Macrovascular Risk Equations Based on the CANVAS Program

Abstract

Background

Widely used risk equations for cardiovascular outcomes for individuals with type 2 diabetes mellitus (T2DM) have been incapable of predicting cardioprotective effects observed in recent cardiovascular outcomes trials (CVOTs) involving individuals with T2DM at high risk for or with established cardiovascular disease (CVD).

Objective

We developed cardiovascular and mortality risk equations using patient-level data from the CANVAS (CANagliflozin cardioVascular Assessment Study) Program to address this shortcoming.

Methods

Data from 10,142 patients with T2DM at high risk for or with established CVD, randomized to canagliflozin + standard of care (SoC) or SoC alone and followed for a mean duration of 3.6 years in the CANVAS Program were used to derive parametric risk equations for myocardial infarction (MI), stroke, hospitalization for heart failure (HHF), and death. Accumulated knowledge from the widely used UKPDS-OM2 (United Kingdom Prospective Diabetes Study Outcomes Model 2) was leveraged, and any departures in parameterization were limited to those necessary to provide adequate goodness of fit. Candidate explanatory covariates were selected using only the placebo arm to minimize confounding effects. Internal validation was performed separately by study treatment arm.

Results

UKPDS-OM2 predicted CANVAS Program outcomes poorly. Recalibrating UKPDS-OM2 intercepts improved calibration in some cases. Refitting the coefficients but otherwise preserving the UKPDS-OM2 structure improved the fit substantially, which was sufficient for stroke and death. For MI, reselecting UKPDS-OM2 covariates and functional form proved sufficient. For HHF, selection from a broad set of candidate covariates and inclusion of a canagliflozin indicator was required.

Conclusion

These risk equations address some of the limitations of widely used risk equations, such as the UKPDS-OM2, for modeling cardioprotective treatments for individuals with T2DM and high cardiovascular risk, including derivation from overly healthy patients treated with agents that lack cardioprotection and have been described as reflecting a different therapeutic era. Future work is needed to examine external validity.

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Notes

  1. 1.

    Vital status was collected for all patients who did not withdraw consent (1.4% of the population withdrew consent). The analysis considers the outcomes censored at the minimum of last trial contact date, trial end date, or the last laboratory measurement + 13 months.

  2. 2.

    For example, a post hoc analysis of the EMPA-REG OUTCOME trial attributed 90% of reduction in cardiovascular death to risk factors (mostly hematocrit, hemoglobin, and albumin) [52], but these risk factors may have captured features identifying empagliflozin treatment (and associated benefits in other channels) rather than a direct causal relationship between these biomarkers and event risk [53].

References

  1. 1.

    American Diabetes Association. Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004;27(9):2262–5.

  2. 2.

    Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation (Handbooks in Health Economic Evaluation). Oxford: Oxford University Press; 2006.

    Google Scholar 

  3. 3.

    Kothari V, Stevens RJ, Adler AI, Stratton IM, Manley SE, Neil HA, et al. UKPDS 60: risk of stroke in type 2 diabetes estimated by the UK Prospective Diabetes Study risk engine. Stroke J Cerebral Circul. 2002;33(7):1776–81.

    Article  Google Scholar 

  4. 4.

    Stevens RJ, Kothari V, Adler AI, Stratton IM. The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56). Clin Sci (Lond, Engl: 1979). 2001;101(6):671–9.

    CAS  Article  Google Scholar 

  5. 5.

    Eastman RC, Javitt JC, Herman WH, Dasbach EJ, Zbrozek AS, Dong F, et al. Model of complications of NIDDM. I. Model construction and assumptions. Diabetes Care. 1997;20(5):725–34.

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Eastman RC, Javitt JC, Herman WH, Dasbach EJ, Copley-Merriman C, Maier W, et al. Model of complications of NIDDM. II. Analysis of the health benefits and cost-effectiveness of treating NIDDM with the goal of normoglycemia. Diabetes Care. 1997;20(5):735–44.

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Palmer AJ, Mount Hood 5 Modeling Group; Clarke P, Gray A, Leal J, Lloyd A, et al. Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting. Value in Health. 2013;16(4):670–85.

  8. 8.

    Anderson KM, Odell PM, Wilson PW, Kannel WB. Cardiovascular disease risk profiles. Am Heart J. 1991;121(1 Pt 2):293–8.

    CAS  PubMed  Article  Google Scholar 

  9. 9.

    Brown JB, Palmer AJ, Bisgaard P, Chan W, Pedula K, Russell A. The Mt Hood challenge: cross-testing two diabetes simulation models. Diabetes Res Clin Pract. 2000;50(3):S57–64.

