title: Formulation and evaluation of a predictive model to identify the sites of future diabetic retinopathy.
abstract: OBJECTIVE
  To formulate and test a model to predict the development of local patches of nonproliferative diabetic retinopathy (NPDR), based on multifocal electroretinogram (mfERG) implicit times and candidate diabetic risk factors.
  
  
  METHODS
  mfERGs and fundus photographs were obtained from 28 eyes of 28 diabetic patients during an initial and 12-month follow-up examination. mfERG implicit times were derived at 103 locations using a template-stretching method, and a z-score was calculated in comparison with 20 age-matched normal subjects. Thirty-five nonoverlapping retinal zones were constructed by grouping two to three adjacent stimulated locations, and each zone was assigned the maximum z-score within it. Zones containing initial retinopathy were excluded from further analysis. The probability that new retinopathy would develop in the remaining zones by the follow-up examination was modeled based on the mfERG implicit time z-score for the zone and other candidate diabetic risk factors determined during the initial visit. Data collected from four previously untested diabetic subjects and the other eye of eight previous subjects during their second year follow-up were used to test the predictive model.
  
  
  RESULTS
  After 1 year, new retinopathy developed in 11 of the 12 NPDR eyes and 1 of the 16 eyes without initial retinopathy. After accounting for the correlation among zones within each eye, a predictive model was formulated with the variables mfERG implicit time, duration of diabetes, presence of retinopathy (NPDR or no retinopathy), and blood glucose level at initial visit. The area under the receiver operating characteristic (ROC) curve of this multivariate model is 0.90 (P <0.001). The predictive model has an expected sensitivity of 86% and a specificity of 84%, which was verified by the test data.
  
  
  CONCLUSIONS
  The development of diabetic retinopathy over a 1-year period can be well predicted by a multivariate model. The inclusion of local mfERG implicit times allowed the model to identify the specific sites of future retinopathy.
authors.affiliation: Vision Science Program, School of Optometry, University of California at Berkeley, 94720-2020, USA. yingh@uclink.berkeley.edu
authors.forename: Ying
authors.initials: Y
authors.lastname: Han
authors.forename: Marilyn E
authors.initials: ME
authors.lastname: Schneck
authors.forename: Marcus A
authors.initials: MA
authors.lastname: Bearse
authors.forename: Shirin
authors.initials: S
authors.lastname: Barez
authors.forename: Carl H
authors.initials: CH
authors.lastname: Jacobsen
authors.forename: Nicholas P
authors.initials: NP
authors.lastname: Jewell
authors.forename: Anthony J
authors.initials: AJ
authors.lastname: Adams
mesh_terms: D000328:Adult; D019540:Area Under Curve; D001786:Blood Glucose / Q000032:analysis; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D003941:Diagnostic Techniques, Ophthalmological; D004596:Electroretinography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015233:Models, Statistical; D010781:Photography; D011336:Probability; D012307:Risk Factors; D013997:Time Factors
chemical_list: D001786:Blood Glucose