title: Decision trees as a simple-to-use and reliable tool to identify individuals with impaired glucose metabolism or type 2 diabetes mellitus.
abstract: OBJECTIVE
  The prevalence of unknown impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or type 2 diabetes mellitus (T2DM) is high. Numerous studies demonstrated that IFG, IGT, or T2DM are associated with increased cardiovascular risk, therefore an improved identification strategy would be desirable. The objective of this study was to create a simple and reliable tool to identify individuals with impaired glucose metabolism (IGM).
  
  
  METHODS
  A cohort of 1737 individuals (1055 controls, 682 with previously unknown IGM) was screened by 75 g oral glucose tolerance test (OGTT). Supervised machine learning was used to automatically generate decision trees to identify individuals with IGM. To evaluate the accuracy of identification, a tenfold cross-validation was performed. Resulting trees were subsequently re-evaluated in a second, independent cohort of 1998 individuals (1253 controls, 745 unknown IGM).
  
  
  RESULTS
  A clinical decision tree included age and systolic blood pressure (sensitivity 89.3%, specificity 37.4%, and positive predictive value (PPV) 48.0%), while a tree based on clinical and laboratory data included fasting glucose and systolic blood pressure (sensitivity 89.7%, specificity 54.6%, and PPV 56.2%). The inclusion of additional parameters did not improve test quality. The external validation approach confirmed the presented decision trees.
  
  
  CONCLUSIONS
  We proposed a simple tool to identify individuals with existing IGM. From a practical perspective, fasting blood glucose and blood pressure measurements should be regularly measured in all individuals presenting in outpatient clinics. An OGTT appears to be useful only if the subjects are older than 48 years or show abnormalities in fasting glucose or blood pressure.
authors.affiliation: Department of Endocrinology, Diabetes and Nutrition, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Hindenburgdamm 30, Berlin, Germany.
authors.forename: Manuela
authors.initials: M
authors.lastname: Hische
authors.forename: Olga
authors.initials: O
authors.lastname: Luis-Dominguez
authors.forename: Andreas F H
authors.initials: AF
authors.lastname: Pfeiffer
authors.forename: Peter E
authors.initials: PE
authors.lastname: Schwarz
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
authors.forename: Joachim
authors.initials: J
authors.lastname: Spranger
mesh_terms: D000328:Adult; D000368:Aged; D001185:Artificial Intelligence; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis*; D005260:Female; D018149:Glucose Intolerance / Q000175:diagnosis*; D006801:Humans; D008297:Male; D008875:Middle Aged