Herman and colleagues’ risk assessment instrument identified 1269 of 3384 patients in the sample as high risk. The questionnaire identified 129 of the 164 persons with diabetes (LR+ was 2.22; LR-, 0.32). In this model, 10% of those identified as high risk would actually have diabetes. Performing blood glucose testing on only those at high risk would miss 21% of the cases of diabetes.
Barriga and coworkers18 used the same technique as Herman and colleagues to develop several risk-factor-based models using data from community-based Hispanic and non-Hispanic white patients in California. Models were designed to help decide which patients should undergo confirmatory blood glucose testing through oral glucose tolerance testing (OGTT) to identify both type 2 diabetes and impaired glucose tolerance. All but 1 of their models used a serum test result as a risk factor, either fasting blood glucose or glycohemoglobin. Using a serum test result as a risk factor defeats the purpose of risk-factor-based screening, in which the goal is to minimize blood glucose tests. Sequential fasting glucose measurements can be used to confirm or rule out diabetes.6
The use of serum test results as a risk factor, measuring impaired glucose tolerance and type 2 diabetes as a combined outcome, and the methodologic shortcomings inherent to computerized statistical model design and testing make the validity of this study questionable and the results difficult to compare and interpret. One of the models described, the first step in a sequential assessment of risk factors, used only body mass index (>27.9) and age (>53.6 years) as risk factors. A high-risk patient had 1 or both of these risk factors. The LR+ for this risk factor model was 1.56; LR-, 0.2.
Azzopardi and coworkers19 developed computerized models on the basis of risk factors and symptoms of diabetes such as lethargy and thirst. Models were designed to collectively identify both type 1 and type 2 diabetes. Their study, therefore, does not precisely match the question addressed in this review. In developing their models, the authors studied patients in Malta: 128 newly diagnosed with diabetes and 320 without known diabetes. This second group was used as a control group. The prevalence of risk factors and symptoms were compared between the 2 groups to generate models to identify high-risk patients. The models were then tested on the same group of control patients and those with diabetes. OGTT was used as the reference standard for diagnosis of diabetes, and was performed only on the control subjects identified as high risk.
This study not only suffers from the shortcomings in validity inherent to this methodology, but the reference standard of OGTT was not applied to all control patients. In a true control group, the absence of diabetes would be confirmed. How many cases of diabetes were missed in this group is unknown; therefore, likelihood ratios cannot be calculated. The best performing model identified 84% of the 128 patients with diabetes as high risk. Sixty-four (20%) of control patients were identified as being at high risk. Diabetes was confirmed in 17. There were, therefore, 47 false positives.
Statistical Model with Prospective Validation
Ruige and colleagues20 developed a questionnaire to identify patients at risk for type 2 diabetes by studying both symptoms and risk factors among 2364 white patients in the Netherlands without known diabetes. OGTT was used to confirm or rule out disease in all patients. The final questionnaire included questions about thirst, shortness of breath, reluctance to use a bicycle, age, obesity, sex, family history of diabetes, and use of antihypertensive drugs as predictors of type 2 diabetes. Age, family history, and obesity were the most significant risks. This questionnaire was then prospectively evaluated in a completely separate but similar second population of 786 patients in whom diabetes was confirmed either through fasting glucose or OGTT. The questionnaire generates a composite score with a cutoff that can be varied.
Ruige and coworkers used the widely accepted tests of fasting glucose and OGTT as diagnostic reference standards. It is unclear whether the comparison of questionnaire results with blood glucose testing was blind or completely independent. The reference standard was applied regardless of the results on the questionnaire. The questionnaire is easy to use and reproduce.
The sample did not include nonwhite patients. Race is a significant risk factor for type 2 diabetes in the United States. Nonwhite patients may receive the greatest benefit from diabetes screening.
Using a cutoff score of 5 on the self-reporting questionnaire, the LR+ was 1.6; LR-, 0.50. In the second population, Ruige and colleagues also tested the questionnaires used by Herman and coworkers16 which yielded an LR+ of 1.60 and an LR- of 0.51, and the ADA questionnaire,13 which yielded an LR+ of 1.37 and an LR- of 0.72.