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Abstract

Purpose
To evaluate if an artificial intelligence (AI) model can better select candidates that would demonstrate visual field (VF) progression in order to shorten the duration or the number of patients needed for a clinical trial

Design
Retrospective cohort study

Methods
7,428 eyes of 3,871 patients from the University of Washington Department of Ophthalmology VF Dataset were included. Progression was defined as at least 5 locations with greater than 7 dB of change compared to baseline on two consecutive tests. Progression for all patients, a subgroup of the fastest progressing based on survival curves, and patients selected based on an elastic net Cox regression model were compared. The model was trained on pointwise threshold deviation values of the first VF, age, gender, laterality and the mean total deviation (MD) at baseline.

Results
13% of all patients met the criteria for progression at five years. Differences in survival were observed when stratified by MD and age (p < 0.0001). Those at risk of progression included patients 60 to 80 years old with an initial MD < -5.0. This subgroup decreased the sample size required to detect progression compared to the entire cohort. The AI model-selected patients required the lowest number of patients for all effect sizes and trial lengths. For a trial length of 3 years and effect size of 30%, the number of patients required was 1656 [95% confidence interval (CI), 1638–1674], 903 [95% CI, 884–922], and 636 [95% CI, 625–646] for the entire cohort, the subgroup, and the model-selected patients, respectively.

Conclusion
An AI model can identify high risk patients to substantially reduce the number of patients needed or study duration required to meet clinical trial endpoints.

Publication Type:	Article
Additional Information:	© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RE Ophthalmology
Departments:	School of Health & Psychological Sciences > Optometry & Visual Sciences
SWORD Depositor:	Symplectic Administrator

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