[Performance of glycated hemoglobin and a risk model for detection of unknown diabetes in coronary patients]

Introduction and objectives: Traditionally, the oral glucose tolerance test has been useful to diagnose unknown diabetes. Recently, the American Diabetes Association committee has accepted glycated hemoglobin ≥ 6.5% as a criterion for unknown diabetes. The aim was to determine the benefit of glycated hemoglobin for diagnosing unknown diabetes and also create a predictive model that adjusts the indication for oral glucose tolerance test in coronary patients.

Methods: We examined the glycemic profile of 338 coronary patients without previous diagnosis of diabetes, applying 2010 American Diabetes Association criteria. A unknown diabetes risk predictive model was developed using logistic regression analysis, and then validated in another cohort.

Results: Using the glycated hemoglobin criteria and/or fasting plasma glucose, unknown diabetes was diagnosed in 26 patients. The remaining patients were classified according to oral glucose tolerance test as follows: unknown diabetes 53 (17%), prediabetes 144 (46.2%), and normoglycemic 115 (36.8%). The diagnostic method for unknown diabetes was fasting plasma glucose in 25.3%, glycated hemoglobin in 7.6%, and oral glucose tolerance test in 67.1%. A risk model including fasting plasma glucose, glycated hemoglobin, left ventricular ejection fraction, age, and noncoronary vascular disease was shown to effectively predict unknown diabetes after oral glucose tolerance test: area under the ROC curve 0.8 (95% confidence interval: 0.74-0.87). When the oral glucose tolerance test is restricted to patients with a risk score >6 (31% of our sample) we properly identify 83% of unknown diabetes cases (sensitivity: 75%, specificity: 73%, positive predictive value: 40%, negative predictive value: 93%). The model was adequately validated in another cohort of 115 patients (area under the ROC curve 0.84 [95% confidence interval: 0.74-0.95]).

Conclusions: In coronary patients, glycated hemoglobin alone failed to detect many cases of unknown diabetes. However, its inclusion in a risk prediction model leads to optimizing the usefulness of oral glucose tolerance test.