Send to

Choose Destination
Ophthalmology. 2018 Mar;125(3):352-360. doi: 10.1016/j.ophtha.2017.09.021. Epub 2017 Nov 2.

Reversal of Glaucoma Hemifield Test Results and Visual Field Features in Glaucoma.

Author information

Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts.
Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Bascom Palmer Eye Institute, University of Miami School of Medicine, Miami, Florida.
Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York.
Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania.
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Institute for Psychology and Behavior, Jilin University of Finance and Economics, Changchun, China.
Department of Psychology, Northeastern University, Boston, Massachusetts.
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address:



To develop a visual field (VF) feature model to predict the reversal of glaucoma hemifield test (GHT) results to within normal limits (WNL) after 2 consecutive outside normal limits (ONL) results.


Retrospective cohort study.


Visual fields of 44 503 eyes from 26 130 participants.


Eyes with 3 or more consecutive reliable VFs measured with the Humphrey Field Analyzer (Swedish interactive threshold algorithm standard 24-2) were included. Eyes with ONL GHT results for the 2 baseline VFs were selected. We extracted 3 categories of VF features from the baseline tests: (1) VF global indices (mean deviation [MD] and pattern standard deviation), (2) mismatch between baseline VFs, and (3) VF loss patterns (archetypes). Logistic regression was applied to predict the GHT results reversal. Cross-validation was applied to evaluate the model on testing data by the area under the receiver operating characteristic curve (AUC). We ascertained clinical glaucoma status on a patient subset (n = 97) to determine the usefulness of our model.


Predictive models for GHT results reversal using VF features.


For the 16 604 eyes with 2 initial ONL results, the prevalence of a subsequent WNL result increased from 0.1% for MD < -12 dB to 13.8% for MD ≥-3 dB. Compared with models with VF global indices, the AUC of predictive models increased from 0.669 (MD ≥-3 dB) and 0.697 (-6 dB ≤ MD < -3 dB) to 0.770 and 0.820, respectively, by adding VF mismatch features and computationally derived VF archetypes (P < 0.001 for both). The GHT results reversal was associated with a large mismatch between baseline VFs. Moreover, the GHT results reversal was associated more with VF archetypes of nonglaucomatous loss, severe widespread loss, and lens rim artifacts. For a subset of 97 eyes, using our model to predict absence of glaucoma based on clinical evidence after 2 ONL results yielded significantly better prediction accuracy (87.7%; P < 0.001) than predicting GHT results reversal (68.8%) with a prescribed specificity 67.7%.


Using VF features may predict the GHT results reversal to WNL after 2 consecutive ONL results.

[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center