1887
Volume 2021, Issue 2
  • ISSN: 0253-8253
  • EISSN: 2227-0426

Abstract

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population.

Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population.

Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models.

Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains.

Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

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2021-09-26
2024-03-19
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References

  1. Lerner DJ, Kannel WB. Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. Am Heart J. 1986; 111:(2): 383–90.
    [Google Scholar]
  2. World Health Organization. Cardiovascular diseases (CVDs) [Internet]. 2021 [updated 2021 June 11; cited 2021 July 15]. Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
    [Google Scholar]
  3. JBS3 Joint British Societies for the prevention of cardiovascular disease. Lifetime Risk [Internet]. [cited 2021 July 15]. Available from: https://www.jbs3risk.co.uk/pages/lifetime_risk.htm .
  4. Framingham Heart Study. Hard Coronary Heart Disease (10-year risk) [Internet]. [cited 2021 July 15]. Available from: https://www.framinghamheartstudy.org/fhs-risk-functions/hard-coronary-heart-disease-10-year-risk/.
  5. ASSIGN Score. Prioritising prevention of cardiovascular disease [Internet]. [cited 2021 July 15]. Available from: http://www.assign-score.com/estimate-the-risk/.
  6. ClinRisk. Welcome to the QRISK®3-2018 risk calculator https://qrisk.org/three [Internet]. [cited 2021 July 15]. Available from: https://qrisk.org/three/.
  7. Scottish Intercollegiate Guidelines Network. SIGN 149: Risk estimation and the prevention of cardiovascular disease. A national clinical guideline [Internet]. 2017 [updated 2017 July; cited 2021 July 15]. Available from: https://www.sign.ac.uk/assets/sign149.pdf .
  8. Allan GM, Nouri F, Korownyk C, Kolber MR, Vandermeer B, McCormack J. Agreement among cardiovascular disease risk calculators. Circulation, 2013; 127:(19): 1948–56.
    [Google Scholar]
  9. Institute of Health Metrics and Evaluation. Qatar [Internet].2019 [cited 2021 July 15]. Available from: http://www.healthdata.org/qatar .
  10. World Health Organization. Noncommunicable Diseases (NCD) Country profiles Qatar: risk of premature death due to NCDs [Internet]. 2018 [cited 2021 July 15]. Available from: https://www.who.int/nmh/countries/qat_en.pdf?ua = 1 .
  11. Wikipedia contributors. Demographics of Qatar. Wikipedia, The Free Encyclopedia [Internet]. 2021 [updated 2021 August 9; cited 2021 July 15]. Available from: https://en.wikipedia.org/wiki/Demographics_of_Qatar#Population .
  12. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al. 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.
    [Google Scholar]
  13. Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Yuan-I Min, Basu S. Clinical implications of revised pooled cohort equations for estimating Atherosclerotic cardiovascular disease risk. Ann Intern Med. 2018; 169:20–9.
    [Google Scholar]
  14. Moons KG, de Groot. JA, Bouwmeester W, Vergouwe Y, Mallett S, Altman DG, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med. 2014; 11:e1001744. doi:10.1371/journal.pmed.1001744.
    [Google Scholar]
  15. Damen JA, Hooft L, Schuit E, Debray TP, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016; 353:i2416. doi:10.1136/bmj.i2416.
    [Google Scholar]
  16. Siontis GCM, Tzoulaki I, Sionti KC, Ioannidis JPA. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012; 344:e3318. doi:10.1136/bmj.e3318.
    [Google Scholar]
  17. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, 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:(Suppl 2): S49–73.
    [Google Scholar]
  18. Hajifathalian K, Ueda P, Lu Y, Woodward M, Ahmadvand A, Aguilar-Salinas CA, 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; 3:339–55. doi:10.1016/S2213-8587(15)00081-9.
    [Google Scholar]
  19. Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med. 2019; 170:(1): 51–8.
    [Google Scholar]
  20. Pencina MJ, D'Agostino RB, Pencina KM, Janssens ACJWGreenland P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol. 2012; 176:(6): 473–81.
    [Google Scholar]
