Diabetes Screening in the Emergency Department: Development of a Predictive Model for Elevated Hemoglobin A1c.
Mary H Smart, Janet Y Lin, Brian T Layden, Yuval Eisenberg, A Simon Pickard, Lisa K Sharp, Kirstie K Danielson, Angela Kong
Author Information
Mary H Smart: Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
Janet Y Lin: Department of Emergency Medicine, College of Medicine, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
Brian T Layden: Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
Yuval Eisenberg: Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
A Simon Pickard: Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
Lisa K Sharp: Department of Biobehavioral Nursing Science, College of Nursing, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
Kirstie K Danielson: Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
Angela Kong: Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, The University of Illinois Chicago, Chicago, Illinois, USA. ORCID
We developed a prediction model for elevated hemoglobin A1c (HbA1c) among patients presenting to the emergency department (ED) at risk for diabetes to identify important factors that may influence follow-up patient care. Retrospective electronic health records data among patients screened for diabetes at the ED in May 2021 was used. The primary outcome was elevated HbA1c (������5.7%). The data was divided into a derivation set (80%) and a test set (20%) stratified by elevated HbA1c. In the derivation set, we estimated the optimal significance level for backward elimination using a 10-fold cross-validation method. A final model was derived using the entire derivation set and validated on the test set. Performance statistics included C-statistic, sensitivity, specificity, predictive values, Hosmer-Lemeshow test, and Brier score. There were 590 ED patients screened for diabetes in May 2021. The final model included nine variables: age, race/ethnicity, insurance, chief complaints of back pain and fever/chills, and a past medical history of obesity, hyperlipidemia, chronic obstructive pulmonary disease, and substance misuse. Adequate model discrimination (C-statistic = 0.75; sensitivity, specificity, and predictive���values > 0.70), no evidence of model ill fit (Hosmer-Lemeshow test = 0.29), and moderate Brier score (0.21) suggest acceptable model performance. In addition to age, obesity, and hyperlipidemia, a history of substance misuse was identified as an important predictor of elevated HbA1c levels among patients screened for diabetes in the ED. Our findings suggest that substance misuse may be an important factor to consider when facilitating follow-up care for patients identified with prediabetes or diabetes in the ED and warrants further investigation. Future research efforts should also include external validation in larger samples of ED patients.
References
Can Fam Physician. 2017 Jul;63(7):e350-e354
[PMID: 28701461]
