Prediction of Ten-Year Cardiovascular Disease Risk in People with Type 2 Diabetes Mellitus: Derivation in Nanjing, China and External Validation in Scotland, UK

Objective: To use routinely collected data to develop a 10-year cardiovascular disease(CVD) risk prediction model for Chinese adults with type 2 diabetes with validation of its performance in a population of European ancestry. Research Design and Methods: People with incident type 2 diabetes and no history of CVD at diagnosis of diabetes between 2008 and 2017 were included in derivation and validation cohorts. The derivation cohort was identified from a pseudonymised research extract of data from the First Affiliated Hospital of Nanjing Medical University(NMU). Ten-year risk of CVD was estimated using basic and extended Cox proportional hazards regression models including six and 11 predictors respectively. The risk prediction models were internally validated by a bootstrap approach and externally validated in a Scottish population-based cohort with CVD events identified from linked hospital records. Discrimination and calibration were assessed using Harrell’s C statistic and calibration plots, respectively. Results: Mean age of the derivation and validation cohorts were 58.4 and 59.2 years, with 53.5% and 56.9% men respectively. During a median follow-up time of 4.75 [2.67, 7.42] years, 18,827 (22.25%) of the 84,630 people in the NMU cohort and 8,763(7.31%) of the Scottish cohort of 119,891 people developed CVD. The extended model had a C statistic of 0.723[0.721-0.724] in internal validation and 0.716 [0.713-0.719] in external validation. Conclusions: It is possible to generate a risk prediction model with moderate discriminative power in internal and external validation derived from routinely collected Chinese hospital data. The proposed risk score in this paper could be used to improve CVD prevention in people with diabetes. Funding Statement: This work was supported by grants from the National key Research & Development Plan of the Ministry of Science and Technology of the People’s Republic of China (Grant no. 2018YFC1314900, 2018YFC1314901), 2019 Provincial Special Guide Fund Project for the Development of Modern Service Industry (2019 (783) ), the 2018 Projects of Jiangsu Province Department of Industry and Information Technology(Grant no. 2018419) and the 2016 Projects of Nanjing Science Bureau (Grant no. 201608003). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Approval for generation and analysis of the linked data set was provided by the Jiangsu Province Hospitals and the Nanjing Medical University ethics committees.