdf2020.
[41]
JBS3 Board. Joint British Societies’ consensus recommendations for the prevention of cardiovascular disease (JBS3). Heart 2014; 100(Suppl. 2): ii1-ii67.
[http://dx.doi.org/10.1136/heartjnl-2014-305693] [PMID: 24667225]
[42]
WHO/ISH cardiovascular risk prediction charts. https://www.who.int/cardiovascular_diseases/guidelines/Chart_predictions/en/2020.
[43]
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. Indian Heart J 2014; 66(6): 580-6.
[http://dx.doi.org/10.1016/j.ihj.2014.10.399] [PMID: 25634388]
[44]
Bansal M, Kasliwal RR, Trehan N. Relationship between different cardiovascular risk scores and measures of subclinical atherosclerosis in an Indian population. Indian Heart J 2015; 67(4): 332-40.
[http://dx.doi.org/10.1016/j.ihj.2015.04.017] [PMID: 26304565]
[45]
Garg N, Muduli SK, Kapoor A, et al. Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline recommended statin uses. Indian Heart J 2017; 69(4): 458-63.
[http://dx.doi.org/10.1016/j.ihj.2017.01.015] [PMID: 28822511]
[46]
Ranawaka UK, Wijekoon CN, Pathmeswaran A, et al. Risk estimates of cardiovascular diseases in a Sri Lankan community. Ceylon Med J 2016; 61(1): 11-7.
[http://dx.doi.org/10.4038/cmj.v61i1.8253] [PMID: 27031973]
[47]
Yusuf S, Hawken S, Ounpuu S, et al. INTERHEART Study Investigators. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet 2004; 364(9438): 937-52.
[http://dx.doi.org/10.1016/S0140-6736(04)17018-9] [PMID: 15364185]
[48]
Yusuf S, Rangarajan S, Teo K, et al. PURE Investigators. Cardiovascular risk and events in 17 low-, middle-, and high-income countries. N Engl J Med 2014; 371(9): 818-27.
[http://dx.doi.org/10.1056/NEJMoa1311890] [PMID: 25162888]
[49]
Joseph P, Yusuf S, Lee SF, et al. PURE INVESTIGATORS. Prognostic validation of a non-laboratory and a laboratory based cardiovascular disease risk score in multiple regions of the world. Heart 2018; 104(7): 581-7.
[http://dx.doi.org/10.1136/heartjnl-2017-311609] [PMID: 29066611]
[50]
Hajifathalian K, Ueda P, Lu Y, et al. A novel risk score to predict cardiovascular disease risk in national populations (Globorisk): a pooled analysis of prospective cohorts and health examination surveys. Lancet Diabetes Endocrinol 2015; 3(5): 339-55.
[http://dx.doi.org/10.1016/S2213-8587(15)00081-9] [PMID: 25819778]
[51]
Ueda P, Woodward M, Lu Y, et al. Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys. Lancet Diabetes Endocrinol 2017; 5(3): 196-213.
[http://dx.doi.org/10.1016/S2213-8587(17)30015-3] [PMID: 28126460]
[52]
McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary heart disease risk prediction using coronary artery calcium and traditional risk factors: derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) with validation in the HNR (Heinz Nixdorf Recall) study and the DHS (Dallas Heart Study). J Am Coll Cardiol 2015; 66(15): 1643-53.
[http://dx.doi.org/10.1016/j.jacc.2015.08.035] [PMID: 26449133]
[53]
Greenland P, Blaha MJ, Budoff MJ, Erbel R, Watson KE. Coronary calcium score and cardiovascular risk. J Am Coll Cardiol 2018; 72(4): 434-47.
[http://dx.doi.org/10.1016/j.jacc.2018.05.027] [PMID: 30025580]
[54]
Nasir K, Bittencourt MS, Blaha MJ, et al. Implications of coronary artery calcium testing among statin candidates according to american college of cardiology/american heart association cholesterol management guidelines: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol 2015; 66(15): 1657-68.
[http://dx.doi.org/10.1016/j.jacc.2015.07.066] [PMID: 26449135]
[55]
Mahabadi AA, Möhlenkamp S, Lehmann N, et al. Heinz Nixdorf recall study investigators. cac score improves coronary and cv risk assessment above statin indication by esc and aha/acc primary prevention guidelines. JACC Cardiovasc Imaging 2017; 10(2): 143-53.
