Dataset Information

The Frontloading Fields Study (FFS): Detecting Changes in Mean Deviation in Glaucoma Using Multiple Visual Field Tests Per Clinical Visit.

ABSTRACT:

Purpose

To determine the impact of different numbers of visual field tests per visit for detecting mean deviation changes over time in patients with early glaucoma or suspected glaucoma and to identify a practical approach to maximize change detection.

Methods

Intrasession (n = 322) and intersession (n = 323) visual field results for patients with glaucoma or suspected glaucoma were used to model mean deviation change in 10,000 progressing and 10,000 non-progressing computer-simulated patients over time. Variables assessed in the model included follow-up intervals (0.5, 1, or 2 years), reliability rates (70%, 85%, or 100%) and number of visual field tests performed at each visit (one to four).

Results

Two visual field tests per session compared with one provided higher case detection rates at 2 years (99%-99.8% vs. 34.7%-76.3%, respectively), reduced time to detection (three or four visits vs. six to 10, respectively), and more positive mean deviation score (-4 dB vs. -10 dB, respectively) at the point of mean deviation change identification, especially in the context of unreliable results. Performing two tests per visit offered similar advantages compared with more tests. False positive change detection rates (<2.5%), were similar across all conditions. Patients followed up 6 monthly had less severe mean deviation loss at follow-up compared to 1-year and 2-year follow-up intervals.

Conclusions

Performing two tests per clinical visit at 6 months is practical using SITA-Faster and provides higher detection rates of mean deviation change in comparison with only one test performed per visit and more spaced-out intervals.

Translational relevance

This model provides guidance for selecting the number of tests per visit to detect mean deviation change.

SUBMITTER: Phu J

PROVIDER: S-EPMC8606810 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.