尼泊尔 2 型糖尿病患者 10 年冠心病风险估计:弗雷明汉与英国前瞻性糖尿病研究》。

Daya Ram Pokharel, Dipendra Khadka, Manoj Sigdel, Naval Kishor Yadav, Lokendra Bahadur Sapkota, Ramchandra Kafle, Sarthak Nepal, Ravindra Mohan Sapkota, Niraj Choudhary
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背景:利用有效的风险预测功能预测未来冠心病(CHD)风险,有助于临床医生识别高风险糖尿病患者,并为他们提供适当的预防药物:目的:本研究的目的是使用两种最常见的风险预测函数估算和比较尼泊尔糖尿病患者的10年冠心病风险:患者和方法:我们对 524 名 2 型糖尿病患者进行了一项基于医院的横断面研究。我们记录了每位患者的基线和生化变量,并通过弗雷明汉和 UKPDS 风险预测功能估算了其患冠心病的风险。估算的风险分为低、中和高。使用卡帕统计、皮尔逊双变量相关性、布兰-阿尔特曼图和多元回归分析对估计的冠心病风险进行比较:弗雷明汉风险函数和 UKPDS 风险函数估计的平均 10 年慢性心肌梗死风险分别为 17.7 ± 12.1 和 16.8 ± 15(偏倚:0.88,P > 0.05),男性和年龄较大者的风险总是较高(P < 0.001)。这两种风险函数在预测冠心病风险方面显示出适度的趋同有效性,但在分层和解释患者的风险概况方面存在差异。弗雷明汉方程对通常在70岁以下的患者预测的风险较高,而且与他们目前的风险状况的关联性也优于UKPDS风险引擎:结论:根据预测的风险,尼泊尔糖尿病患者,尤其是风险因素增多的患者,未来罹患冠心病的风险较高。由于该研究是一项横断面研究,使用的是经过外部验证的风险函数,因此尼泊尔临床医生在做出预防冠心病治疗的重要医疗决策时,应谨慎使用这些函数,最好与其他指南结合使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Estimation of 10-Year Risk of Coronary Heart Disease in Nepalese Patients with Type 2 Diabetes: Framingham Versus United Kingdom Prospective Diabetes Study.

Background: Predicting future coronary heart disease (CHD) risk with the help of a validated risk prediction function helps clinicians identify diabetic patients at high risk and provide them with appropriate preventive medicine.

Aim: The aim of this study is to estimate and compare 10-year CHD risks of Nepalese diabetic patients using two most common risk prediction functions: The Framingham risk equation and United Kingdom Prospective Diabetes Study (UKPDS) risk engine that are yet to be validated for Nepalese population.

Patients and methods: We conducted a hospital-based, cross-sectional study on 524 patients with type 2 diabetes. Baseline and biochemical variables of individual patients were recorded and CHD risks were estimated by the Framingham and UKPDS risk prediction functions. Estimated risks were categorized as low, medium, and high. The estimated CHD risks were compared using kappa statistics, Pearson's bivariate correlation, Bland-Altman plots, and multiple regression analysis.

Results: The mean 10-year CHD risks estimated by the Framingham and UKPDS risk functions were 17.7 ± 12.1 and 16.8 ± 15 (bias: 0.88, P > 0.05), respectively, and were always higher in males and older age groups (P < 0.001). The two risk functions showed moderate convergent validity in predicting CHD risks, but differed in stratifying them and explaining the patients' risk profile. The Framingham equation predicted higher risk for patients usually below 70 years and showed better association with their current risk profile than the UKPDS risk engine.

Conclusions: Based on the predicted risk, Nepalese diabetic patients, particularly those associated with increased numbers of risk factors, bear higher risk of future CHDs. Since this study is a cross-sectional one and uses externally validated risk functions, Nepalese clinicians should use them with caution, and preferably in combination with other guidelines, while making important medical decisions in preventive therapy of CHD.

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