A Multivariate Logistic Regression Equation to Screen for Diabetes

Abstract

OBJECTIVE—To develop and validate an empirical equation to screen for diabetes. RESEARCH DESIGN AND METHODS—A predictive equation was developed using multiple logistic regression analysis and data collected from 1,032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, BMI, postprandial time (self-reported number of hours since last food or drink other than water), and random capillary plasma glucose as independent covariates for prediction of undiagnosed diabetes. These covariates were based on a fasting plasma glucose level ≥126 mg/dl and/or a plasma glucose level 2 h after a 75-g oral glucose load ≥200 mg/dl. The equation was validated using data collected from an independent sample of 1,065 American subjects. Its performance was also compared with that of recommended and proposed static plasma glucose cut points for diabetes screening. RESULTS—The predictive equation was calculated with the following logistic regression parameters: P = 1/(1 − e−x), where x = −10.0382 + [0.0331 (age in years) + 0.0308 (random plasma glucose in mg/dl) + 0.2500 (postprandial time assessed as 0 to ≥8 h) + 0.5620 (if female) + 0.0346 (BMI)]. The cut point for the prediction of previously undiagnosed diabetes was defined as a probability value ≥0.20. The equation’s sensitivity was 65%, specificity 96%, and positive predictive value (PPV) 67%. When applied to a new sample, the equation’s sensitivity was 62%, specificity 96%, and PPV 63%. CONCLUSIONS—This multivariate logistic equation improves on currently recommended methods of screening for undiagnosed diabetes and can be easily implemented in a inexpensive handheld programmable calculator to predict previously undiagnosed diabetes.