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

Brian J Wells; Kristin M Lenoir; Jose-Franck Diaz-Garelli; Wendell Futrell; Elizabeth Lockerman; Kevin M Pantalone; Michael W Kattan

doi:10.2196/10780

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

JMIR Med Inform. 2018 Oct 22;6(4):e10780. doi: 10.2196/10780.

Authors

Brian J Wells¹, Kristin M Lenoir¹, Jose-Franck Diaz-Garelli², Wendell Futrell³, Elizabeth Lockerman⁴, Kevin M Pantalone⁵, Michael W Kattan⁶

Affiliations

¹ Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
² Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
³ Clinical and Translational Science Institute, Wake Forest University School of Medicine, Winston-Salem, NC, United States.
⁴ Department of Internal Medicine, Loyola University Medical Center, Maywood, IL, United States.
⁵ Endocrinology and Metabolism Institute, Department of Endocrinology, Diabetes and Metabolism, Cleveland Clinic, Cleveland, OH, United States.
⁶ Lerner Research Institute, Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States.

PMID: 30348631
PMCID: PMC6231807
DOI: 10.2196/10780

Abstract

Background: Electronic, personalized clinical decision support tools to optimize glycated hemoglobin (HbA_1c) 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 HbA_1c (≥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 HbA_1c 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 HbA_1c significantly outperformed the existing calculators. The personalized prediction model presented in this paper could improve the efficiency of HbA_1c screening initiatives.

Keywords: clinical decision support; diabetes; electronic health records; hemoglobin A1c; risk prediction.

©Brian J Wells, Kristin M Lenoir, Jose-Franck Diaz-Garelli, Wendell Futrell, Elizabeth Lockerman, Kevin M Pantalone, Michael W Kattan. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 22.10.2018.

Abstract

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