Variables included in the final model were duration of diabetes, age, sex, smoking, systolic blood pressure, total cholesterol to high-density lipoprotein cholesterol ratio and presence of atrial fibrillation. Diagnostic plots were used to compare survival probabilities calculated by the model with those calculated using nonparametric methods. Model fitting was carried out by maximum likelihood estimation using the Newton-Raphson method. We developed mathematical models to estimate the risk of a first stroke using data from 4549 newly diagnosed type 2 diabetic patients enrolled in the UK Prospective Diabetes Study.ĭuring 30 700 person-years of follow-up, 188 first strokes (52 fatal) occurred. Relative risks have been examined in earlier work, but there is no readily available method for predicting the absolute risk of stroke in a diabetic individual. Even though hsCRP is known to predict cardiovascular events like ischemic stroke and myocardial infarction, 31) hsCRP failed to be an independent risk factor for early carotid atherosclerosis.People with type 2 diabetes are at elevated risk of stroke compared with those without diabetes. But they compared the predictability between the FRS and the UKPDS based on the high sensitivity C-reactive protein (hsCRP). This result is vastly different to our results. In a study that examined Korean patients with type 2 diabetes, 30) the CVD risk was better assessed using the FRS than using the UKPDS. The AUCs of the FRS and UKPDS risk engine were 0.61 and 0.56, respectively, with no significant difference between them. 29) recruited 199 asymptomatic type 2 diabetes patients from the community and a hospital out-patient clinic in Australia and compared the predictability of the FRS with that of the UKPDS risk engine, based on exercise electrocardiography findings. In a cross-sectional study, Rakhit et al. Others compared the predictability using CVD surrogate mea sure similar to our study. Similar to our results, the few prospective studies comparing risk scores for diabetes patients have reported no significant difference among risk scores. The AUCs of the FRS and UKPDS scores for known diabetes patients were 0.60 and 0.61 and there was no significant difference. They observed the subjects for 10.5 years and compared the predictability of the FRS with that of UKPDS risk engine. 28) conducted a population-based cohort study of 9,000 people who presented to general practices for health exams in the United Kingdom. 27) compared the predictability of the FRS and SCORE with the UKPDS in 5,102 newly diagnosed type 2 patients and reported that the AUCs of the FRS and SCORE were similar (0.76 and 0.77, respectively). They compared the predictability of the FRS with that of the UKPDS risk engine and reported that the AUCs of the FRS and UKPDS were 0.657 and 0.670, respectively. 26) investigated a cohort composed of 428 newly diagnosed type 2 diabetes patients without clinically evident CVD in the United Kingdom for 4.2 years. Some researchers studied the predictability based on the observation of CVD events. The UKPDS includes the duration of diabetes and glycated hemoglobin (HbA 1c) level as variables, thus allowing it address the risk of CVD specifically in diabetes patients.Īdditional studies have compared the predictability among risk scores. 15) In 2001, the United Kingdom Prospective Diabetes Study (UKPDS) risk engine was published based on data from 5,102 newly diagnosed type 2 patients who were followed-up for an average of 10.4 years. 12- 14) To correct this overestimation, the Systematic Coronary Risk Evaluation (SCORE) project was initiated to develop an appropriate risk estimation method for the general European population. 11) On the other hand, some European studies have reported that the FRS overestimates CVD risk in the general European population. Because very few diabetes patients were included in this previous study, some uncertainty remains according the accuracy of the FRS to predict CVD risk in diabetes patients. The Framingham Risk Score (FRS) was developed to predict the incident risk of CVD according to age, low-density lipoprotein (LDL) levels, high-density lipoprotein (HDL) levels, smoking, and hypertension. 10) estimated the risk of CVD using data from the Framingham heart study.
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