Abstract
Background
There is an urgent need to update diabetes prediction, which has relied on the United Kingdom Prospective Diabetes Study (UKPDS) that dates back to 1970 s’ European populations.
Objective
The objective of this study was to develop a risk engine with multiple risk equations using a recent patient cohort with type 2 diabetes mellitus reflective of the US population.
Methods
A total of 17 risk equations for predicting diabetes-related microvascular and macrovascular events, hypoglycemia, mortality, and progression of diabetes risk factors were estimated using the data from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (n = 10,251). Internal and external validation processes were used to assess performance of the Building, Relating, Assessing, and Validating Outcomes (BRAVO) risk engine. One-way sensitivity analysis was conducted to examine the impact of risk factors on mortality at the population level.
Results
The BRAVO risk engine added several risk factors including severe hypoglycemia and common US racial/ethnicity categories compared with the UKPDS risk engine. The BRAVO risk engine also modeled mortality escalation associated with intensive glycemic control (i.e., glycosylated hemoglobin < 6.5%). External validation showed a good prediction power on 28 endpoints observed from other clinical trials (slope = 1.071, R2 = 0.86).
Conclusion
The BRAVO risk engine for the US diabetes cohort provides an alternative to the UKPDS risk engine. It can be applied to assist clinical and policy decision making such as cost-effective resource allocation in USA.
Similar content being viewed by others
Change history
16 May 2019
The original article can be found online.
16 May 2019
The original article can be found online.
References
American Diabetes Association. Economic costs of diabetes in the U.S. in 2007. Diabetes Care. 2008;31(3):596–615.
Menke A, Casagrande S, Geiss L, et al. Prevalence of and trends in diabetes among adults in the United States, 1988-2012. JAMA. 2015;314(10):1021–9.
Boyle JP, Thompson TJ, Gregg EW, et al. Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr. 2010;8(1):29.
American Diabetes Association. Economic costs of diabetes in the US in 2012. Diabetes Care. 2013;36(4):1033–46.
Uusitupa M, Siitonen O, Aro A, et al. Prevalence of coronary heart disease, left ventricular failure and hypertension in middle-aged, newly diagnosed type 2 (non-insulin-dependent) diabetic subjects. Diabetologia. 1985;28(1):22–7.
Fowler MJ. Microvascular and macrovascular complications of diabetes. Clin Diabetes. 2008;26(2):77–82.
Palmer AJ, M.H.M. Group. Computer modeling of diabetes and its complications: a report on the Fifth Mount Hood challenge meeting. Value Health. 2013;16(4):670–85.
McEwen LN, Karter AJ, Waitzfelder BE, et al. Predictors of mortality over 8 years in type 2 diabetic patients: Translating Research Into Action for Diabetes (TRIAD). Diabetes Care. 2012;35(6):1301–9.
De Cosmo S, Copetti M, Lamacchia O, et al. Development and validation of a predicting model of all-cause mortality in patients with type 2 diabetes. Diabetes Care. 2013;36(9):2830–5.
McEwan P, Bennett H, Ward T, et al. Refitting of the UKPDS 68 risk equations to contemporary routine clinical practice data in the UK. Pharmacoeconomics. 2015;33(2):149–61.
Erhardt W, Bergenheim K, Duprat-Lomon I, et al. Cost effectiveness of saxagliptin and metformin versus sulfonylurea and metformin in the treatment of type 2 diabetes mellitus in Germany: a Cardiff diabetes model analysis. Clin Drug Investig. 2012;32(3):189–202.
Schwarz B, Gouveia M, Chen J, et al. Cost-effectiveness of sitagliptin-based treatment regimens in European patients with type 2 diabetes and haemoglobin A1c above target on metformin monotherapy. Diabetes Obes Metab. 2008;10(Suppl. 1):43–55.
Shafie AA, Gupta V, Baabbad R, et al. An analysis of the short- and long-term cost-effectiveness of starting biphasic insulin aspart 30 in insulin-naive people with poorly controlled type 2 diabetes. Diabetes Res Clin Pract. 2014;106(2):319–27.
Palmer AJ, Roze S, Valentine WJ, et al. Impact of changes in HbA1c, lipids and blood pressure on long-term outcomes in type 2 diabetes patients: an analysis using the CORE Diabetes Model. Curr Med Res Opin. 2004;20(S1):S53–8.
Charokopou M, McEwan P, Lister S, et al. The cost-effectiveness of dapagliflozin versus sulfonylurea as an add-on to metformin in the treatment of type 2 diabetes mellitus. Diabet Med. 2015;32(7):890–8.
Elgart JF, Caporale JE, Gonzalez L, et al. Treatment of type 2 diabetes with saxagliptin: a pharmacoeconomic evaluation in Argentina. Health Econ Rev. 2013;3(1):11.
Hoerger TJ, Zhang P, Segel JE, et al. Cost-effectiveness of bariatric surgery for severely obese adults with diabetes. Diabetes Care. 2010;33(9):1933–9.
CDC, Diabetes Cost-Effectiveness Group. Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA. 2002;287(19):2542–51.