[http://dx.doi.org/10.1016/j.jcmg.2016.03.022] [PMID: 27665163]
[56]
Miedema MD, Duprez DA, Misialek JR, et al. Use of coronary artery calcium testing to guide aspirin utilization for primary prevention: estimates from the multi-ethnic study of atherosclerosis. Circ Cardiovasc Qual Outcomes 2014; 7(3): 453-60.
[http://dx.doi.org/10.1161/CIRCOUTCOMES.113.000690] [PMID: 24803472]
[57]
Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007; 297(6): 611-9.
[http://dx.doi.org/10.1001/jama.297.6.611] [PMID: 17299196]
[58]
Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation 2008; 118(22): 2243-51.
[59]
Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Münster (PROCAM) study. Circulation 2002; 105(3): 310-5.
[http://dx.doi.org/10.1161/hc0302.102575] [PMID: 11804985]
[60]
Assmann G, Schulte H, Cullen P, Seedorf U. Assessing risk of myocardial infarction and stroke: new data from the Prospective Cardiovascular Münster (PROCAM) study. Eur J Clin Invest 2007; 37(12): 925-32.
[http://dx.doi.org/10.1111/j.1365-2362.2007.01888.x] [PMID: 18036028]
[61]
Woodward M, Brindle P, Tunstall-Pedoe H. SIGN group on risk estimation. Adding social deprivation and family history to cardiovascular risk assessment: the ASSIGN score from the Scottish Heart Health Extended Cohort (SHHEC). Heart 2007; 93(2): 172-6.
[http://dx.doi.org/10.1136/hrt.2006.108167] [PMID: 17090561]
[62]
Yang X, Li J, Hu D, et al. Predicting the 10-year risks of atherosclerotic cardiovascular disease in Chinese population: The China- PAR Project (Prediction for ASCVD Risk in China). Circulation 2016; 134(19): 1430-40.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.116.022367] [PMID: 27682885]
[63]
Pylypchuk R, Wells S, Kerr A, et al. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet 2018; 391(10133): 1897-907.
[http://dx.doi.org/10.1016/S0140-6736(18)30664-0] [PMID: 29735391]
[64]
Khera A, Budoff MJ, O’Donnell CJ, et al. Astronaut cardiovascular health and risk modification (astro-charm) coronary calcium atherosclerotic cardiovascular disease risk calculator. Circulation 2018; 138(17): 1819-27.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.118.033505] [PMID: 30354651]
[65]
van Dieren S, Beulens JW, Kengne AP, et al. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart 2012; 98(5): 360-9.
[http://dx.doi.org/10.1136/heartjnl-2011-300734] [PMID: 22184101]
[66]
Berry JD, Dyer A, Cai X, et al. Lifetime risks of cardiovascular disease. N Engl J Med 2012; 366(4): 321-9.
[http://dx.doi.org/10.1056/NEJMoa1012848] [PMID: 22276822]
[67]
Elward KS, Simpson RJ Jr, Mendys P. Improving cardiovascular risk reduction for primary prevention-utility of lifetime risk assessment. Postgrad Med 2010; 122(4): 192-9.
[http://dx.doi.org/10.3810/pgm.2010.07.2186] [PMID: 20675982]
[68]
Berry JD, Liu K, Folsom AR, et al. Prevalence and progression of subclinical atherosclerosis in younger adults with low short-term but high lifetime estimated risk for cardiovascular disease: the coronary artery risk development in young adults study and multi-ethnic study of atherosclerosis. Circulation 2009; 119(3): 382-9.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.108.800235] [PMID: 19139385]
[69]
Lloyd-Jones DM, Leip EP, Larson MG, et al. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation 2006; 113(6): 791-8.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.105.548206] [PMID: 16461820]
[70]
Marma AK, Berry JD, Ning H, Persell SD, Lloyd-Jones DM. Distribution of 10-year and lifetime predicted risks for cardiovascular disease in US adults: findings from the National Health and Nutrition Examination Survey 2003 to 2006. Circ Cardiovasc Qual Outcomes 2010; 3(1): 8-14.
