June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Visual acuity screen failures based on Snellen, ETDRS, and qVA Testing
Author Affiliations & Notes
  • Yukai Zhao
    Center for Neural Science, New York University, New York, New York, United States
  • Luis A Lesmes
    Adaptive Sensory Technology, Inc., San Diego, California, United States
  • Michael Dorr
    Adaptive Sensory Technology, Inc., San Diego, California, United States
  • Zhong-Lin Lu
    Division of Arts and Sciences, New York University Shanghai, Shanghai, Shanghai, China
    Center for Neural Science, New York University, New York, New York, United States
  • Footnotes
    Commercial Relationships   Yukai Zhao None; Luis Lesmes Adaptive Sensory Technology, Inc., Code E (Employment), Adaptive Sensory Technology, Inc., Code I (Personal Financial Interest), Adaptive Sensory Technology, Inc., Code P (Patent); Michael Dorr Adaptive Sensory Technology, Inc., Code E (Employment), Adaptive Sensory Technology, Inc., Code I (Personal Financial Interest), Adaptive Sensory Technology, Inc., Code P (Patent); Zhong-Lin Lu Adaptive Sensory Technology, Inc., Juehua Medical Technology, Ltd, Code I (Personal Financial Interest), Adaptive Sensory Technology, Inc., Juehua Medical Technology, Ltd, Code P (Patent)
  • Footnotes
    Support  EY017491
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2556 – F0510. doi:
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    • Get Citation

      Yukai Zhao, Luis A Lesmes, Michael Dorr, Zhong-Lin Lu; Visual acuity screen failures based on Snellen, ETDRS, and qVA Testing. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2556 – F0510.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : During recruitment for retina trials, screen failure commonly results from discrepancies between inclusion criteria, real-world Snellen VA, and ETDRS VA verified at the study visit. This simulation study evaluated how failure rate is affected by different inclusion criteria and VA tests used for screening: Snellen, ETDRS and quantitative VA (Zhao et al. 2021).

Methods : To screen a population typical for retina (Fig.1 Kaiser 2009), we approximated a general patient (GP) distribution with piecewise uniform distribution of VA thresholds that varied from -0.2 to 1.8 logMAR, and VA range that scaled proportionally: VA range = 0.253 + 0.185 VA threshold (Zhao et al. 2021). We considered four inclusion criteria, bounded by 20/X and 20/320 (X= 25, 32, 40, or 50). To simulate recruitment, patients were randomly sampled from the GP, screened with a VA test (61-letter Snellen, 70-letter ETDRS, and 15-letter qVA), and verified with ETDRS. For each test and criterion, 1000 simulations that each comprised 1000 patient screenings, were used to calculate failure rate as the % of ETDRS scores verified outside of 20/X - 20/320.

Results : Fig. 2 shows that, across criteria, screen failure rates were highest for Snellen (12-15%), and lower for ETDRS (7-9%), and qVA (4-6%). Across tests, most failures were observed when screening for VA (20/50 – 20/320) known to exhibit the highest variability and worst Snellen-ETDRS discrepancy.

Conclusions : In simulations, the well-known deficiencies of Snellen VA lead to the highest screen failure rates (13%), which can be reduced but not eliminated using ETDRS (9%) and qVA (6%). The qVA’s intelligent algorithm with 15-letter testing (5 rows of 3 letters) exhibits potential for feasible real-world screening that can improve patient recruitment for retina.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

Figure 1. General patient population from Kaiser (2009).

Figure 1. General patient population from Kaiser (2009).

 

Figure 2. Screen failure. Mean and standard deviation calculated from 1000 simulations for each test and inclusion criterion.

Figure 2. Screen failure. Mean and standard deviation calculated from 1000 simulations for each test and inclusion criterion.

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