AIM: The aim of this study was to investigate whether the Indian Diabetes Risk Score (IDRS) could assist in classifying type 2 diabetes mellitus (T2DM) and non-T2DM among patients attending clinics in India. METHODS: Patient records from 2006 through 2009 were taken from the clinical database of a tertiary care diabetes hospital in Chennai, Southern India. A total of 8747 patients with diabetes, diagnosed by a physician either as type 1 diabetes mellitus (T1DM), T2DM, or other types were included for analysis. The IDRS, based on age, abdominal obesity, family history of diabetes, and physical activity, was calculated for each patient at first visit to our clinic. Receiver operating characteristic (ROC) curves were generated to obtain optimal IDRS cut points for predicting T2DM and non-T2DM. RESULTS: Of the 8747 patient records analyzed, 204 (2.3%) were classified as non-T2DM and 8543 (97.7%) as T2DM. In ROC analysis, an IDRS ≥60 [area under the curve (AUC), 0.894; sensitivity, 83.8%; specificity, 81.0%] was predictive of T2DM, while an IDRS <60 (AUC, 0.882; sensitivity, 79.9%; specificity, 83.8%) was predictive of non-T2DM. CONCLUSIONS: The IDRS, a simple, cost-effective risk score, can assist in classifying T2DM versus non-T2DM among clinic patients in India.
AIM: The aim of this study was to investigate whether the Indian Diabetes Risk Score (IDRS) could assist in classifying type 2 diabetes mellitus (T2DM) and non-T2DM among patients attending clinics in India. METHODS:Patient records from 2006 through 2009 were taken from the clinical database of a tertiary care diabetes hospital in Chennai, Southern India. A total of 8747 patients with diabetes, diagnosed by a physician either as type 1 diabetes mellitus (T1DM), T2DM, or other types were included for analysis. The IDRS, based on age, abdominal obesity, family history of diabetes, and physical activity, was calculated for each patient at first visit to our clinic. Receiver operating characteristic (ROC) curves were generated to obtain optimal IDRS cut points for predicting T2DM and non-T2DM. RESULTS: Of the 8747 patient records analyzed, 204 (2.3%) were classified as non-T2DM and 8543 (97.7%) as T2DM. In ROC analysis, an IDRS ≥60 [area under the curve (AUC), 0.894; sensitivity, 83.8%; specificity, 81.0%] was predictive of T2DM, while an IDRS <60 (AUC, 0.882; sensitivity, 79.9%; specificity, 83.8%) was predictive of non-T2DM. CONCLUSIONS: The IDRS, a simple, cost-effective risk score, can assist in classifying T2DM versus non-T2DM among clinic patients in India.
Authors: Matthias B Schulze; Kurt Hoffmann; Heiner Boeing; Jakob Linseisen; Sabine Rohrmann; Matthias Möhlig; Andreas F H Pfeiffer; Joachim Spranger; Claus Thamer; Hans-Ulrich Häring; Andreas Fritsche; Hans-Georg Joost Journal: Diabetes Care Date: 2007-03 Impact factor: 19.112
Authors: Jay M Sosenko; Jeffrey P Krischer; Jerry P Palmer; Jeffrey Mahon; Catherine Cowie; Carla J Greenbaum; David Cuthbertson; John M Lachin; Jay S Skyler Journal: Diabetes Care Date: 2007-11-13 Impact factor: 19.112