[http://dx.doi.org/10.1161/CIRCOUTCOMES.109.869727] [PMID: 20123666]
[71]
Bansal M, Shrivastava S, Mehrotra R, Agarwal V, Kasliwal RR. Low Framingham risk score despite high prevalence of metabolic syndrome in asymptomatic North-Indian population. J Assoc Physicians India 2009; 57: 17-22.
[PMID: 19753753]
[72]
Jeemon P, Prabhakaran D, Huffman MD, et al. Sentinel Surveillance in Industrial Populations Study Group. Distribution of 10-year and lifetime predicted risk for cardiovascular disease in the Indian Sentinel Surveillance Study population (cross-sectional survey results). BMJ Open 2011; 1(1): e000068.
[http://dx.doi.org/10.1136/bmjopen-2011-000068] [PMID: 22021747]
[73]
Ference BA, Yoo W, Alesh I, et al. Effect of long-term exposure to lower low-density lipoprotein cholesterol beginning early in life on the risk of coronary heart disease: a Mendelian randomization analysis. J Am Coll Cardiol 2012; 60(25): 2631-9.
[http://dx.doi.org/10.1016/j.jacc.2012.09.017] [PMID: 23083789]
[74]
An International Atherosclerosis Society Position Paper: Global Recommendations for the Management of Dyslipidemia www.athero.org/download/IASPPGuidelines_FullReport_20131011.pdf
[75]
Iyengar SS, Puri R, Narasingan SN, et al. Lipid association of India expert consensus statement on management of dyslipidemia in Indians 2016: Part 1. J Assoc Physicians India 2016; 64(3)(Suppl.): 7-52.
[PMID: 28762263]
[76]
Chandra KS, Bansal M, Nair T, et al. Consensus statement on management of dyslipidemia in Indian subjects. Indian Heart J 2014; 66(Suppl. 3): S1-S51.
[http://dx.doi.org/10.1016/j.ihj.2014.12.001] [PMID: 25595144]
[77]
Joshi P, Islam S, Pais P, et al. Risk factors for early myocardial infarction in South Asians compared with individuals in other countries. JAMA 2007; 297(3): 286-94.
[http://dx.doi.org/10.1001/jama.297.3.286] [PMID: 17227980]
[78]
Hariram V. Assessment of cardiovascular risk in low resource settings “so much to do so little done”. Indian Heart J 2015; 68(3): 260-2.
[http://dx.doi.org/10.1016/j.ihj.2015.07.046] [PMID: 27316475]
[79]
Hense HW, Schulte H, Löwel H, Assmann G, Keil U. Framingham risk function overestimates risk of coronary heart disease in men and women from Germany-results from the MONICA Augsburg and the PROCAM cohorts. Eur Heart J 2003; 24(10): 937-45.
[http://dx.doi.org/10.1016/S0195-668X(03)00081-2] [PMID: 12714025]
[80]
Barzi F, Patel A, Gu D, et al. Asia Pacific cohort studies collaboration. cardiovascular risk prediction tools for populations in Asia. J Epidemiol community health 2007; 61(2): 115-21.
[http://dx.doi.org/10.1136/jech.2005.044842] [PMID: 17234869]
[81]
Thomsen TF, McGee D, Davidsen M, Jørgensen T. A cross-validation of risk-scores for coronary heart disease mortality based on data from the Glostrup Population Studies and Framingham Heart Study. Int J Epidemiol 2002; 31(4): 817-22.
[http://dx.doi.org/10.1093/ije/31.4.817] [PMID: 12177028]
[82]
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. BMJ 2002; 325(7375): 1271.
[http://dx.doi.org/10.1136/bmj.325.7375.1271] [PMID: 12458243]
[83]
Brindle PM, McConnachie A, Upton MN, Hart CL, Davey Smith G, Watt GC. The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective study. Br J Gen Pract 2005; 55(520): 838-45.
[PMID: 16281999]
[84]
Empana JP, Ducimetière P, Arveiler D, et al. PRIME Study Group. Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study. Eur Heart J 2003; 24(21): 1903-11.
[http://dx.doi.org/10.1016/j.ehj.2003.09.002] [PMID: 14585248]
[85]
de Ruijter W, Westendorp RG, Assendelft WJ, et al. Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ 2009; 338: a3083.