  21. Sharot T. The optimism bias. Current Biology. 2011; 21:(23): R941-5. doi: 10.1016/j.cub.2011.10.030.
    [Google Scholar]
  22. Bazo-Alvarez JC, Quispe R, Peralta F, Poterico JA, Valle GA, Burroughs M, et al. Agreement between cardiovascular disease risk scores in resource-limited settings: Evidence from 5 Peruvian sites. Crit Pathw Cardiol. 2015; 14:74–80.
    [Google Scholar]
  23. Boatening D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze MB, et al. Cardiovascular disease risk prediction in sub-Saharan African populations — Comparative analysis of risk algorithms in the RODAM study. Int J Cardiol. 2018;254: 310–5.
    [Google Scholar]
  24. Chia YC, Lim MH. and Ching SM. Validation of the pooled cohort risk score in an Asian population – a retrospective cohort study. BMC Cardiovasc Disord. 2014; 14:163.
    [Google Scholar]
  25. DeFilippis AP, Young R, Carrubba CJ, McEvoy JW, Budoff MJ, Blumenthal RS, et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multi-ethnic cohort. Ann Intern Med. 2015; 162:(4): 266–75.
    [Google Scholar]
  26. DeGoma EM, Dunbar RL, Jacoby D, French B. Differences in absolute risk of cardiovascular events using risk-refinement tests: A systematic analysis of four cardiovascular risk equations. Atherosclerosis. 2013; 227:172e177.
    [Google Scholar]
  27. de Las Heras Gala T, Geisel MH, Peters A, Thorand B, Baumert J, Lehmann N, et al. Recalibration of the ACC/AHA risk score in two population-based German cohorts. PLoS ONE. 2016; 11:(10): e0164688.
    [Google Scholar]
  28. Dufouil C, Beiser A, McLure LA, Wolf PA, Tzourio C, Howard VJ, et al. Revised Framingham Stroke Risk Profile to Reflect Temporal Trends. Circulation. 2017; 135:(12): 1145–59.
    [Google Scholar]
  29. Fatema K, Rahman B, Zwar NA, Milton AH, Ali L. Short-term predictive ability of selected cardiovascular risk prediction models in a rural Bangladeshi population: a case-cohort study. BMC Cardiovasc Disord. 2016; 16:105.
    [Google Scholar]
  30. Flueckiger P, Longstreth W, Herrington D, Yeboah J. Revised Framingham Stroke Risk Score, Non-traditional Risk Markers, and Incident Stroke in a Multiethnic Cohort. Stroke. 2018; 49:(2): 363–369.
    [Google Scholar]
  31. Foraker RE, Greiner M, Sims M, Tucker KL, Towfighi A, Bidulescu A, et al. Comparison of risk scores for the prediction of stroke in African Americans: findings from the Jackson heart study. Am Heart J. 2016; 177:25–32.
    [Google Scholar]
  32. Fox ER, Samdarshi TE, Musani SK, Pencina MJ, Sung JH, Bertoni AG, et al. Development and validation of risk prediction models for cardiovascular events in black adults: the Jackson heart study Cohort. JAMA Cardiol. 2016; 1:(1): 15–25.
    [Google Scholar]
  33. Goh LGH, Welborn TA, Dhaliwal SS. Independent external validation of cardiovascular disease mortality in women utilising Framingham and SCORE risk models: a mortality follow-up study. BMC. Women's Health. 2014; 14:118.
    [Google Scholar]
  34. Goh LGH, Dhaliwal SS, Welborn TA, Thompson PL, Maycock BR, Kerr DA, et al. Cardiovascular disease risk score prediction models for women and its applicability to Asians. Int J Women's Health. 2014; 6:259–67.
    [Google Scholar]
  35. Harari G, Green MS, Zelber-Sagi S. Estimation and development of 10- and 20-year cardiovascular mortality risk models in an industrial male workers database. Prevent Med. 2017; 103:26–32.
    [Google Scholar]
  36. Hu G, Root M, Duncan AW. Adding multiple risk factors improves Framingham coronary heart disease risk scores. Vasc Health Risk Management. 2014; 10:557–62.
    [Google Scholar]
  37. Hua X, McDermott R, Lung T, Wenitong M, Tran-Duy A, Li M, et al. Validation and recalibration of the Framingham cardiovascular disease risk models in an Australian Indigenous cohort. Eur J Prevent Cardiol. 2017; 24:(15): 1660–9.
    [Google Scholar]
  38. Jee SH, Jang Y, Oh DJ, Oh BH, Lee SH, Park SW, et al. A coronary heart disease prediction model: the Korean heart study. BMJ Open. 2014; 4:e005025.
    [Google Scholar]
  39. Johansson JK, Pukka PJ, Niiranen TJ, Varis J, Peltonen M, Salomaa V, et al. Health 2000 score – development and validation of a novel cardiovascular risk score. Ann Med. 2016; 48:(6): 403–9.
    [Google Scholar]
