Predicting Current Glycated Hemoglobin Values in Adults: Development of an Algorithm From the Electronic Health Record

By B. Wells, K. Lenoir, J. Diaz-Garelli, Wendell Futrell, Elizabeth Lockerman, K. Pantalone, M. Kattan. 2018

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Background Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA1c) screening are lacking. Current screening guidelines are based on simple, categorical rules developed for populations of patients. Although personalized diabetes risk calculators have been created, none are designed to predict current glycemic status using structured data commonly available in electronic health records (EHRs). Objective The goal of this project was to create a mathematical equation for predicting the probability of current elevations in HbA1c (≥5.7%) among patients with no history of hyperglycemia using readily available variables that will allow integration with EHR systems. Methods The reduced model was compared head-to-head with calculators created by Baan and Griffin. Ten-fold cross-validation was used to calculate the bias-adjusted prediction accuracy of the new model. Statistical analyses were performed in R version 3.2.5 (The R Foundation for Statistical Computing) using the rms (Regression Modeling Strategies) package. Results The final model to predict an elevated HbA1c based on 22,635 patient records contained the following variables in order from most to least importance according to their impact on the discriminating accuracy of the model: age, body mass index, random glucose, race, serum non–high-density lipoprotein, serum total cholesterol, estimated glomerular filtration rate, and smoking status. The new model achieved a concordance statistic of 0.77 which was statistically significantly better than prior models. The model appeared to be well calibrated according to a plot of the predicted probabilities versus the prevalence of the outcome at different probabilities. Conclusions The calculator created for predicting the probability of having an elevated HbA1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA1c screening initiatives.

Subfields: Endocrinology, Diabetes and Metabolism

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