[http://dx.doi.org/10.1136/bmj.a3083] [PMID: 19131384]
[86]
Scheltens T, Verschuren WM, Boshuizen HC, et al. Estimation of cardiovascular risk: a comparison between the Framingham and the SCORE model in people under 60 years of age. Eur J Cardiovasc Prev Rehabil 2008; 15(5): 562-6.
[http://dx.doi.org/10.1097/HJR.0b013e3283063a65] [PMID: 18756178]
[87]
Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review. Heart 2006; 92(12): 1752-9.
[http://dx.doi.org/10.1136/hrt.2006.087932] [PMID: 16621883]
[88]
Chia YC, Lim HM, Ching SM. Validation of the pooled cohort risk score in an Asian population - a retrospective cohort study. BMC Cardiovasc Disord 2014; 14: 163.
[http://dx.doi.org/10.1186/1471-2261-14-163] [PMID: 25410585]
[89]
DeFilippis AP, Young R, Carrubba CJ, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann Intern Med 2015; 162(4): 266-75.
[http://dx.doi.org/10.7326/M14-1281] [PMID: 25686167]
[90]
Jung KJ, Jang Y, Oh DJ, et al. The ACC/AHA 2013 pooled cohort equations compared to a Korean Risk Prediction Model for atherosclerotic cardiovascular disease. Atherosclerosis 2015; 242(1): 367-75.
[http://dx.doi.org/10.1016/j.atherosclerosis.2015.07.033] [PMID: 26255683]
[91]
Lee CH, Woo YC, Lam JK, Fong CH, Cheung BM, Lam KS, et al. Validation of the Pooled Cohort equations in a long-term cohort study of Hong Kong Chinese. J Clin Lipidol 2015; 9(5): 640-.
[92]
Volgman AS, Palaniappan LS, Aggarwal NT, et al. American heart association council on epidemiology and prevention; cardiovascular disease and stroke in women and special populations committee of the council on clinical cardiology; council on cardiovascular and stroke nursing; council on quality of care and outcomes research; and stroke council. atherosclerotic cardiovascular disease in south asians in the united states: epidemiology, risk factors, and treatments: a scientific statement from the american heart association. Circulation 2018; 138(1): e1-e34.
[http://dx.doi.org/10.1161/CIR.0000000000000580] [PMID: 29794080]
[93]
Allan GM, Nouri F, Korownyk C, Kolber MR, Vandermeer B, McCormack J. Agreement among cardiovascular disease risk calculators. Circulation 2013; 127(19): 1948-56.
[http://dx.doi.org/10.1161/CIRCULATIONAHA.112.000412] [PMID: 23575355]
[94]
Krishnan S. South Asians and Cardiovascular Disease: The Hidden Threat. https://www.acc.org/latest-in-cardiology/articles/2019/05/07/12/42/cover-story-south-asians-and-cardiovascular-disease-the-hidden-threat2020.
[95]
Gijsberts CM, Seneviratna A, de Carvalho LP, et al. Ethnicity modifies associations between cardiovascular risk factors and disease severity in parallel dutch and singapore coronary cohorts. PLoS One 2015; 10(7): e0132278.
[http://dx.doi.org/10.1371/journal.pone.0132278] [PMID: 26147693]
[96]
Bhopal R, Unwin N, White M, et al. Heterogeneity of coronary heart disease risk factors in Indian, Pakistani, Bangladeshi, and European origin populations: cross sectional study. BMJ 1999; 319(7204): 215-20.
[http://dx.doi.org/10.1136/bmj.319.7204.215] [PMID: 10417082]
[97]
Zahid N, Meyer HE, Kumar BN, Claussen B, Hussain A. High levels of cardiovascular risk factors among Pakistanis in Norway compared to Pakistanis in Pakistan. J Obes 2011; 2011: 163749.
[http://dx.doi.org/10.1155/2011/163749] [PMID: 21773000]
[98]
Danaraj TJ, Acker MS, Danaraj W, Wong HO, Tan BY. Ethnic group differences in coronary heart disease in Singapore: an analysis of necropsy records. Am Heart J 1959; 58: 516-26.
[http://dx.doi.org/10.1016/0002-8703(59)90085-7] [PMID: 13813926]
[99]
Hughes K, Lun KC, Yeo PP. Cardiovascular diseases in Chinese, Malays, and Indians in Singapore. I. Differences in mortality. J Epidemiol Community Health 1990; 44(1): 24-8.
