Predicting Global Test–Retest Variability of Visual Fields in Glaucoma

Eun Young Choi, Dian Li, Yuying Fan, Louis R. Pasquale, Lucy Q. Shen, Michael V. Boland, Pradeep Ramulu, Siamak Yousefi, Carlos Gustavo De Moraes, Sarah R. Wellik, Jonathan S. Myers, Peter J. Bex, Tobias Elze, Mengyu Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To model the global test–retest variability of visual fields (VFs) in glaucoma. Design: Retrospective cohort study. Participants: Test–retest VFs from 4044 eyes of 4044 participants. Methods: We selected 2 reliable VFs per eye measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm 24-2) within 30 days of each other. Each VF had fixation losses (FLs) of 33% or less, false-negative results (FNRs) of 20% or less, and false-positive results (FPRs) of 20% or less. Stepwise linear regression was applied to select the model best predicting the global test–retest variability from 3 categories of features of the first VF: (1) base parameters (age, mean deviation, pattern standard deviation, glaucoma hemifield test results, FPR, FNR, and FL); (2) total deviation (TD) at each location; and (3) computationally derived archetype VF loss patterns. The global test–retest variability was defined as root mean square deviation (RMSD) of TD values at all 52 VF locations. Main Outcome Measures: Archetype models to predict the global test–retest variability. Results: The mean ± standard deviation of the root mean square deviation was 4.39 ± 2.55 dB. Between the 2 VF tests, TD values were correlated more strongly in central than in peripheral VF locations (intraclass coefficient, 0.66–0.89; P < 0.001). Compared with the model using base parameters alone (adjusted R2 = 0.45), adding TD values improved prediction accuracy of the global variability (adjusted R2 = 0.53; P < 0.001; Bayesian information criterion [BIC] decrease of 527; change of >6 represents strong improvement). Lower TD sensitivity in the outermost peripheral VF locations was predictive of higher global variability. Adding archetypes to the base model improved model performance with an adjusted R2 of 0.53 (P < 0.001) and lowering of BIC by 583. Greater variability was associated with concentric peripheral defect, temporal hemianopia, inferotemporal defect, near total loss, superior peripheral defect, and central scotoma (listed in order of decreasing statistical significance), and less normal VF results and superior paracentral defect. Conclusions: Inclusion of archetype VF loss patterns and TD values based on first VF improved the prediction of the global test–retest variability than using traditional global VF indices alone.

Original languageEnglish (US)
Pages (from-to)390-399
Number of pages10
JournalOphthalmology. Glaucoma
Volume4
Issue number4
DOIs
StatePublished - Jul 1 2021

Keywords

  • Glaucoma
  • Machine learning
  • Test–retest variability
  • Visual field

ASJC Scopus subject areas

  • Ophthalmology
  • Medicine(all)

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