SCORE 2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe
AbstractAims – to develop and validate a recalibrated prediction model (SCORE 2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe.
Methods and results. SCORE 2-Diabetes was developed by extending SCORE 2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors, i. e. age, smoking, systolic blood pressure, total, and HDL-cholesterol, as well as diabetes-related variables (i. e. age at diabetes diagnosis, glycated haemoglobin (HbA1c) and creatinine-based estimated glomerular filtration rate (eGFR)]. Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE 2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE 2-Diabetes risk predictions varied several-fold, depending on individuals’ levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m2, and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m2, and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8 and 13%, respectively.
Conclusion. SCORE 2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe.
Keywords:prediction model; diabetes; cardiovascular diseases
SCORE 2-Diabetes Working Group and the ESC Cardiovascular Risk Collaboration. SCORE 2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe. European Heart Journal. 2023; 44 (28): 2544–56. DOI: https://doi.org/10.1093/eurheartj/ehad260
References
1. Timmis A., Vardas P., Townsend N., Torbica A., Katus H., De Smedt D., et al. European Society of cardiology: cardiovascular disease statistics 2021. Eur Heart J. 2022; 43: 716–99. DOI: https://doi.org/10.1093/eurheartj/ehab892
2. Emerging Risk Factors Collaboration. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010; 375: 2215–22. DOI: https://doi.org/10.1016/S0140-6736(10)60484-9
3. Visseren F.L.J., Mach F., Smulders Y.M., Carballo D., Koskinas K.C., Bäck M., et al. 2021 ESC guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2021; 42: 3227–337. DOI: https://doi.org/10.1093/eurheartj/ehab484
4. Goff D.C., Lloyd-Jones D.M., Bennett G., Coady S., D’Agostino R.B., 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: S 49–73. DOI: https://doi.org/10.1161/01.cir.0000437741.48606.98
5. 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: e1332–45. DOI: https://doi.org/10.1016/S2214-109X(19)30318-3
6. 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. DOI: https://doi.org/10.1136/bmj.j2099
7. Dziopa K., Asselbergs F.W., Gratton J., Chaturvedi N., Schmidt A.F. Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings. Diabetologia. 2022; 65: 644–56. DOI: https://doi.org/10.1007/s00125-021-05640-y
8. Read S.H, van Diepen M., Colhoun H.M., Halbesma N., Lindsay R.S., McKnight J.A., et al. Performance of cardiovascular disease risk scores in people diagnosed with type 2 diabetes: external validation using data from the national Scottish diabetes register. Diabetes Care. 2018; 41: 2010–8. DOI: https://doi.org/10.2337/dc18-0578
9. Berkelmans G.F.N., Gudbjornsdottir S., Visseren F.L.J., Wild S.H., Franzen S., Chalmers J., et al. Prediction of individual life-years gained without cardiovascular events from lipid, blood pressure, glucose, and aspirin treatment based on data of more than 500 000 patients with type 2 diabetes mellitus. Eur Heart J. 2019; 40: 2899–906. DOI: https://doi.org/10.1093/eurheartj/ehy839
10. Kengne A.P., Patel A., Marre M., Travert F., Lievre M., Zoungas S., et al. Contemporary model for cardiovascular risk prediction in people with type 2 diabetes. Eur J Cardiovasc Prev Rehabil. 2011; 18: 393–8. DOI: https://doi.org/10.1177/1741826710394270
11. Stevens R.J., Kothari V., Adler A.I., Stratton I.M.; United Kingdom Prospective Diabetes Study Group. The UKPDS risk engine: a model for the risk of coronary heart disease in type II diabetes (UKPDS 56). Clin Sci (Lond). 2001; 101: 671–9. DOI: https://doi.org/10.1042/CS20000335
12. Hageman S., Pennells L., Ojeda F., Kaptoge S., Kuulasmaa K., de Vries T., et al. SCORE 2 Risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021; 42: 2439–54. DOI: https://doi.org/10.1093/eurheartj/ehab309
13. McKnight J.A., Morris A.D., Cline D., Peden N., Fischbacher C., Wild S. Implementing a national quality assurance system for diabetes care: the Scottish diabetes survey 2001–2006. Diabet Med. 2008; 25: 743–6. DOI: https://doi.org/10.1111/j.1464-5491.2008.02453.x
14. Herrett E., Gallagher A.M., Bhaskaran K., Forbes H., Mathur R., van Staa T., et al. Data resource profile: clinical practice research datalink (CPRD). Int J Epidemiol. 2015; 44: 827–36. DOI: https://doi.org/10.1093/ije/dyv098
15. Sudlow C., Gallacher J., Allen N., Beral V., Burton P., Danesh J., et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015; 12: e1001779. DOI: https://doi.org/10.1371/journal.pmed.1001779
16. Emerging Risk Factors Collaboration; Danesh J., Erqou S., Walker M., Thompson S.G., Tipping R., Ford C., et al. The emerging risk factors collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. Eur J Epidemiol. 2007; 22: 839–69. DOI: https://doi.org/10.1007/s10654-007-9165-7
17. Emerging Risk Factors Collaboration; Angelantonio E., Kaptoge S., Wormser D., Willeit P., Butterworth A.S., Bansal N., et al. Association of cardiometabolic multimorbidity with mortality. JAMA. 2015; 314: 52–60. DOI: https://doi.org/10.1001/jama.2015.7008