[http://dx.doi.org/10.1136/jech.44.1.24] [PMID: 2348144]
[100]
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. Indian Heart J 1996; 48(4): 343-53.
[PMID: 8908818]
[101]
Bansal N, Fischbacher CM, Bhopal RS, Brown H, Steiner MF, Capewell S. Scottish Health and Ethnicity Linkage Study. Myocardial infarction incidence and survival by ethnic group: Scottish Health and Ethnicity Linkage retrospective cohort study. BMJ Open 2013; 3(9): e003415.
[http://dx.doi.org/10.1136/bmjopen-2013-003415] [PMID: 24038009]
[102]
Hajra A, Li Y, Siu S, et al. Risk of coronary disease in the South Asian American population. J Am Coll Cardiol 2013; 62(7): 644-5.
[http://dx.doi.org/10.1016/j.jacc.2013.05.048] [PMID: 23770164]
[103]
Rafnsson SB, Bhopal RS, Agyemang C, et al. Sizable variations in circulatory disease mortality by region and country of birth in six European countries. Eur J Public Health 2013; 23(4): 594-605.
[http://dx.doi.org/10.1093/eurpub/ckt023] [PMID: 23478207]
[104]
Palaniappan L, Wang Y, Fortmann SP. Coronary heart disease mortality for six ethnic groups in California, 1990-2000. Ann Epidemiol 2004; 14(7): 499-506.
[http://dx.doi.org/10.1016/j.annepidem.2003.12.001] [PMID: 15310526]
[105]
Anand SS, Yusuf S, Vuksan V, et al. Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE). Lancet 2000; 356(9226): 279-84.
[http://dx.doi.org/10.1016/S0140-6736(00)02502-2] [PMID: 11071182]
[106]
Sheth T, Nair C, Nargundkar M, Anand S, Yusuf S. Cardiovascular and cancer mortality among Canadians of European, south Asian and Chinese origin from 1979 to 1993: an analysis of 1.2 million deaths. CMAJ 1999; 161(2): 132-8.
[PMID: 10439820]
[107]
McKeigue PM, Miller GJ, Marmot MG. Coronary heart disease in south Asians overseas: a review. J Clin Epidemiol 1989; 42(7): 597-609.
[http://dx.doi.org/10.1016/0895-4356(89)90002-4] [PMID: 2668448]
[108]
Hayman LL, Helden L, Chyun DA, Braun LT. A life course approach to cardiovascular disease prevention. J Cardiovasc Nurs 2011; 26(4)(Suppl.): S22-34.
[http://dx.doi.org/10.1097/JCN.0b013e318213ef7f] [PMID: 21659809]
[109]
Bhopal RS. A four-stage model explaining the higher risk of Type 2 diabetes mellitus in South Asians compared with European populations. Diabet Med 2013; 30(1): 35-42.
[http://dx.doi.org/10.1111/dme.12016] [PMID: 22998210]
[110]
Stein CE, Fall CH, Kumaran K, Osmond C, Cox V, Barker DJ. Fetal growth and coronary heart disease in south India. Lancet 1996; 348(9037): 1269-73.
[http://dx.doi.org/10.1016/S0140-6736(96)04547-3] [PMID: 8909379]
[111]
Fall CH, Sachdev HS, Osmond C, et al. New Delhi birth cohort. adult metabolic syndrome and impaired glucose tolerance are associated with different patterns of bmi gain during infancy: data from the new Delhi birth cohort. Diabetes Care 2008; 31(12): 2349-56.
[http://dx.doi.org/10.2337/dc08-0911] [PMID: 18835958]
[112]
Yajnik CS, Lubree HG, Rege SS, et al. Adiposity and hyperinsulinemia in Indians are present at birth. J Clin Endocrinol Metab 2002; 87(12): 5575-80.
[http://dx.doi.org/10.1210/jc.2002-020434] [PMID: 12466355]
[113]
Held C, Iqbal R, Lear SA, et al. Physical activity levels, ownership of goods promoting sedentary behaviour and risk of myocardial infarction: results of the INTERHEART study. Eur Heart J 2012; 33(4): 452-66.
