title: Development and validation of a total coronary heart disease risk score in type 2 diabetes mellitus.
abstract: There are no validated risk scores for predicting coronary heart disease (CHD) in Chinese patients with type 2 diabetes mellitus. This study aimed to validate the UKPDS risk engine and, if indicated, develop CHD risk scores. A total of 7,067 patients without CHD at baseline were analyzed. Data were randomly assigned to a training data set and a test data set. Cox models were used to develop risk scores to predict total CHD in the training data set. Calibration was assessed using the Hosmer-Lemeshow test, and discrimination was examined using the area under the receiver-operating characteristic curve in the test data set. During a median follow-up of 5.40 years, 4.97% of patients (n = 351) developed incident CHD. The UKPDS CHD risk engine overestimated the risk of CHD with suboptimal discrimination, and a new total CHD risk score was developed. The developed total CHD risk score was 0.0267 x age (years) - 0.3536 x sex (1 if female) + 0.4373 x current smoking status (1 if yes) + 0.0403 x duration of diabetes (years) - 0.4808 x Log(10) (estimated glomerular filtration rate [ml/min/1.73 m(2)]) + 0.1232 x Log(10) (1 + spot urinary albumin-creatinine ratio [mg/mmol]) + 0.2644 x non-high-density lipoprotein cholesterol (mmol/L). The 5-year probability of CHD = 1 - 0.9616(EXP(0.9440 x [RISK SCORE - 0.7082])). Predicted CHD probability was not significantly different from observed total CHD probability, and the adjusted area under the receiver-operating characteristic curve was 0.74 during 5 years of follow-up. In conclusion, the UKPDS CHD risk engine overestimated the risk of Chinese patients with type 2 diabetes mellitus and the newly developed total CHD risk score performed well in the test data set. External validations are required in other Chinese populations.
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, SAR, China.
authors.forename: Xilin
authors.initials: X
authors.lastname: Yang
authors.forename: Wing-Yee
authors.initials: WY
authors.lastname: So
authors.forename: Alice P S
authors.initials: AP
authors.lastname: Kong
authors.forename: Ronald C W
authors.initials: RC
authors.lastname: Ma
authors.forename: Gary T C
authors.initials: GT
authors.lastname: Ko
authors.forename: Chung-Shun
authors.initials: CS
authors.lastname: Ho
authors.forename: Christopher W K
authors.initials: CW
authors.lastname: Lam
authors.forename: Clive S
authors.initials: CS
authors.lastname: Cockram
authors.forename: Juliana C N
authors.initials: JC
authors.lastname: Chan
authors.forename: Peter C Y
authors.initials: PC
authors.lastname: Tong
mesh_terms: D000367:Age Factors; D000419:Albuminuria / Q000453:epidemiology; D044466:Asian People; D008076:Cholesterol, HDL / Q000097:blood; D003327:Coronary Disease / Q000453:epidemiology*; D003404:Creatinine / Q000652:urine; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology*; D005260:Female; D005919:Glomerular Filtration Rate; D006442:Glycated Hemoglobin / Q000032:analysis; D006723:Hong Kong / Q000453:epidemiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment; D012907:Smoking / Q000453:epidemiology; D013997:Time Factors
chemical_list: D008076:Cholesterol, HDL; D003404:Creatinine; D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human