    PubMed  Article  Google Scholar 

  10. 10.

    Mt Hood Diabetes Challenges. Diabetes simulation modeling database. 2019. https://www.mthooddiabeteschallenge.com/registry. Accessed 2 Dec 2019.

  11. 11.

    Govan L, Wu O, Lindsay R, Briggs A. How do diabetes models measure up? A review of diabetes economic models and ADA guidelines. J Health Econ Outcomes Res. 2015;3(2):132–52.

    Article  Google Scholar 

  12. 12.

    Asche CV, Hippler SE, Eurich DT. Review of models used in economic analyses of new oral treatments for type 2 diabetes mellitus. PharmacoEconomics. 2014;32(1):15–27.

    PubMed  Article  Google Scholar 

  13. 13.

    Kent S, Becker F, Feenstra T, Tran-Duy A, Schlackow I, Tew M, et al. The challenge of transparency and validation in health economic decision modelling: a view from Mount Hood. PharmacoEconomics. 2019;37(11):1305–12.

    PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Palmer AJ, Roze S, Valentine WJ, Minshall ME, Foos V, Lurati FM, et al. Validation of the CORE Diabetes Model against epidemiological and clinical studies. Curr Med Res Opin. 2004;20(Suppl 1):S27–40.

    PubMed  Article  Google Scholar 

  15. 15.

    McEwan P, Foos V, Palmer JL, Lamotte M, Lloyd A, Grant D. Validation of the IMS CORE Diabetes Model. Value Health. 2014;17(6):714–24.

    PubMed  Article  Google Scholar 

  16. 16.

    McEwan P, Ward T, Bennett H, Bergenheim K. Validation of the UKPDS 82 risk equations within the Cardiff Diabetes Model. Cost Effectiveness Resour Alloc. 2015;13:12.

    Article  Google Scholar 

  17. 17.

    Willis M, Johansen P, Nilsson A, Asseburg C. Validation of the Economic and Health Outcomes Model of Type 2 Diabetes Mellitus (ECHO-T2DM). PharmacoEconomics. 2017;35(3):375–96.

    PubMed  Article  Google Scholar 

  18. 18.

    Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–57.

    CAS  PubMed  Article  Google Scholar 

  19. 19.

    Wiviott SD, Raz I, Bonaca MP, Mosenzon O, Kato ET, Cahn A, et al. Dapagliflozin and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2019;380(4):347–57.

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Zinman B, Wanner C, Lachin JM, Fitchett D, Bluhmki E, Hantel S, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117–28.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004;47(10):1747–59.

  22. 22.

    Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925–33.

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Li J, Woodward M, Perkovic V, Figtree GA, Heerspink HJL, Mahaffey KW, et al. Mediators of the effects of canagliflozin on heart failure in patients with type 2 diabetes. Heart Failure. 2020;8(1):57.

    PubMed  Google Scholar 

  24. 24.

    Evans M, Johansen P, Vrazic H. Incorporating cardioprotective effects of once-weekly semaglutide in estimates of health benefits for patients with type 2 diabetes. Presented at: American Diabetes Association (ADA) 78th Scientific Sessions; 22–26 June 2018; Orlando, FL.

  25. 25.

    Willis M, Neslusan C, Johansen P, Nilsson A. The importance of considering the evolving evidence base on cardiovascular effects of anti-hyperglycemic agents on estimates of ‘value for money’. Poster presented at: American Diabetes Association (ADA) 77th Scientific Sessions; 2017 June 9–13; San Diego, CA.

  26. 26.

    Kuo S, Ye W, Duong J, Herman WH. Are the favorable cardiovascular outcomes of empagliflozin treatment explained by its effects on multiple cardiometabolic risk factors? A simulation of the results of the EMPA-REG OUTCOME trial. Diabetes Res Clin Pract. 2018;141:181–9.

    CAS  PubMed  Article  Google Scholar 

  27. 27.

    Canadian Agency for Drugs and Technologies in Health (CADTH). Therapeutic review new drugs for type 2 diabetes: second-line therapy recommendations report. 2017. https://www.cadth.ca/sites/default/files/pdf/TR0012_T2DM_Final_Recommendations.pdf. Accessed 24 Jan 2021.

  28. 28.