[http://dx.doi.org/10.1093/eurheartj/ehr432] [PMID: 22238330]
[114]
Karthikeyan G, Teo KK, Islam S, et al. Lipid profile, plasma apolipoproteins, and risk of a first myocardial infarction among Asians: an analysis from the INTERHEART Study. J Am Coll Cardiol 2009; 53(3): 244-53.
[http://dx.doi.org/10.1016/j.jacc.2008.09.041] [PMID: 19147041]
[115]
Chowdhury TA, Lasker SS. Complications and cardiovascular risk factors in South Asians and Europeans with early-onset type 2 diabetes. QJM 2002; 95(4): 241-6.
[http://dx.doi.org/10.1093/qjmed/95.4.241] [PMID: 11937651]
[116]
Ramachandran A, Snehalatha C, Yamuna A, Murugesan N, Narayan KM. Insulin resistance and clustering of cardiometabolic risk factors in urban teenagers in southern India. Diabetes Care 2007; 30(7): 1828-33.
[http://dx.doi.org/10.2337/dc06-2097] [PMID: 17416794]
[117]
Narayan KMV, Kanaya AM. Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways. Diabetologia 2020; 63(6): 1103-9.
[http://dx.doi.org/10.1007/s00125-020-05132-5] [PMID: 32236731]
[118]
Gujral UP, Narayan KM, Kahn SE, Kanaya AM. The relative associations of β-cell function and insulin sensitivity with glycemic status and incident glycemic progression in migrant Asian Indians in the United States: the MASALA study. J Diabetes Complications 2014; 28(1): 45-50.
[http://dx.doi.org/10.1016/j.jdiacomp.2013.10.002] [PMID: 24211090]
[119]
Shah AD, Kandula NR, Lin F, et al. Less favorable body composition and adipokines in South Asians compared with other US ethnic groups: results from the MASALA and MESA studies. Int J Obes 2016; 40(4): 639-45.
[http://dx.doi.org/10.1038/ijo.2015.219] [PMID: 26499444]
[120]
Patel SA, Shivashankar R, Ali MK, Anjana RM, Deepa M, Kapoor D, et al. Is the "South Asian Phenotype" unique to South Asians?: Comparing cardiometabolic risk factors in the CARRS and NHANES studies. Global heart 2016; 11(1): 89-96.
[121]
Unnikrishnan R, Anjana RM, Mohan V. Diabetes in South Asians: is the phenotype different? Diabetes 2014; 63(1): 53-5.
[http://dx.doi.org/10.2337/db13-1592] [PMID: 24357697]
[122]
Enas EA, Chacko V, Pazhoor SG, Chennikkara H, Devarapalli HP. Dyslipidemia in South Asian patients. Curr Atheroscler Rep 2007; 9(5): 367-74.
[http://dx.doi.org/10.1007/s11883-007-0047-y] [PMID: 18001619]
[123]
Misra A, Shrivastava U. Obesity and dyslipidemia in South Asians. Nutrients 2013; 5(7): 2708-33.
[http://dx.doi.org/10.3390/nu5072708] [PMID: 23863826]
[124]
Khawaja KI, Mian SA, Fatima A, et al. Phenotypic and metabolic dichotomy in obesity: clinical, biochemical and immunological correlates of metabolically divergent obese phenotypes in healthy South Asian adults. Singapore Med J 2018; 59(8): 431-8.
[http://dx.doi.org/10.11622/smedj.2018019] [PMID: 29430577]
[125]
Misra A, Soares MJ, Mohan V, et al. Body fat, metabolic syndrome and hyperglycemia in South Asians. J Diabetes Complications 2018; 32(11): 1068-75.
[http://dx.doi.org/10.1016/j.jdiacomp.2018.08.001] [PMID: 30115487]
[126]
Ajjan R, Carter AM, Somani R, Kain K, Grant PJ. Ethnic differences in cardiovascular risk factors in healthy Caucasian and South Asian individuals with the metabolic syndrome. J Thromb Haemost 2007; 5(4): 754-60.
[http://dx.doi.org/10.1111/j.1538-7836.2007.02434.x] [PMID: 17408409]
[127]
Haldar S, Chia SC, Henry CJ. Body Composition in Asians and Caucasians: comparative analyses and influences on cardiometabolic outcomes. Adv Food Nutr Res 2015; 75: 97-154.
