Table 2 shows the distribution of the population into high risk and non-high risk groups using thresholds of 20% on both FRS and UKPDS risk engine. Lowering the cut-point to 15% did not increase agreement between the two charts. At this cut-point, the number of high risk cases identified by the two scoring systems was found to be similar ( P<0.001). The highest agreement was observed for a threshold of 20% of both the scores. The κ index increased as the cut-point for high risk decreased on the UKPDS risk engine. The κ indices between the FRS and UKPDS risk engine are also shown. The proportion of high risk subjects on FRS with high risk cut off at 20% and the UKPDS risk engine at various cut-offs is shown in Fig. When a cut-point of 25% was used, the proportion of high risk subjects on the UKPDS risk engine increased to 12.7%. Using a threshold of 20%, 20.9% of the sample was classified as high risk using the FRS compared to 7.2% using UKPDS risk engine with cut-off at 30%. The sociodemographic and clinical characteristics of patients were obtained from the medical records available with the subjects and behavioural factors by personal in-terviews. Pregnant and lactating women and patients with prior CVD were excluded.
All patients provided written informed consent before their recruitment in the study. The patients were enrolled if they had necessary data in their medical files to calculate FRS and UKPDS and were also ready to be interviewed for additional parameters. Consecutive patients of either sex, aged 18 to 75 years visiting endocrinology outpatient clinic of the hospital and newly diagnosed with T2DM (≤6 months duration of diagnosis) were eligible for study enrolment. The study proceeded following the approval from the Institute Ethics Committee (PGIMER, Chandigarh, India). Subjects for the present study are drawn out of the baseline cross-sectional data from an ongoing study that is aimed to assess the performance of FRS and UKPDS risk engine in a public tertiary care hospital in North India. The Framingham risk assessment tool has been validated in whites and blacks in the United States and later modified so that they are valid to culturally diverse populations in Europe, the Mediterranean region and Asia. For example, the Framingham formulation for predicting CHD was incorporated into the Third Report of the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). This understanding has led to the development of multivariable risk prediction models incorporating various risk factors and can be utilized by clinicians for assessing individual subject for the risk of developing CVD or specific components of CVD, i.e., coronary heart disease (CHD), peripheral vascular disease, or stroke. It is a well accepted fact that cardiovascular disease (CVD) risk factors such as smoking, dyslipidemia, and diabetes cluster together and interact multiplicatively to enhance vascular risk.
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