  40. Jung KJ, Jang Y, Oh DJ, Oh BH, Lee SH, Park SW, et al. The ACC/AHA 2013 pooled cohort equations compared to a Korean Risk Prediction Model for atherosclerotic cardiovascular disease. Atherosclerosis. 2015; 242:367e375.
    [Google Scholar]
  41. Kariukia JK, Stuart-Shorb EM, Leveilleb SG, Gonab P, Cromwellb J, Haymanb LL. Validation of the nonlaboratory-based Framingham cardiovascular disease risk assessment algorithm in the Atherosclerosis Risk in Communities dataset. J Cardiovasc Med. 2017; 18:1–10.
    [Google Scholar]
  42. Karjalainen T, Adiels M, Bjorck L, Cooney MT, Graham I, Perk J, et al. An evaluation of the performance of SCORE Sweden 2015 in estimating cardiovascular risk The Northern Sweden MONICA Study 1999–2014. Eur J Prevent Cardiol. 2017; 24:(1): 103–10.
    [Google Scholar]
  43. Kavousi M, Leening MJG, Nanchen D, Greenland P, Graham IM, Steyerberg EW, et al. Comparison of application of the ACC/AHA guidelines, adult treatment panel III guidelines, and European Society of Cardiology guidelines for cardiovascular disease prevention in a European cohort. JAMA. 2014; 311:(14): 1416–23.
    [Google Scholar]
  44. Kempf K, Martin S, Döhring C, Dugi K, Haastert B, Schneider M. The Boehringer Ingelheim employee study (Part 2): 10-year cardiovascular diseases risk estimation. Occup Med. 2016; 66:(7): 543–50.
    [Google Scholar]
  45. Lee K. 10-year risk for atherosclerotic cardiovascular disease and coronary heart disease among Korean adults: Findings from the Korean National Health and Nutrition Examination Survey 2009–2010. Int J Cardiol. 2014; 176:418–22.
    [Google Scholar]
  46. Lee CH, Woo YC, Lam JKY, Fong CHY, Cheung BMY, Lam KSL, et al. Validation of the pooled cohort equations in a long-term cohort study of Hong Kong Chinese. J Clinic Lipidol. 2015; 9:640–6.
    [Google Scholar]
  47. Marrugat J, Subirana I, Ramos R, Vila J, Marín-Ibañez A, Guembe MJ, et al. Derivation and validation of a set of 10-year cardiovascular risk predictive functions in Spain: the FRESCO Study. Prevent Med. 2014; 61:66–74.
    [Google Scholar]
  48. Mortensen MB, Afzal S, Nordestgaard BG, Falk E. ACC/AHA risk-based approach versus trial-based approaches to guide statin therapy. Am Coll Cardiol. 2015; 66:2699–709.
    [Google Scholar]
  49. Mortensen MB, Nordestgaard BG, Afzal S, Falk E. ACC/AHA guidelines superior to ESC/EAS guidelines for primary prevention with statins in non-diabetic Europeans: the Copenhagen General Population Study. Eur Heart J. 2017; 38:586–94.
    [Google Scholar]
  50. Nishimura K, Okamura T, Watanabe M, Nakai M, Takegami M, Higashiyama A, et al. Predicting coronary heart disease using risk factor categories for a Japanese urban population, and comparison with the Framingham Risk Score: The Suita Study. J Atheroscler Thromb. 2014; 21:784–98.
    [Google Scholar]
  51. Pylypchuk R, Wells S, Kerr A, Poppe K, Riddell T, Harwood M, et al. Cardiovascular disease risk prediction equations in 400 000 primary care patients in New Zealand: a derivation and validation study. Lancet. 2018; 391:1897–907.
    [Google Scholar]
  52. Pursnani A, Massaro JM, D'Agostino RB, O'Donnell CJ, Hoffmann U. Guideline-based statin eligibility, coronary artery calcification, and cardiovascular events. JAMA. 2015; 314:(2): 134–41.
    [Google Scholar]
  53. Qureshi WT, Michos ED, Flueckiger P, Blaha M, Sandfort V, Herrington DM, et al. Impact of replacing the pooled cohort equation with other cardiovascular disease risk scores on atherosclerotic cardiovascular disease risk assessment (from the Multi-Ethnic Study of Atherosclerosis [MESA]). Am J Cardiol. 2016; 118:691e696.
    [Google Scholar]
  54. Sarrafzadegan N, Hassannejad R, Marateb HR, Talaei M, Sadeghi M, Roohafza HR, et al. PARS risk charts: a 10-year study of risk assessment for cardiovascular diseases in Eastern Mediterranean Region. PLoS ONE. 2017; 12:(12): e0189389.
    [Google Scholar]
  55. Selvarajah S, Kaur G, Haniff J, Cheong KC, Hiong TG, van derGraaf Y, et al. Comparison of the framingham risk score, SCORE and WHO/ISH cardiovascular risk prediction models in an Asian population. Int J Cardiol. 2014; 176:211–8.
    [Google Scholar]
  56. Sun C, Xu F, Liu X, Fang M, Zhou H, Lian Y, et al. Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population. Scientific Reports. 2017; 7:43227.
