Derivation and validation of essential predictors and risk index for early detection of diabetic retinopathy using electronic health records

Ru Wang, Zhuqi Miao, Tieming Liu, Mei Liu, Kristine Grdinovac, Xing Song, Ye Liang, Dursun Delen, William Paiva

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Diabetic retinopathy (DR) is a leading cause for blindness among working-aged adults. The growing prevalence of diabetes urges for cost-effective tools to improve the compliance of eye examinations for early detection of DR. The objective of this research is to identify essential predictors and develop predictive technologies for DR using electronic health records. We conducted a retrospective analysis on a derivation cohort with 3749 DR and 94, 127 non-DR diabetic patients. In the analysis, an ensemble predictor selection method was employed to find essential predictors among 26 variables in demographics, duration of diabetes, complications and laboratory results. A predictive model and a risk index were built based on the selected, essential predictors, and then validated using another independent validation cohort with 869 DR and 6448 non-DR diabetic patients. Out of the 26 variables, 10 were identified to be essential for predicting DR. The predictive model achieved a 0.85 AUC on the derivation cohort and a 0.77 AUC on the validation cohort. For the risk index, the AUCs were 0.81 and 0.73 on the derivation and validation cohorts, respectively. The predictive technologies can provide an early warning sign that motivates patients to comply with eye examinations for early screening and potential treatments.

Original languageEnglish
Article number1473
JournalJournal of Clinical Medicine
Volume10
Issue number7
DOIs
StatePublished - 1 Apr 2021

Keywords

  • Diabetic retinopathy
  • Early detection
  • Electronic health records
  • Predictive models
  • Predictor selection
  • Risk index

Fingerprint

Dive into the research topics of 'Derivation and validation of essential predictors and risk index for early detection of diabetic retinopathy using electronic health records'. Together they form a unique fingerprint.

Cite this