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Diabetes

The PRIME Type 2 Diabetes Model: a novel, patient-level model for estimating long-term clinical and cost outcomes in patients with type 2 diabetes mellitus

ORCID Icon, , , ORCID Icon &
Pages 393-402 | Received 27 Aug 2021, Accepted 25 Jan 2022, Published online: 21 Mar 2022

References

  • World Health Organization. Health Topics – Diabetes; [cited 2020 Nov 27]. Available from: https://www.who.int/health-topics/diabetes#tab=tab_1.
  • Khan MAB, Hashim MJ, King JK, Govender RD, et al. Epidemiology of type 2 diabetes – global burden of disease and forecasted trends. J Epidemiol Glob Health. 2020;10(1):107–111.
  • Bommer C, Sagalova V, Heesemann E, et al. Global economic burden of diabetes in adults: projections from 2015 to 2030. Diabetes Care. 2018;41(5):963–970.
  • UK Prospective Diabetes Study Group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes. Lancet. 1998;352(9131):837–853.
  • Holman RR, Paul SK, Bethel MA, et al. 10-Year follow-up of intensive glucose control in type 2 diabetes. N Engl J Med. 2008;359(15):1577–1589.
  • Cefalu WT, Kaul S, Gerstein HC, et al. Cardiovascular outcomes trials in type 2 diabetes: Where do We go from here? Reflections from a diabetes care editors’ expert forum. Dia Care. 2018;41(1):14–31.
  • Schnell O, Standl E, Cos X, et al. Report from the 5th cardiovascular outcome trial (CVOT) summit. Cardiovasc Diabetol. 2020;19(1):47.
  • Sattar N. Advances in the clinical management of type 2 diabetes: a brief history of the past 15 years and challenges for the future. BMC Med. 2019;17(1):1.
  • Palmer AJ, Si L, Tew M, et al. Computer modeling of diabetes and its transparency: a report on the eighth mount hood challenge. Value Health. 2018;21(6):724–731.
  • Si L, Willis MS, Asseburg C, 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):1163–1170.
  • Eddy DM, Hollingworth W, Caro JJ, et al. ISPOR-SMDM modeling good research practices task force. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7. Med Decis Making. 2012;32(5):733–743.
  • American diabetes association consensus panel. Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004;27(9):2262–2265.
  • Hayes AJ, Leal J, Gray AM, 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):1925–1933.
  • Briggs AH, Weinstein MC, Fenwick EA, et al. Paltiel AD; ISPOR-SMDM modeling good research practices task force. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group-6. Med Decis Making. 2012;32(5):722–732.
  • 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. 2018;36(9):1125–1134.
  • Yang X, So WY, Kong AP, et al. Development and validation of a total coronary heart disease risk score in type 2 diabetes mellitus. Am J Cardiol. 2008;101(5):596–601.
  • Yang X, Ma RC, So WY, et al. Development and validation of a risk score for hospitalization for heart failure in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2008;7(1):9.
  • Yang X, So WY, Kong AP, et al. Development and validation of stroke risk equation for Hong Kong chinese patients with type 2 diabetes: the Hong Kong diabetes registry. Diabetes Care. 2007;30(1):65–70.
  • Valentine WJ, Bae J, Boye K, et al. Predicting complications and long-term outcomes in type 1 diabetes: the PRIME Diabetes Model. European Association for the Study of Diabetes Annual Meeting 2015. Podium Presentation, Stockholm, Sweden. Abstract #882
  • Wang Y, Wu X, Mo X. A novel adaptive-weighted-average framework for blood glucose prediction. Diabetes Technol Ther. 2013;15(10):792–801.
  • Zoppini G, Targher G, Chonchol M, et al. Predictors of estimated GFR decline in patients with type 2 diabetes and preserved kidney function. Clin J Am Soc Nephrol. 2012;7(3):401–408.
  • Gower EW, Lovato JF, Ambrosius WT, et al. Lack of longitudinal association between thiazolidinediones and incidence and progression of diabetic eye disease: the ACCORD eye study. Am J Ophthalmol. 2018;187:138–147.
  • Hammes HP, Welp R, Kempe HP, et al. Holl RW; DPV initiative—german BMBF competence network diabetes mellitus. Risk factors for retinopathy and DME in type 2 diabetes-results from the german/Austrian DPV database. PLoS One. 2015;10(7):e0132492.
  • Wan X, Wang W, Liu J, et al. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135.
  • The Apache Software Foundation. Commons Math: The Apache Commons Mathematics Library; [cited 2020 Dec 1]. Available from: http://commons.apache.org/proper/commons-math/.
  • Matsumoto M, Nishimura T. Mersenne twister: a 623-Dimensionally equidistributed uniform Pseudo-Random number generator. ACM Trans Model Comput Simul. 1998;8(1):3–30.