    [Google Scholar]
  57. Sussman JB, Wiitala WL, Zawistowski M, Hofer TP, Bentley D, Hayward RA. The veterans affairs cardiac risk score: recalibrating the ASCVD score for applied use. Med Care. 2017; 55:(9): 864–70.
    [Google Scholar]
  58. Tillin T, Hughes AD, Whincup P, Mayet J, Sattar N, McKeigue PM, et al. Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a UK tri-ethnic prospective cohort study (SABRE—Southall And Brent REvisited). Heart. 2014; 100:(1): 60–7.
    [Google Scholar]
  59. Tralhãoa A, Ferreiraa AM, de Araújo onçalvesa P, Rodriguesa R, Costaa C, Guerreiroa S, et al. Accuracy of pooled-cohort equation and SCORE cardiovascular risk calculators to identify individuals with high coronary atherosclerotic burden – implications for statin treatment. Coronary Artery Disease. 2016; 27:573–79.
    [Google Scholar]
  60. van Staa TP, Gulliford M, Ng ESW, Goldacre B, Smeeth L. Prediction of cardiovascular risk using Framingham, ASSIGN and QRISK2: how well do they predict individual rather than population risk? PLoS ONE. 2014; 9:(10): e106455.
    [Google Scholar]
  61. Veronesia G, Giampaoli S, Vanuzzoc D, Gianfagnaa F, Palmieri L, Grassie G, et al. Combined use of short-term and long-term cardiovascular risk scores in primary prevention: an assessment of clinical utility. Cardiovasc Med. 2017; 18:318-24.
    [Google Scholar]
  62. Yang X, Li J, Hu D, Chen J, Li Y, Liu F, 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; 134:1430–40.
    [Google Scholar]
  63. D'Agostino, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General Cardiovascular Risk Profile for use in Primary Care. The Framingham Heart Study. Circulation. 2008; 117:743–53.
    [Google Scholar]
  64. Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003; 24:(11): 987–1003.
    [Google Scholar]
  65. Yadlowsky S, Hayward RA, Sussman JB, McClelland RL, Min YI, Basu S. Clinical implications of revised pooled cohort equations for estimating atherosclerotic cardiovascular disease risk. Ann Intern Med. 2018; 169:20–9.
    [Google Scholar]
  66. Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, 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.
    [Google Scholar]
  67. 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; 297:611–9.
    [Google Scholar]
  68. WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019; 7:(10): e1332–45. doi: 10.1016/S2214-109X(19)30318-3.
    [Google Scholar]
  69. 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.
    [Google Scholar]
  70. Kulshreshtha A, Vaccarino V, Judd SE, Howard VJ, McClellan WM, Muntner P, et al. Life's simple 7 and risk of incident stroke: the reasons for geographic and racial differences in stroke study. Stroke. 2013; 44:(7):1909–14.
    [Google Scholar]
  71. Folsom AR, Yatsuya H, Nettleton JA, Lutsey PL, Cushman M, Rosamond WD, et al. Community prevalence of ideal cardiovascular health, by the American heart association definition, and relationship with cardiovascular disease. J Am Coll Cardiol. 2011; 57:(16): 1690–6.
    [Google Scholar]
  72. 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; 93:172–6. doi:10.1136/hrt.2006.108167.
    [Google Scholar]
  73. Vartiainen E, Laatikainen T, Peltonen M, Puska P. Predicting coronary heart disease and stroke: the FINRISK calculator. Global Heart. 2016; 11:(2): 213–6.
    [Google Scholar]
  74. 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: 310–5.
    [Google Scholar]
  75. Cullen P, Schulte H, Assmann G. The Münster Heart Study (PROCAM): total mortality in middle-aged men is increased at low total and LDL cholesterol concentrations in smokers but not in nonsmokers. Circulation. 1997; 96:(7): 2128–36.
    [Google Scholar]
  76. Analyticca Datalab. Hosmer-Lemeshow Goodness-of-Fit Test [Internet]. 2019 [updated 2019 Jan 23; cited 2021 July 15]. Available at: https://medium.com/@analyttica/hosmer-lemeshow-goodness-of-fit-test-65b339477210 .
  77. Damen JA, Pajouheshnia R, Heus P, Moons KGM, Reitsma JB, Scholten RJPM, et al. Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis. BMC Med. 2019; 17:(1): 109. doi:10.1186/s12916-019-1340-7.
    [Google Scholar]
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