A classification model for predicting eye disease in newly diagnosed XXXX with type 2 @entity6 .

@entity1 @entity6 @entity989 @entity1011 @entity413
UNASSIGNED: @entity6 may be present at the time type 2 @entity6 is diagnosed, and initial screening encompassing an eye examination performed by an ophthalmologist or optometrist is therefore recommended. However, proper screening for @entity989 may be challenging in many parts of the world. We hypothesized that simple, commonly available @entity1 characteristics can be used to identify @entity1 at high risk for having @entity989 . We investigated data from multiple years extracted from the National Health and Nutrition Examination Survey which holds information about blood @entity413 and eye examinations. Individuals with hitherto undiagnosed @entity6 were classified according to the presence or absence of @entity989 . Linear classification was used to predict which @entity1 had @entity989 at the time of diagnosis. A total of 266 individuals with undiagnosed @entity6 were identified from the cohorts. Of these, 222 individuals had no sign of @entity989 , whereas 44 had mild or moderate @entity989 . Using information regarding HbA1c, BMI, waist circumference, age, systolic blood pressure, urinary albumin, and urinary @entity1011 , we were able to construct a model that predicts the presence of @entity989 with a positive predictive value of 22% and a negative predictive value of 99%. Only one true positive (1/44) with mild @entity989 was falsely classified. A classification model using readily available @entity1 information and routine biochemical measures can be used to identify @entity1 at high risk of having @entity989 at the time their @entity6 is diagnosed. The model may be used to identify high-risk @entity1 for @entity989 screening.