18. Gudbjornsdottir S., Cederholm J., Nilsson P.M., Eliasson B.; Steering Committee of the Swedish National Diabetes Register. The national diabetes register in Sweden: an implementation of the st Vincent declaration for quality improvement in diabetes care. Diabetes Care. 2003; 26: 1270–6. DOI: https://doi.org/10.2337/diacare.26.4.1270
19. Mata-Cases M., Mauricio D., Real J., Bolibar B., Franch-Nadal J. Is diabetes mellitus correctly registered and classified in primary care? A population-based study in Catalonia, Spain. Endocrinol Nutr. 2016; 63: 440–8. DOI: https://doi.org/10.1016/j.endonu.2016.07.004
20. Bolibar B., Fina Aviles F., Morros R., Garcia-Gil M. del M., Hermosilla E., Ramos R., et al. SIDIAP database: electronic clinical records in primary care as a source of information for epidemiologic research. Med Clin (Barc) 2012; 138: 617–21. DOI: https://doi.org/10.1016/j.medcli.2012.01.020
21. Carinci F., Štotl I., Cunningham S.G., Poljicanin T., Pristas I., Traynor V., et al. Making use of comparable health data to improve quality of care and outcomes in diabetes: the EUBIROD review of diabetes registries and data sources in Europe. Front Clin Diabetes Healthc. 2021; 2: 744516. DOI: https://doi.org/10.3389/fcdhc.2021.744516
22. Cunningham S.G., Carinci F., Brillante M., Leese G.P., McAlpine R.R., Azzopardi J., et al. Core standards of the EUBIROD project. Defining a European diabetes data dictionary for clinical audit and healthcare delivery. Methods Inf Med. 2016; 55: 166–76. DOI: https://doi.org/10.3414/ME 15-01-0016
23. EUBIROD. NeuBIRO Software. URL: http://www.eubirod.eu/academy/software.html (date of access December, 2022).
24. Di Iorio C.T., Carinci F., Oderkirk J., Smith D., Siano M., de Marco D.A., et al. Assessing data protection and governance in health information systems: a novel methodology of privacy and ethics impact and performance assessment (PEIPA). J Med Ethics. 2021; 47: e23. DOI: https://doi.org/10.1136/medethics-2019-105948
25. Di Iorio C.T., Carinci F., Brillante M., Azzopardi J., Beck P., Bratina N., et al. Cross-border flow of health information: is ‘privacy by design’ enough? Privacy performance assessment in EUBIROD. Eur J Public Health. 2013; 23: 247–53. DOI: https://doi.org/10.1093/eurpub/cks043
26. Di Iorio C.T., Carinci F., Azzopardi J., Baglioni V., Beck P., Cunningham S., et al. Privacy impact assessment in the design of transnational public health information systems: the BIRO project. J Med Ethics. 2009; 35: 753–61. DOI: https://doi.org/10.1136/jme.2009.029918
27. Holman N., Knighton P., Wild S.H., Sattar N., Dew C., Gregg E.W., et al. Cohort profile: national diabetes audit for England and Wales. Diabet Med. 2021; 38: e14616. DOI: https://doi.org/10.1111/dme.14616
28. Wolbers M., Koller M.T., Witteman J.C., Steyerberg E.W. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology. 2009; 20: 555–61. DOI: https://doi.org/10.1097/EDE.0b013e3181a39056
29. Inker L.A., Eneanya N.D., Coresh J., Tighiouart H., Wang D., Sang Y., et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med. 2021; 385: 1737–49. DOI: https://doi.org/10.1056/NEJMoa2102953
30. Collins G.S., Reitsma J.B., Altman D.G., Moons K.G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015; 162: 55–63. DOI: https://doi.org/10.7326/M14-0697
31. European Society of Cardiology. URL: https://www.escardio.org/Education/ESC-Prevention-of-CVD-Programme/Risk-assessment/esc-cvd-risk-calculation-app (date of access December, 2022).
32. European Society of Cardiology. URL: https://www.escardio.org/Education/Practice-Tools/CVD-prevention-toolbox/HeartScore (date of access December, 2022).
33. Batty G.D., Gale C.R., Kivimaki M., Deary I.J., Bell S. Comparison of risk factor associations in UK biobank against representative, general population-based studies with conventional response rates: prospective cohort study and individual participant meta-analysis. BMJ. 2020; 368: m131. DOI: https://doi.org/10.1136/bmj.m131
34. K., Coresh J., Sang Y., Chalmers J., Fox C., Guallar E., et al. Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data. Lancet Diabetes Endocrinol. 2015; 3: 514–25. DOI: https://doi.org/10.1016/S2213-8587(15)00040-6
35. Xu Z., Arnold M., Stevens D., Kaptoge S., Pennells L., Sweeting M.J., et al. Prediction of cardiovascular disease risk accounting for future initiation of statin treatment. Am J Epidemiol. 2021; 190: 2000–14. DOI: https://doi.org/10.1093/aje/kwab031
36. de Vries T., Cooney M.T., Selmer R.M., Hageman S.H.J., Pennells L.A., Wood A., et al. SCORE 2-OP Risk prediction algorithms: estimating incident cardiovascular event risk in older persons in four geographical risk regions. Eur Heart J. 2021; 42: 2455–67. DOI: https://doi.org/10.1093/eurheartj/ehab312
37. Pennells L., Kaptoge S., Wood A., Sweeting M., Zhao X., White I., et al. Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. Eur Heart J. 2019; 40: 621–31. DOI: https://doi.org/10.1093/eurheartj/ehy653
38. Hippisley-Cox J., Coupland C., Vinogradova Y., Robson J., Minhas R., Sheikh A., et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2. BMJ. 2008; 336: 1475–82. DOI: https://doi.org/10.1136/bmj.39609.449676.25