Open AccessSimple non‐laboratory‐ and laboratory‐based risk assessment algorithms and nomogram for detecting undiagnosed diabetes mellitus
Author(s) -
Wong Carlos K.H.,
Siu ShingChung,
Wan Eric Y.F.,
Jiao FangFang,
Yu Esther Y.T.,
Fung Colman S.C.,
Wong KaWai,
Leung Angela Y.M.,
Lam Cindy L.K.
Publication year - 2016
Publication title -
journal of diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.949
H-Index - 43
eISSN - 1753-0407
pISSN - 1753-0393
DOI - 10.1111/1753-0407.12310
Subject(s) - medicine , nomogram , diabetes mellitus , logistic regression , asymptomatic , algorithm , anthropometry , receiver operating characteristic , type 2 diabetes mellitus , body mass index , endocrinology , mathematics
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 C hinese 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 H osmer– L emeshow 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 ( H osmer– L emeshow 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.