  • Clarke PM, Gray AM, Briggs A, et al. UK prospective diabetes study (UKDPS) group. 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):1747–1759.
  • Valentine WJ, Pollock RF, Saunders R, et al. The PRIME diabetes model: Novel methods for estimating Long-Term clinical and cost outcomes in type 1 diabetes mellitus. Value in Health. 2017;20(7):985–991.
  • Gerstein HC, Miller ME, Byington RP, et al. Effects of intensive glucose lowering in Type 2 Diabetes. N Engl J Med. 2008;358(24):2545–2559.
  • Gerstein HC, Colhoun HM, Dagenais GR, et al. Dulaglutide and cardiovascular outcomes in type 2 diabetes (REWIND): a double-blind, randomised placebo-controlled trial. Lancet. 2019;394(10193):121–130.
  • Marso SP, Daniels GH, Brown-Frandsen K, et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N Engl J Med. 2016;375(4):311–322.
  • Zinman B, Wanner C, Lachin JM, et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015;373(22):2117–2128.
  • Marso SP, McGuire DK, Zinman B, et al. Efficacy and safety of degludec versus glargine in type 2 diabetes. N Engl J Med. 2017;377(8):723–732.
  • Fung CS, Wan EY, Wong CK, et al. Effect of metformin monotherapy on cardiovascular diseases and mortality: a retrospective cohort study on chinese type 2 diabetes mellitus patients. Cardiovasc Diabetol. 2015;14(1):137.
  • Wan EYF, Fung CSC, Jiao FF, et al. Five-year effectiveness of the multidisciplinary risk assessment and management Programme-Diabetes mellitus (RAMP-DM) on Diabetes-Related complications and health service Uses-A Population-Based and Propensity-Matched cohort study. Diabetes Care. 2018;41(1):49–59.
  • Shah AD, Langenberg C, Rapsomaniki E, et al. Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 19 million people. Lancet Diabetes Endocrinol. 2015;3(2):105–113.
  • Gaede P, Vedel P, Larsen N, et al. Multifactorial intervention and cardiovascular disease in patients with type 2 diabetes. N Engl J Med. 2003;348(5):383–393.
  • Gaede P, Lund-Andersen H, Parving H-H, et al. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med. 2008;358(6):580–591.
  • Alva ML, Gray A, Mihaylova B, et al. The impact of diabetes-related complications on healthcare costs: new results from the UKPDS (UKPDS 84). Diabet Med. 2015;32(4):459–466.
  • Bain SC, Bekker Hansen B, Hunt B, et al. Evaluating the burden of poor glycemic control associated with therapeutic inertia in patients with type 2 diabetes in the UK. J Med Econ. 2020;23(1):98–105.
  • National Institute for Health and Care Excellence. British National Formulary; [cited 2020 Dec 2]. Available from: https://bnf.nice.org.uk/.
  • Clarke P, Gray A, Holman R. Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making. 2002;22(4):340–349.
  • Bagust A, Beale S. Modelling EuroQol health-related utility values for diabetic complications from CODE-2 data. Health Econ. 2005;14(3):217–230.
  • Wasserfallen JB, Halabi G, Saudan P, et al. Quality of life on chronic dialysis: comparison between haemodialysis and peritoneal dialysis. Nephrol Dial Transplant. 2004;19(6):1594–1599.
  • National Institute for Health and Care Excellence. Guide to the methods of technology appraisal 2013; [cited 2020 Dec 1]. Available from: https://www.nice.org.uk/process/pmg9/resources/guide-to-the-methods-of-technology-appraisal-2013-pdf-2007975843781.
  • Caro JJ, Briggs AH, Siebert U, et al. ISPOR-SMDM modeling good research practices task force. Modeling good research practices–overview: a report of the ISPOR-SMDM modeling good research practices task force–1. Value Health. 2012;15(6):796–803.
  • van Dieren S, Beulens JWJ, 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):360–369.
  • Laxy M, Schöning VM, Kurz C, et al. Performance of the UKPDS outcomes model 2 for predicting death and cardiovascular events in patients with type 2 diabetes mellitus from a german population-based cohort. Pharmacoeconomics. 2019;37(12):1485–1494.
  • Carlsson AC, Wändell P, Ösby U, et al. High prevalence of diagnosis of diabetes, depression, anxiety, hypertension, asthma and COPD in the total population of Stockholm, Sweden – a challenge for public health. BMC Public Health. 2013;13(1):670.
  • Farmer AJ, Stevens R, Hirst J, et al. Optimal strategies for identifying kidney disease in diabetes: properties of screening tests, progression of renal dysfunction and impact of treatment – systematic review and modelling of progression and cost-effectiveness. Health Tech Assess. 2014;18(14):1–128.