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Health Services Research & Development

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2012 HSR&D/QUERI National Conference Abstract

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2012 National Meeting

3044 — Who is Missed When Screening for Diabetes and Prediabetes with a New VA/DOD Algorithm?

Olson DE, Atlanta VAMC and Emory University School of Medicine; Jackson S, Emory University; Tomolo A, Atlanta VAMC and Emory University School of Medicine; Mohan A, and Barb D, Emory University School of Medicine; Zhu M, and Long Q, Emory University Rollins School of Public Health; Phillips LS, Atlanta VAMC and Emory University School of Medicine;

Objectives:
A new VA/DoD screening algorithm for diabetes and prediabetes using fasting plasma glucose (FPG) and hemoglobin A1c (A1c) in patients with risk factors could improve clinical decision-making and care by translating new knowledge about A1c and easier testing into practice. We compared the VA/DoD algorithm to ADA diagnostic guidelines to see if subjects would be missed by the VA algorithm.

Methods:
We applied the VA algorithm to datasets of Americans without known diabetes with FPG, A1c, and oral glucose tolerance tests (OGTT) from a prospective Screening for Impaired Glucose Tolerance Study (SIGT) and the NHANES 2005-2006 survey (NH0506), including subjects if risk categories of age, race/ethnicity, BMI, lipids, blood pressure, and family history were known. Diabetes (5.8% in SIGT, 7.7% in NH0506) or prediabetes (45.3% in SIGT, 48.1% in NH0506) were classified per ADA diagnostic guidelines. If FPG >=126 mg/dl or A1c >=7%, they received a VA/DoD diagnosis of diabetes, or a VA/DoD diagnosis of prediabetes if the FPG >=100 mg/dl or A1c >=5.7%.

Results:
95% of 1581 SIGT subjects and 93% of 1723 NH0506 subjects would be screened by the VA/DoD algorithm, including all subjects with diabetes and 98% with prediabetes. The VA/DoD algorithm only identified 54-60% of diabetic subjects and 75-79% of prediabetic subjects. FPG determined VA/DoD diabetes 91% of the time and A1c contributed rarely. However, A1c contributed to over half of the VA/DoD prediabetes diagnoses. Relying on A1c as the primary test was more likely to miss older, white, and male subjects.

Implications:
The VA/DoD algorithm includes all subjects that would eventually be diagnosed with diabetes, but does not effectively reduce the number of screened subjects, while missing a large proportion of diabetics vs. standard ADA criteria. The algorithm identifies relatively more subjects with prediabetes that could undergo further testing.

Impacts:
As VA policy, the current algorithm appears not to reduce how often primary care providers would screen for diabetes. Screening without OGTT misses early diabetes; performs differently according to race, age, and gender; and does not identify prediabetic subjects with impaired glucose tolerance known to benefit from best practices of lifestyle and medication interventions.


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