[http://dx.doi.org/10.1016/bs.afnr.2015.07.001] [PMID: 26319906]
[128]
Mangalmurti SS, Paley A, Gany F, Fisher EA, Hochman JS. South Asians and risk of cardiovascular disease: current insights and trends. Ethn Dis 2010; 20(4): 474-8.
[PMID: 21305840]
[129]
Durairaj G, Oommen AT, Gopalakrishna Pillai M. A cross-sectional validation study comparing the accuracy of different risk scores in assessing the risk of acute coronary syndrome among patients in a tertiary care hospital in Kerala. Indian Heart J 2020; 72(2): 113-8.
[http://dx.doi.org/10.1016/j.ihj.2020.03.011] [PMID: 32534683]
[130]
Gobardhan SN, Dimitriu-Leen AC, van Rosendael AR, et al. Prevalence by computed tomographic angiography of coronary plaques in south asian and white patients with type 2 diabetes mellitus at low and high risk using four cardiovascular risk scores (UKPDS, FRS, ASCVD, AND JBS3). Am J Cardiol 2017; 119(5): 705-11.
[http://dx.doi.org/10.1016/j.amjcard.2016.11.029] [PMID: 28024655]
[131]
Tillin T, Hughes AD, Whincup P, et al. SABRE Study Group. Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a U.K. tri-ethnic prospective cohort study (SABRE-Southall And Brent REvisited). Heart 2014; 100(1): 60-7.
[http://dx.doi.org/10.1136/heartjnl-2013-304474] [PMID: 24186564]
[132]
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. Int J Cardiol 2014; 176(1): 211-8.
[http://dx.doi.org/10.1016/j.ijcard.2014.07.066] [PMID: 25070380]
[133]
Brindle P, May M, Gill P, et al. Primary prevention of cardiovascular disease: a web-based risk score for seven British black and minority ethnic groups. Heart 2006; 92(11): 1595-602.
[http://dx.doi.org/10.1136/hrt.2006.092346] [PMID: 16762981]
[134]
Aarabi M, Jackson PR. Predicting coronary risk in UK South Asians: an adjustment method for Framingham-based tools. Eur J Cardiovasc Prev Rehabil 2005; 12(1): 46-51.
[http://dx.doi.org/10.1177/204748730501200107] [PMID: 15703505]
[135]
McNeil JJ, Wolfe R, Woods RL, et al. ASPREE Investigator group. Effect of aspirin on cardiovascular events and bleeding in the healthy elderly. N Engl J Med 2018; 379(16): 1509-18.
[http://dx.doi.org/10.1056/NEJMoa1805819] [PMID: 30221597]
[136]
Gaziano JM, Brotons C, Coppolecchia R, et al. ARRIVE Executive Committee. Use of aspirin to reduce risk of initial vascular events in patients at moderate risk of cardiovascular disease (ARRIVE): a randomised, double-blind, placebo-controlled trial. Lancet 2018; 392(10152): 1036-46.
[http://dx.doi.org/10.1016/S0140-6736(18)31924-X] [PMID: 30158069]
[137]
Bansal M, Agarwala R, Kasliwal RR. Imaging atherosclerosis for cardiovascular risk prediction- in search of the holy grail! Indian Heart J 2018; 70(5): 587-92.
[http://dx.doi.org/10.1016/j.ihj.2018.10.001] [PMID: 30392492]
[138]
Shrivastava S, Agrawal V, Kasliwal RR, et al. Coronary calcium and coronary artery disease: an Indian perspective. Indian Heart J 2003; 55(4): 344-8.
[PMID: 14686663]
[139]
Chambless LE, Heiss G, Folsom AR, et al. Association of coronary heart disease incidence with carotid arterial wall thickness and major risk factors: the Atherosclerosis Risk in Communities (ARIC) Study, 1987-1993. Am J Epidemiol 1997; 146(6): 483-94.
[http://dx.doi.org/10.1093/oxfordjournals.aje.a009302] [PMID: 9290509]
[140]
O’Leary DH, Polak JF, Kronmal RA, Manolio TA, Burke GL, Wolfson SK Jr. Cardiovascular Health Study Collaborative Research Group. Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. N Engl J Med 1999; 340(1): 14-22.
[http://dx.doi.org/10.1056/NEJM199901073400103] [PMID: 9878640]
[141]
van der Meer IM, Bots ML, Hofman A, del Sol AI, van der Kuip DA, Witteman JC. Predictive value of noninvasive measures of atherosclerosis for incident myocardial infarction: the Rotterdam Study. Circulation 2004; 109(9): 1089-94.
