School of Population and Global Health, University of Melbourne, Australia.
Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Arch Iran Med. 2019 Mar 1;22(3):116-124.
Iran needs pragmatic screening methods for identifying those with undiagnosed type 2 diabetes or at high risk of developing it. The aim of this study was to assess performance of three non-invasive risk prediction models, i.e. the Finnish Diabetes Risk Score (FINDRISC), the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK), and the American Diabetes Association Risk Score (ADA), for identifying those with undiagnosed type 2 diabetes (prevalent type 2 diabetes at baseline without any treatment) or those who would develop type 2 diabetes within 5 years of follow-up.
3467 participants aged ≥30 years without treated type 2 diabetes in the Tehran Lipid and Glucose Study (TLGS) were included in this study. The discrimination power of models was assessed by area under the curve (AUC), their calibrations were assessed by calibration plots and Hosmer-Lemeshow test, and their net benefits were assessed by decision curves.
430 participants had undiagnosed type 2 diabetes at baseline and 203 developed type 2 diabetes during 5 years of followup. AUSDRISK had the highest AUC (0.77) as compared to FINDRISC (0.75; P value: 0.014), and the ADA model (0.73; P value: <0.001). The original model for AUSDRISK and calibrated versions of FINDRISC and ADA models had acceptable calibration (Hosmer-Lemeshow chi-square <20) and these models were clinically useful in a wide range of risk thresholds as their net benefit was higher than no-screening scenarios.
The original AUSDRISK model and recalibrated models for FINDRISC and ADA are valid and effective tools for identifying those with undiagnosed or 5-year incident type 2 diabetes in Iran.
伊朗需要实用的筛查方法来识别那些未被诊断出的 2 型糖尿病患者或有发展为该病风险的人群。本研究旨在评估三种非侵入性风险预测模型的性能，即芬兰糖尿病风险评分（FINDRISC）、澳大利亚 2 型糖尿病风险评估工具（AUSDRISK）和美国糖尿病协会风险评分（ADA），以识别那些未被诊断出的 2 型糖尿病患者（基线时未经治疗的 2 型糖尿病，且无任何治疗）或那些在随访 5 年内会发展为 2 型糖尿病的患者。
本研究纳入了 Tehran Lipid and Glucose Study（TLGS）中年龄≥30 岁、无治疗 2 型糖尿病的 3467 名参与者。通过曲线下面积（AUC）评估模型的区分能力，通过校准图和 Hosmer-Lemeshow 检验评估其校准度，通过决策曲线评估其净收益。
430 名参与者在基线时患有未被诊断出的 2 型糖尿病，203 名参与者在 5 年的随访期间患上了 2 型糖尿病。与 FINDRISC（0.75；P 值：0.014）和 ADA 模型（0.73；P 值：<0.001）相比，AUSDRISK 的 AUC 值最高（0.77）。AUSDRISK 的原始模型和 FINDRISC 和 ADA 模型的校准版本具有可接受的校准度（Hosmer-Lemeshow χ²<20），这些模型在广泛的风险阈值范围内具有临床实用性，因为它们的净收益高于不筛查的情况。
AUSDRISK 的原始模型和重新校准的 FINDRISC 和 ADA 模型是识别伊朗未诊断或 5 年内发生的 2 型糖尿病患者的有效工具。
