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
Background and Objective
Accurate prediction of relevant outcomes is important for targeting therapies and to support health economic evaluations of healthcare interventions in patients with diabetes. The United Kingdom Prospective Diabetes Study (UKPDS) risk equations are some of the most frequently used risk equations. This study aims to analyze the calibration and discrimination of the updated UKPDS risk equations as implemented in the UKPDS Outcomes Model 2 (UKPDS-OM2) for predicting cardiovascular (CV) events and death in patients with type 2 diabetes mellitus (T2DM) from population-based German samples.
Analyses are based on data of 456 individuals diagnosed with T2DM who participated in two population-based studies in southern Germany (KORA (Cooperative Health Research in the Region of Augsburg)-A: 1997/1998, n = 178; KORA-S4: 1999–2001, n = 278). We compared the participants’ 10-year observed incidence of mortality, CV mortality, myocardial infarction (MI), and stroke with the predicted event rate of the UKPDS-OM2. The model’s calibration was evaluated by Greenwood–Nam–D’Agostino tests and discrimination was evaluated by C-statistics.
Of the 456 participants with T2DM (mean age 65 years, mean diabetes duration 8 years, 56% male), over the 10-year follow-up time 129 died (61 due to CV events), 64 experienced an MI, and 46 a stroke. The UKPDS-OM2 significantly over-predicted mortality and CV mortality by 25% and 28%, respectively (Greenwood–Nam–D’Agostino tests: p < 0.01), but there was no significant difference between predicted and observed MI and stroke risk. The model poorly discriminated for death (C-statistic [95% confidence interval] = 0.64 [0.60–0.69]), CV death (0.64 [0.58–0.71]), and MI (0.58 [0.52–0.66]), and failed to discriminate for stroke (0.57 [0.47–0.66]).
The study results demonstrate acceptable calibration and poor discrimination of the UKPDS-OM2 for predicting death and CV events in this population-based German sample. Those limitations should be considered when using the UKPDS-OM2 for economic evaluations of healthcare strategies or using the risk equations for clinical decision-making.
ML planned the analysis, supervised analysis of the data, and drafted the manuscript. VMS analyzed the data and was a major contributor to the draft of the manuscript. CK consulted on the data analysis and commented on the manuscript draft. RH consulted on the data analysis, was involved in the data collection, and commented on the manuscript draft. AP, CM, WR, and KM were involved in the data collection and commented on the manuscript draft. KK was a major contributor to the draft of the manuscript.
Compliance with Ethical Standards
Conflict of interest
Michael Laxy, Verena M. Schöning, Christoph Kurz, Rolf Holle, Annette Peters, Christa Meisinger, Wolfgang Rathmann, Kristin Mühlenbruch, and Katharina Kähm declare that they have no competing interests.
The KORA (Cooperative Health Research in the Region of Augsburg) research platform was initiated and financed by the Helmholtz Zentrum München (the German Research Center for Environmental Health), which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC Health), Ludwig-Maximilians-Universität (LMU), as part of the LMUinnovative.
- 6.National Institute for Health and Care Excellence. Lipid-modifying drugs; 2017. https://www.nice.org.uk/advice/ktt3/chapter/evidence-context#statins. Accessed 3 Sept 2018.
- 7.Chou R, Dana T, Blazina I, Daeges M, Bougatsos C, Grusing S, et al. Statin use for the prevention of cardiovascular disease in adults: a systematic review for the U.S. Preventive Services Task Force. Rockville: Agency for Healthcare Research and Quality (US); 2016. (Evidence Syntheses, No. 139). https://www.ncbi.nlm.nih.gov/books/NBK396415/. Accessed 3 Sept 2018.
- 8.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.Google Scholar
- 10.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.CrossRefGoogle Scholar
- 15.Kengne AP, Patel A, Colagiuri S, Heller S, Hamet P, Marre M, ADVANCE Collaborative Group, 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.CrossRefGoogle Scholar
- 16.Diabetes Trials Unit, H.E.R.C. UKPDS Outcomes Model user manual. Version 2; 2015. https://www.dtu.ox.ac.uk/outcomesmodel/OM2Manual.pdf. Accessed 26 July 2017.
- 25.Laxy M, Mielck A, Hunger M, Schunk M, Meisinger C, Ruckert IM, et al. The association between patient-reported self-management behavior, intermediate clinical outcomes, and mortality in patients with type 2 diabetes: results from the KORA-A study. Diabetes Care. 2014;37(6):1604–12.CrossRefGoogle Scholar
- 27.Kuhfeld W, So Y. Creating and customizing the Kaplan–Meier survival plot in PROC LIFETEST in SAS Global Forum Statistics and Data Analysis. Paper 427. 2013. https://support.sas.com/resources/papers/proceedings13/427-2013.pdf. Accessed 24 June 2019.