A multivariate logistic regression equation to screen for dysglycaemia: development and validation.

AIMS: To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes].
METHODS: A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG)>or=6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG)>or=7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening.
RESULTS: The predictive equation was calculated with the following logistic regression parameters: P=1+1/(1+e-X)=where X=-8.3390+0.0214 (age in years)+0.6764 (if female)+0.0335 (BMI in kg/m2)+0.0934 (post-prandial time in hours)+0.0141 (systolic blood pressure in mmHg)-0.0110 (HDL in mmol/l)+0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability>or=0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%.
CONCLUSIONS: This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator.