The Development and Validation of A1c+, a novel multivariable prediction model for the diagnosis of diabetes
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Importance
The hemoglobin A1c and fasting plasma glucose (FPG) have known limitations for diabetes diagnosis, but models to identify individuals who would benefit from 2-hour oral glucose tolerance testing (OGTT) are limited.
Objective
To determine if OGTT-only diagnosed diabetes has comparable outcomes to A1c- or FPG-diagnosed diabetes and if standard clinical features could be leveraged to identify undiagnosed diabetes.
Design
Multivariable prediction model development and validation.
Setting
US National Health and Nutrition Examination Survey (NHANES) data with corresponding US National Center for Health Statistics (NCHS) mortality data.
Participants
Of 105,862 NHANES subjects from 1999 to 2016, we identified 13,800 subjects with FPG, A1c, or OGTT results (11,550 with mortality data) and 92,062 other subjects (53,255 with mortality data).
Exposure
OGTT-diagnosed diabetes
Main Outcomes and Measures
The primary outcomes were association of mortality with diabetes diagnostic approach and models to diagnose diabetes. We used a gradient boosted machine decision tree to predict diabetes from standard clinical features. In the test set, we compared the AUROC and the net benefit by decision curve analysis to A1c, FPG, and a combination of the two. We performed survival analysis based on method of diabetes diagnosis and diabetes model predictions.
Results
The rate of OGTT-only diabetes was 1.34%. Subjects with OGTT-only diabetes had equivalent risk of mortality compared to subjects with FPG- or A1c-diagnosed diabetes after adjusting for age, sex, and race/ethnicity. A model using the A1c and standard clinical features (A1c+ model) outperformed the A1c to exclude diabetes (Sensitivity at Youden’s Index: 0.72 vs. 0.37). Adding FPG to that model (A1c/FPG+) outperformed FPG for excluding diabetes (Sensitivity: 0.87 vs. 0.48). Subjects with A1c/FPG+-predicted diabetes but sub-diagnostic A1c and FPG had equivalent mortality (HR=8.2, p<2*10 −16 ) to those with A1c or FPG-diagnosed diabetes (comparison p<0.17).
Conclusions and Relevance
Diabetes diagnosed by OGTT alone has equivalent mortality to A1c and FPG-diagnosed diabetes. A model using standard clinical features can bolster the A1c and FPG to identify potentially undiagnosed diabetes. Model predictions associated with mortality equivalently to having a diabetes diagnosis. Implementation of a clinical decision support tool could improve diagnosis of diabetes and lead to earlier interventions.
Key Points
Question
Are individuals with diabetes only diagnosable by oral glucose tolerance test (OGTT) at similar risk of mortality as those diagnosed by hemoglobin A1c or fasting plasma glucose (FPG)? Can readily-available clinical data improve diagnosis?
Findings
Stratifying NHANES subjects by OGTT-only (1.34%) vs A1c or FPG diagnosis (4.13%) of diabetes found that OGTT-only diagnosis had equivalent mortality. A model combining A1c and standard clinical features (A1c+) had superior AUROC for diabetes compared to the A1c alone. The addition of FPG (A1c/FPG+) had superior AUROC compared to the FPG and A1c+. The A1c/FPG+ predictions strongly associated with mortality in subjects with sub-diagnostic A1c and FPG.
Meaning
Incorporation of clinical features can improve diabetes diagnosis missed by A1c and FPG. Model prediction of diabetes associates with mortality.
