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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

1
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts.
2
Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
3
Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
4
Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
5
Bascom Palmer Eye Institute, University of Miami School of Medicine, Miami, Florida.
6
Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York.
7
Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania.
8
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Institute for Psychology and Behavior, Jilin University of Finance and Economics, Changchun, China.
9
Department of Psychology, Northeastern University, Boston, Massachusetts.
10
Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address: tobias-elze@tobias-elze.de.

Abstract

PURPOSE:

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.

DESIGN:

Retrospective cohort study.

PARTICIPANTS:

Visual fields of 44 503 eyes from 26 130 participants.

METHODS:

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.

MAIN OUTCOME MEASURES:

Predictive models for GHT results reversal using VF features.

RESULTS:

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%.

CONCLUSIONS:

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

PMID:
29103791
DOI:
10.1016/j.ophtha.2017.09.021
[Indexed for MEDLINE]

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