[http://dx.doi.org/10.1161/01.CIR.0000120708.59903.1B] [PMID: 14993130]
[142]
Hansa G, Bhargava K, Bansal M, Tandon S, Kasliwal RR. Carotid intima-media thickness and coronary artery disease: an Indian perspective. Asian Cardiovasc Thorac Ann 2003; 11(3): 217-21.
[http://dx.doi.org/10.1177/021849230301100308] [PMID: 14514551]
[143]
Jadhav UM, Kadam NN. Association of microalbuminuria with carotid intima-media thickness and coronary artery disease-a cross-sectional study in Western India. J Assoc Physicians India 2002; 50: 1124-9.
[PMID: 12516693]
[144]
Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis. Atherosclerosis 2012; 220(1): 128-33.
[http://dx.doi.org/10.1016/j.atherosclerosis.2011.06.044] [PMID: 21764060]
[145]
Nambi V, Chambless L, Folsom AR, et al. Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk In Communities) study. J Am Coll Cardiol 2010; 55(15): 1600-7.
[http://dx.doi.org/10.1016/j.jacc.2009.11.075] [PMID: 20378078]
[146]
Kasliwal RR, Kaushik M, Grewal H, Bansal M. Carotid ultrasound for cardiovascular risk prediction: From intima-media thickness to carotid plaques. J Indian Acad Echocardiograph Cardiovasc Imag 2017; 1(1): 39-46.
[147]
Kasliwal RR, Bansal M, Desai N, et al. SCORE-India collaborators. A Study to derive distribution of carotid intima media thickness and to determine its correlation with cardiovascular Risk factors in asymptomatic nationwide Indian population (SCORE-India). Indian Heart J 2016; 68(6): 821-7.
[http://dx.doi.org/10.1016/j.ihj.2016.04.009] [PMID: 27931554]
[148]
Singh S, Nagra A, Maheshwari P, et al. Rapid Screening for subclinical atherosclerosis by carotid ultrasound examination: The HAPPY (Heart Attack Prevention Program for You) Substudy. Glob Heart 2013; 8(2): 83-9.
[http://dx.doi.org/10.1016/j.gheart.2013.05.001] [PMID: 25690372]
[149]
Kasliwal RR, Bansal M, Gupta H, Agrawal S. Association of carotid intima-media thickness with left main coronary artery disease. Indian Heart J 2007; 59(1): 50-5.
[PMID: 19098336]
[150]
Kasliwal RR, Bansal M, Bhargava K, Gupta H, Tandon S, Agrawal V. Carotid intima-media thickness and brachial-ankle pulse wave velocity in patients with and without coronary artery disease. Indian Heart J 2004; 56(2): 117-22.
[PMID: 15377132]
[151]
Den Ruijter HM, Peters SA, Anderson TJ, et al. Common carotid intima-media thickness measurements in cardiovascular risk prediction: a meta-analysis. JAMA 2012; 308(8): 796-803.
[http://dx.doi.org/10.1001/jama.2012.9630] [PMID: 22910757]
[152]
Sheridan SL, Crespo E. Does the routine use of global coronary heart disease risk scores translate into clinical benefits or harms? A systematic review of the literature. BMC Health Serv Res 2008; 8: 60.
[http://dx.doi.org/10.1186/1472-6963-8-60] [PMID: 18366711]
[153]
Sheridan SL, Viera AJ, Krantz MJ, et al. Cardiovascular health intervention research and translation network work group on global coronary heart disease risk. The effect of giving global coronary risk information to adults: a systematic review. Arch Intern Med 2010; 170(3): 230-9.
[http://dx.doi.org/10.1001/archinternmed.2009.516] [PMID: 20142567]
[154]
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. Vasc Health Risk Manag 2008; 4(1): 199-211.
[http://dx.doi.org/10.2147/vhrm.2008.04.01.199] [PMID: 18629375]
[155]
Singh T, Pilania M, Jat GS, Kumar R. Ambiguity about selection of cardiovascular risk stratification tools: evidence from a North Indian rural population. Indian J Community Med 2018; 43(3): 170-4.
[PMID: 30294082]

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