    National Institute for Health and Care Excellence (NICE). Type 2 diabetes in adults: Management—Evidence reviews for SGLT-2 inhibitors and GLP-1 mimetics (NG28). 2018. https://www.nice.org.uk/guidance/ng28/evidence/march-2018-evidence-reviews-for-sglt2-inhibitors-and-glp1-mimetics-pdf-4783687597. Accessed 24 Jan 2021.

  29. 29.

    Mt Hood Diabetes Challenges. Challenge session final instructions. 2019. https://www.mthooddiabeteschallenge.com/challenge-sessions. Accessed 24 Jan 2021.

  30. 30.

    Si L, Willis MS, Asseburg C, Nilsson A, Tew M, Clarke PM, et al. Evaluating the ability of economic models of diabetes to simulate new cardiovascular outcomes trials: a report on the ninth Mount Hood Diabetes Challenge. Value Health. 2020;23(9):1163–70.

    PubMed  Article  PubMed Central  Google Scholar 

  31. 31.

    McEwan P, Bennett H, Ward T, Bergenheim K. Refitting of the UKPDS 68 risk equations to contemporary routine clinical practice data in the UK. PharmacoEconomics. 2015;33(2):149–61.

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Matthews DR. Putting the UKPDS into perspective. Presented at: 54th EASD Annual Meeting; 1–5 October 2018; Berlin, Germany.

  33. 33.

    Gray A. Insights from the UKPDS Outcomes Model. Presented at: 54th EASD Annual Meeting; 1–5 October 2018; Berlin, Germany.

  34. 34.

    Basu S, Sussman JB, Berkowitz SA, Hayward RA, Yudkin JS. Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials. Lancet Diabetes Endocrinol. 2017;5(10):788–98.

    PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Shao H, Fonseca V, Stoecker C, Liu S, Shi L. Novel risk engine for diabetes progression and mortality in USA: building, relating, assessing, and validating outcomes (BRAVO). PharmacoEconomics. 2018;36(9):1125–34.

    PubMed  Article  Google Scholar 

  36. 36.

    Iannazzo S, Mannucci E, Reifsnider O, Maggioni AP. Cost-effectiveness analysis of empagliflozin in the treatment of patients with type 2 diabetes and established cardiovascular disease in Italy, based on the results of the EMPA-REG OUTCOME study. Farmeconomia Health Econ Therapeut Pathways. 2017;18:1.

    Google Scholar 

  37. 37.

    Moons KG, Kengne AP, Grobbee DE, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: II. External validation, model updating, and impact assessment. Heart (Br Cardiac Soc). 2012;98(9):691–8.

    Google Scholar 

  38. 38.

    Neal B, Perkovic V, Mahaffey KW, Fulcher G, Erondu N, Desai M, et al. Optimizing the analysis strategy for the CANVAS Program—a pre-specified plan for the integrated analyses of the CANVAS and CANVAS-R trials. Diabetes Obes Metab. 2017;19(7):926–35.

    PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Neal B, Perkovic V, de Zeeuw D, Mahaffey KW, Fulcher G, Stein P, et al. Rationale, design, and baseline characteristics of the Canagliflozin Cardiovascular Assessment Study (CANVAS)—a randomized placebo-controlled trial. Am Heart J. 2013;166(2):217–23.e11.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    American Diabetes Association. Standards of Medical Care in Diabetes—2018. Diabetes Care. 2018;41(suppl 1):S1–159.

  41. 41.

    Garber AJ, Abrahamson MJ, Barzilay JI, Blonde L, Bloomgarden ZT, Bush MA, et al. Consensus statement by the American Association of Clinical Endocrinologists and American College of Endocrinology on the comprehensive type 2 diabetes management algorithm—2018 Executive Summary. Endocrine Pract. 2018;24(1):91–120.

    Article  Google Scholar 

  42. 42.

    Ryden L, Grant PJ, Anker SD, Berne C, Cosentino F, Danchin N, et al. ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD: the Task Force on diabetes, pre-diabetes, and cardiovascular diseases of the European Society of Cardiology (ESC) and developed in collaboration with the European Association for the Study of Diabetes (EASD). Eur Heart J. 2013;34(39):3035–87.

    PubMed  Article  Google Scholar 

  43. 43.

    Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, et al. 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2016;18(8):891–975.

    PubMed  Article  Google Scholar 

  44. 44.

    Kidney Disease Outcomes Quality Initiative. KDOQI clinical practice guidelines and clinical practice recommendations for diabetes and chronic kidney disease. Am J Kidney Dis. 2007;49(2 Suppl 2):S12–154.

  45. 45.

    National Kidney Foundation. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012;60(5):850–86.

  46. 46.

