Prediction accuracy of a novel dynamic structure-function model for glaucoma progression

Rongrong Hu, Iván Marín-Franch, Lyne Racette

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


PURPOSE. To assess the prediction accuracy of a novel dynamic structure-function (DSF) model to monitor glaucoma progression.

METHODS. Longitudinal data of paired rim area (RA) and mean sensitivity (MS) from 220 eyes with ocular hypertension or primary open-angle glaucoma enrolled in the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study were included. Rim area and MS were expressed as percent of mean normal based on an independent dataset of 91 healthy eyes. The DSF model uses centroids as estimates of the current state of the disease and velocity vectors as estimates of direction and rate of change over time. The first three visits were used to predict the fourth visit; the first four visits were used to predict the fifth visit, and so on up to the 11th visit. The prediction error (PE) was compared to that of ordinary least squares linear regression (OLSLR) using Wilcoxon signedrank test.

RESULTS. For predictions at visit 4 to visit 7, the average PE for the DSF model was significantly lower than OLSLR by 1.19% to 3.42% of mean normal. No significant difference was observed for the predictions at visit 8 to visit 11. The DSF model had lower PE than OLSLR for 70% of eyes in predicting visit 4 and approximately 60% in predicting visits 5, 6, and 7.

CONCLUSIONS. The two models had similar prediction capabilities, and the DSF model performed better in shorter time series. The DSF model could be clinically useful when only limited follow-ups are available.

Original languageEnglish (US)
Pages (from-to)8086-8094
Number of pages9
JournalInvestigative Ophthalmology and Visual Science
Issue number12
StatePublished - Oct 14 2014


  • Computational modeling
  • Glaucoma progression
  • Structure-function

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems
  • Cellular and Molecular Neuroscience

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