Estimation of 10-year risk of coronary heart disease in nepalese patients with type 2 diabetes: Framingham versus United Kingdom prospective diabetes study

Abstract

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.

Prevalence and pattern of dyslipidemia in Nepalese individuals with type 2 diabetes

“…As dyslipidemia is a well-established risk factor for cardiovascular diseases, presence of other co-variate risk factors results significantly higher risk of future CVD. This is in agreement with our earlier studies which have estimated higher risk of 10-year CHD among Nepalese type diabetic patients [43]. These findings warrant extensive preventive approaches, both clinical and non-clinical, to treat all types of dyslipidemia to minimize the future risk of CVD in our patients.…”

“…The clustering of many of these risk factors often manifests as metabolic syndrome that precedes and then continues with the diabetes [42]. We have previously shown that Nepalese type 2 diabetic patients have high prevalence of metabolic syndrome and increased risk of CHD [43]. We also explored the covariate risk factors that were independently associated with dyslipidemia in our patients.…”

Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes

“…Also, in our examination the UKPDS risk engine score classified 29.4% of individuals with DM2 at the high risk group. Higher risk in males was also found in other studies [5, 9]. Similarly, Kim et al [22] reported 24% of subjects at high CVD risk when using the UKPDS risk score algorithm in Korean adults with DM2.…”

“…In line with this, there are discrepant results obtained from many studies, showing that the different CVD risk algorithms have variable precision in different populations when distinguishing subjects who are at high-risk from the other ones [7, 8, 9, 10, 11, 12]. Moreover, a weak concordance between predicted and actual cardiovascular risk was also reported [13].…”

A Tiered Approach On Dimensional Reduction Process for Prediction of Coronary Heart Disease

“…Determination of threshold, which was the percentage of the prediction of coronary heart disease refers to several considerations that were: a. the influence of a threshold value on the performance at level 1, level 2 and level 3, b. At the first stage is the screening stage, thus emphasizing the sensitivity performance parameter [8], c. refers to the percentage predicted incidence of coronary heart disease in the next 10 years, if values are <10% said to have low risk [18]. A number of these considerations used to analyze the results of testing at level 1.…”