External validation of the UK prospective diabetes study (UKPDS) risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran

Mehrdad Valipour; Davood Khalili; Masoud Solaymani-Dodaran; Seyed Abbas Motevalian; Mohammad Ebrahim Khamseh; Hamid Reza Baradaran

doi:10.1007/s40200-023-01224-2

External validation of the UK prospective diabetes study (UKPDS) risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran

J Diabetes Metab Disord. 2023 May 25;22(2):1145-1150. doi: 10.1007/s40200-023-01224-2. eCollection 2023 Dec.

Authors

Mehrdad Valipour¹, Davood Khalili², Masoud Solaymani-Dodaran¹, Seyed Abbas Motevalian¹, Mohammad Ebrahim Khamseh³, Hamid Reza Baradaran^{1

3}

Affiliations

¹ Department of Epidemiology, School of Public Heath, Iran University of Medical Sciences, Tehran, Iran.
² Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
³ Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences, Tehran, Iran.

Abstract

Background: Cardiovascular diseases are the first leading cause of mortality in the world. Practical guidelines recommend an accurate estimation of the risk of these events for effective treatment and care. The UK Prospective Diabetes Study (UKPDS) has a risk engine for predicting CHD risk in patients with type 2 diabetes, but in some countries, it has been shown that the risk of CHD is poorly estimated. Hence, we assessed the external validity of the UKPDS risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran.

Methods: The cohort included 853 patients with type 2diabetes identified between March 21, 2007, and March 20, 2018 in Lorestan province of Iran. Patients were followed for the incidence of CHD. The performance of the models was assessed in terms of discrimination and calibration. Discrimination was examined using the c-statistic and calibration was assessed with the Hosmer-Lemeshow χ2 statistic (HLχ2) test and a calibration plot was depicted to show the predicted risks versus observed ones.

Results: During 7464.5 person-years of follow-up 170 first Coronary heart disease occurred. The median follow-up was 8.6 years. The UKPDS risk engine showed moderate discrimination for CHD (c-statistic was 0.72 for 10-year risk) and the calibration of the UKPDS risk engine was poor (HLχ2 = 69.9, p < 0.001) and the UKPDS risk engine78% overestimated the risk of heart disease in patients with type 2 diabetes identified in the national diabetes program in Iran.

Conclusion: This study shows that the ability of the UKPDS Risk Engine to discriminate patients who developed CHD events from those who did not; was moderate and the ability of the risk prediction model to accurately predict the absolute risk of CHD (calibration) was poor and it overestimated the CHD risk. To improve the prediction of CHD in patients with type 2 diabetes, this model should be updated in the Iranian diabetic population.

Keywords: Cardiovascular disease; Coronary heart disease; Risk prediction; Risk score; Type 2 diabetes mellitus; United Kingdom prospective diabetes study.

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