Hoerger TJ, Zhang P, Segel JE, et al. Improvements in risk factor control among persons with diabetes in the United States: evidence and implications for remaining life expectancy. Diabetes Res Clin Pract. 2009;86(3):225–32.
Clarke P, Gray A, Briggs A, 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.
Gerstein HC, Riddle MC, Kendall DM, et al. Glycemia treatment strategies in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Am J Cardiol. 2007;99(12A):34i–43i.
Baser OHA, Li L, Wang L. Obese patients in the veteran population in the united states: a health care cost and utilization analysis. Value Health. 2013;16(3):A110.
Buse JB. Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial: design and methods. Am J Cardiol. 2007;99(12):S21–33.
Buse JB, A.S. Group. Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial: design and methods. Am J Cardiol. 2007;99(12):S21–33.
Barhak J, Isaman DJ, Ye W, et al. Chronic disease modeling and simulation software. J Biomed Inform. 2010;43(5):791–9.
Zhou H, Isaman DJM, Messinger S, et al. A computer simulation model of diabetes progression, quality of life, and cost. Diabetes Care. 2005;28(12):2856–63.
Fishman G. Discrete-event simulation: modeling, programming, and analysis. Berlin: Springer Science & Business Media; 2013.
Gerds TA, Schumacher M. Consistent estimation of the expected Brier score in general survival models with right-censored event times. Biom J. 2006;48(6):1029–40.
Subramanian J, Simon R. Overfitting in prediction models: is it a problem only in high dimensions? Contemp Clin Trials. 2013;36(2):636–41.
Mount H. Computer modeling of diabetes and its complications: a report on the Fourth Mount Hood Challenge Meeting. Diabetes Care. 2007;30(6):1638.
Hayes A, Leal J, Gray A, et al. 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.
Caro JJ, Möller J, Getsios D. Discrete event simulation: the preferred technique for health economic evaluations? Value Health. 2010;13(8):1056–60.
Gerstein HC, Miller ME, Ismail-Beigi F, et al. Effects of intensive glycaemic control on ischaemic heart disease: analysis of data from the randomised, controlled ACCORD trial. Lancet. 2014;384(9958):1936–41.
Ismail-Beigi F, Craven T, Banerji MA, et al. Effect of intensive treatment of hyperglycaemia on microvascular outcomes in type 2 diabetes: an analysis of the ACCORD randomised trial. Lancet. 2010;376(9739):419–30.
Zoungas S, Patel A, Chalmers J, et al. Severe hypoglycemia and risks of vascular events and death. N Engl J Med. 2010;363(15):1410–8.
Zhao Y, Campbell CR, Fonseca V, et al. Impact of hypoglycemia associated with antihyperglycemic medications on vascular risks in veterans with type 2 diabetes. Diabetes Care. 2012;35(5):1126–32.
Shi L, Shao H, Zhao Y, et al. Is hypoglycemia fear independently associated with health-related quality of life? Health Qual Life Outcomes. 2014;12(1):167.
Karter AJ, Ferrara A, Liu JY, et al. Ethnic disparities in diabetic complications in an insured population. JAMA. 2002;287(19):2519–27.
Riddle MC, Ambrosius WT, Brillon DJ, et al. Epidemiologic relationships between A1C and all-cause mortality during a median 3.4-year follow-up of glycemic treatment in the ACCORD trial. Diabetes Care. 2010;33(5):983–90.
Jackson CH. flexsurv: a platform for parametric survival modelling in R. J Stat Softw. 2016;70(8):1–33.
Basu S, Sussman JB, Berkowitz SA, et al. 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.
National Heart Lung and Blood Institute. Action to Control Cardiovascular Risk in Diabetes (ACCORD) data. Available from: https://biolincc.nhlbi.nih.gov/studies/accord/. Accessed 9 Apr 2018.
Author information
Authors and Affiliations
Contributions
Hui Shao analyzed the data for developing the BRAOVO risk engine with both internal and external validation and drafted the manuscript. Vivian Fonseca and Shuqian Liu provided clinical interpretation to support the BRAVO risk engine. Charles Stoecker reviewed the econometrics in the risk engine during the model development. Lizheng Shi as the principal investigator initiated the project and worked with Hui Shao in developing the BRAVO risk engine and the manuscript. All authors are extensively involved in writing the manuscript.
Corresponding author
Ethics declarations
Funding
No sources of funding were received for the preparation of this article.
Conflict of interest
Hui Shao, Vivian Fonseca, Charles Stoecker, Shuqian Liu, and Lizheng Shi have no conflicts of interest directly relevant to the content of this article.
Data availability
The datasets used for this study are publicly available and can be requested through the National Heart, Lung, and Blood Institute [42].
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Shao, H., Fonseca, V., Stoecker, C. et al. Novel Risk Engine for Diabetes Progression and Mortality in USA: Building, Relating, Assessing, and Validating Outcomes (BRAVO). PharmacoEconomics 36, 1125–1134 (2018). https://doi.org/10.1007/s40273-018-0662-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40273-018-0662-1