Validation of a nomogram for predicting regression from impaired fasting glucose to normoglycaemia to facilitate clinical decision making.

backgroundIn Hong Kong, fasting plasma glucose (FPG) is the most popular screening test for diabetes mellitus (DM) in primary care. Individuals with impaired fasting glucose (IFG) are commonly encountered.

objectivesTo explore the determinants of regression to normoglycaemia among primary care patients with IFG based on non-invasive variables and to establish a nomogram for the prediction of regression from IFG.

methodsThis cohort study consisted of 1197 primary care patients with IFG. These subjects were invited to repeat a FPG test and 75-g 2-hour oral glucose tolerance test (2h-OGTT) to determine the glycaemia change. Normoglycaemia was defined as FPG <5.6 mmol/L and 2h-OGTT <7.8 mmol/L. Stepwise logistic regression model was developed to predict the regression to normoglycaemia with non-invasive variables, using a randomly selected training dataset (810 subjects). The model was validated on the remaining testing dataset (387 subjects). Area under the receiver operating characteristic curve (AUC) and Hosmer-Lemeshow test were used to evaluate discrimination and calibration of the model. A nomogram was constructed based on the model.

resultsAfter a mean follow-up period of 6.1 months, 180 subjects (15.0%) had normoglycaemia based on the repeated FPG and 2h-OGTT results at follow-up. Subjects without central obesity or hypertension, with moderate-to-high-level physical activity and a lower baseline FPG level, were more likely to regress to normoglycaemia. The prediction model had acceptable discrimination (AUC = 0.705) and calibration (P = 0.840).

conclusionThe simple-to-use nomogram could facilitate identification of subjects with low risk of progression to DM and thus aid in clinical decision making and resource prioritization in the primary care setting.