Simple non‐laboratory‐ and laboratory‐based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus. (29th June 2015)

• Record Type: Journal Article
• Authors: Wong, Carlos K.H.; Siu, Shing‐Chung; Wan, Eric Y.F.; Jiao, Fang‐Fang; Yu, Esther Y.T.; Fung, Colman S.C.; Wong, Ka‐Wai; Leung, Angela Y.M.; Lam, Cindy L.K.
• Is Part Of: Journal of diabetes. Volume 8:Number 3(2016:May)
• Page Start: 414
• Page End: 421
• Publication Date: 2015-06-29
• Subjects: nomogram -- risk algorithm -- undiagnosed diabetes -- validation; 计算图 -- 风险评估公式 -- 未诊断的糖尿病 -- 验证; Diabetes -- Periodicals; 618.3646005
• DOI: 10.1111/1753-0407.12310
• Languages: English
• ISSNs: 1753-0393

Abstract

Abstract: Background: The aim of the present study was to develop a simple nomogram that can be used to predict the risk of diabetes mellitus (DM) in the asymptomatic non‐diabetic subjects based on non‐laboratory‐ and laboratory‐based risk algorithms. Methods: Anthropometric data, plasma fasting glucose, full lipid profile, exercise habits, and family history of DM were collected from Chinese non‐diabetic subjects aged 18–70 years. Logistic regression analysis was performed on a random sample of 2518 subjects to construct non‐laboratory‐ and laboratory‐based risk assessment algorithms for detection of undiagnosed DM; both algorithms were validated on data of the remaining sample ( n = 839). The Hosmer–Lemeshow test and area under the receiver operating characteristic (ROC) curve (AUC) were used to assess the calibration and discrimination of the DM risk algorithms. Results: Of 3357 subjects recruited, 271 (8.1%) had undiagnosed DM defined by fasting glucose ≥7.0 mmol/L or 2‐h post‐load plasma glucose ≥11.1 mmol/L after an oral glucose tolerance test. The non‐laboratory‐based risk algorithm, with scores ranging from 0 to 33, included age, body mass index, family history of DM, regular exercise, and uncontrolled blood pressure; the laboratory‐based risk algorithm, with scores ranging from 0 to 37, added triglyceride level to the risk factors. Both algorithms demonstrated acceptable calibration (Hosmer–Lemeshow test: P = 0.229 and P = 0.483) and discrimination (AUC 0.709 and 0.711) for detection of undiagnosed DM. Conclusion: A simple‐to‐use nomogram for detecting undiagnosed DM has been developed using validated non‐laboratory‐based and laboratory‐based risk algorithms.