Valipour Mehrdad, Khalili Davood, Solaymani-Dodaran Masoud, Motevalian Seyed Abbas, Khamseh Mohammad Ebrahim, Baradaran Hamid Reza
Department of Epidemiology, School of Public Heath, Iran University of Medical Sciences, Tehran, Iran.
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Diabetes Metab Disord. 2023 May 25;22(2):1145-1150. doi: 10.1007/s40200-023-01224-2. eCollection 2023 Dec.
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.
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.
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.
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.
心血管疾病是全球首要的死亡原因。实用指南建议准确评估这些事件的风险，以便进行有效的治疗和护理。英国前瞻性糖尿病研究（UKPDS）有一个用于预测2型糖尿病患者冠心病风险的风险评估工具，但在一些国家，已表明对冠心病风险的估计较差。因此，我们评估了UKPDS风险评估工具在伊朗国家糖尿病项目中确诊的2型糖尿病患者中的外部效度。
该队列包括2007年3月21日至2018年3月20日在伊朗洛雷斯坦省确诊的853例2型糖尿病患者。对患者进行冠心病发病率随访。从区分度和校准度方面评估模型的性能。使用c统计量检验区分度，并用Hosmer-Lemeshow卡方统计量（HLχ2）检验评估校准度，并绘制校准图以显示预测风险与观察到的风险。
在7464.5人年的随访期间，发生了170例首次冠心病事件。中位随访时间为8.6年。UKPDS风险评估工具对冠心病的区分度中等（10年风险的c统计量为0.72），UKPDS风险评估工具的校准度较差（HLχ2 = 69.9，p < 0.001），UKPDS风险评估工具高估了伊朗国家糖尿病项目中确诊的2型糖尿病患者患心脏病的风险78%。
本研究表明，UKPDS风险评估工具区分发生冠心病事件患者与未发生者的能力中等；风险预测模型准确预测冠心病绝对风险（校准度）的能力较差，且高估了冠心病风险。为改善对2型糖尿病患者冠心病的预测，该模型应在伊朗糖尿病患者群体中更新。