    ERA-EDTA Guideline Development Group. Clinical Practice Guideline on management of patients with diabetes and chronic kidney disease stage 3b or higher (eGFR <45 mL/min). Nephrol Dialysis Transplant. 2015;30(Suppl 2):ii1–142.

  47. 47.

    Bozkurt B, Aguilar D, Deswal A, Dunbar SB, Francis GS, Horwich T, et al. Contributory risk and management of comorbidities of hypertension, obesity, diabetes mellitus, hyperlipidemia, and metabolic syndrome in chronic heart failure: a scientific statement from the American Heart Association. Circulation. 2016;134(23):e535–78.

    PubMed  Google Scholar 

  48. 48.

    Kengne AP, Masconi K, Mbanya VN, Lekoubou A, Echouffo-Tcheugui JB, Matsha TE. Risk predictive modelling for diabetes and cardiovascular disease. Crit Rev Clin Lab Sci. 2014;51(1):1–12.

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39(5):1–13.

    PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Tibshirani R, Bien J, Friedman J, Hastie T, Simon N, Taylor J, et al. Strong rules for discarding predictors in lasso-type problems. J R Stat Soc Ser B Stat Methodol. 2012;74(2):245–66.

    Article  Google Scholar 

  51. 51.

    Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67(2):301–20.

    Article  Google Scholar 

  52. 52.

    Inzucchi SE, Zinman B, Fitchett D, Wanner C, Ferrannini E, Schumacher M, et al. How does empagliflozin reduce cardiovascular mortality? Insights from a mediation analysis of the EMPA-REG OUTCOME Trial. Diabetes Care. 2018;41(2):356.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Hafeman DM. Confounding of indirect effects: a sensitivity analysis exploring the range of bias due to a cause common to both the mediator and the outcome. Am J Epidemiol. 2011;174(6):710–7.

    PubMed  Article  Google Scholar 

  54. 54.

    Kengne AP, Patel A, Colagiuri S, Heller S, Hamet P, Marre M, et al. The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes: the Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) study. Diabetologia. 2010;53(5):821–31.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    van der Heijden AA, Ortegon MM, Niessen LW, Nijpels G, Dekker JM. Prediction of coronary heart disease risk in a general, pre-diabetic, and diabetic population during 10 years of follow-up: accuracy of the Framingham, SCORE, and UKPDS risk functions: the Hoorn Study. Diabetes Care. 2009;32(11):2094–8.

    PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Tao L, Wilson EC, Griffin SJ, Simmons RK. Performance of the UKPDS outcomes model for prediction of myocardial infarction and stroke in the ADDITION-Europe trial cohort. Value Health. 2013;16(6):1074–80.

    PubMed  Article  Google Scholar 

  57. 57.

    van Dieren S, Beulens JW, Kengne AP, Peelen LM, Rutten GE, Woodward M, et al. Prediction models for the risk of cardiovascular disease in patients with type 2 diabetes: a systematic review. Heart (Br Cardiac Soc). 2012;98(5):360–9.

    Google Scholar 

  58. 58.

    Moons KG, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart (Br Cardiac Soc). 2012;98(9):683–90.

    Google Scholar 

  59. 59.

    Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJL, Charytan DM, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380(24):2295–306.

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

Editorial assistance was provided by Alaina Mitsch, PhD, of MedErgy, and was funded by Janssen Global Services, LLC. The authors wish to thank Shana Traina, PhD, of Janssen Global Services, LLC, for assistance with the methods of the analysis and for commenting on the manuscript.

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Correspondence to Michael Willis.

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Funding

This study was supported by Janssen Global Services, LLC.

Conflict of interest

MW and AN are employees of the Swedish Institute for Health Economics, which provides consulting services for governmental bodies, academic institutions, and commercial life science enterprises (including Janssen Global Services, LLC). CA was an employee of the Swedish Institute for Health Economics at the initiation of the study and is currently an employee of ESiOR Oy, which provides consulting services to governmental bodies, academic institutions, and commercial life science enterprises. AS is an employee of Axio Research, LLC. CN is an employee of Janssen Scientific Affairs, LLC.

Availability of Data and Material

Data from the CANVAS Program are available in the public domain via the Yale University Open Data Access Project (http://yoda.yale.edu/).

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Author Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all authors. The first draft of the manuscript was written by Michael Willis and Andreas Nilsson, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Willis, M., Asseburg, C., Slee, A. et al. Macrovascular Risk Equations Based on the CANVAS Program. PharmacoEconomics (2021). https://doi.org/10.1007/s40273-021-01001-0

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