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- Title
Simple non-laboratory- and laboratory-based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus 检测糖尿病的简化非实验室和实验室风险评估公式和计算图
- 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.
- 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.
- Subjects
DIET therapy for diabetes; RISK management information systems; ALGORITHMIC randomness; MATHEMATICAL models; CARBOHYDRATE intolerance; RISK assessment
- Publication
Journal of Diabetes, 2016, Vol 8, Issue 3, p414
- ISSN
1753-0393
- Publication type
Article
- DOI
10.1111/1753-0407.12310