Prevalence of undiagnosed dysglycemia in an emergency department observation unit

Silverman, Robert A.; Schleicher, Mackenzie G.; Valente, Christopher J.; Kim, Mark; Romanenko, Yevgeniy; Schulman, Rifka; Tiberio, Allison; Greenblatt, Benjamin; Rentala, M.; Salvador-Kelly, Annabella V.; Kwon, Nancy; Jornsay, Donna

doi:10.1002/dmrr.2674

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Prior research demonstrates that ED diabetes surveillance protocols using only random, blood glucose measurements have been successful. [6][7][8][9][10] Patient characteristics, such as demographics, 11 body mass index (BMI), family history, comorbidities, and laboratory measures of impaired glucose tolerance, hemoglobin A1c (HbA1c), cholesterol, and triglycerides, can be used to refine a screening decision for diabetes. Confirmatory testing such as fasting plasma glucose, oral glucose tolerance testing, or HbA1C should be considered for definitive diabetes diagnosis.…”

Section: How Does This Improve Population Health?mentioning

confidence: 99%

Diabetes Patient Surveillance in the Emergency Department: Proof of Concept and Opportunities

Sava

Pirrallo

Helsel

et al. 2021

WJEM

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Introduction The purpose of this study was to characterize the at-risk diabetes and prediabetes patient population visiting emergency department (ED) and urgent care (UC) centers in upstate South Carolina. Methods We conducted this retrospective study at the largest non-profit healthcare system in South Carolina, using electronic health record (EHR) data of patients who had an ED or UC visit between February 2, 2016–July 31, 2018. Key variables including International Classification of Diseases, 10th Revision codes, laboratory test results, family history, medication, and demographic characteristics were used to classify the patients as healthy, having prediabetes, having diabetes, being at-risk for prediabetes, or being at-risk for diabetes. Patients who were known to have diabetes were classified further as having controlled diabetes, management challenged, or uncontrolled diabetes. Population analysis was stratified by the patient’s annual number of ED/UC visits. Results The risk stratification revealed 4.58% unique patients with unrecognized diabetes and 10.34% of the known patients with diabetes considered to be suboptimally controlled. Patients identified as diabetes management challenged had more ED/UC visits. Of note, 33.95% of the patients had unrecognized prediabetes/diabetes risk factors identified during their ED/UC with 87.95% having some form of healthcare insurance. Conclusion This study supports the idea that a single ED/UC unscheduled visit can identify individuals with unrecognized diabetes and an at-risk prediabetes population using EHR data. A patient’s ED/UC visit, regardless of their primary reason for seeking care, may be an opportunity to provide early identification and diabetes disease management enrollment to augment the medical care of our community.

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