July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Accuracy and Precision of the ETDRS Chart, E-ETDRS and Bayesian qVA Method
Author Affiliations & Notes
  • Yukai Zhao
    Psychology, the Ohio State University, Columbus, Ohio, United States
  • Luis Andres Lesmes
    Adaptive Sensory Technology, Inc, San Diego, California, United States
  • Michael Dorr
    Technical University of Munich, Munich, Germany
  • Peter Bex
    Psychology, Northeastern Unversity, Boston, Massachusetts, United States
  • Zhong-Lin Lu
    Psychology, the Ohio State University, Columbus, Ohio, United States
  • Footnotes
    Commercial Relationships   Yukai Zhao, None; Luis Lesmes, Adaptive Sensory Technology, Inc (I), Adaptive Sensory Technology, Inc (E), Adaptive Sensory Technology, Inc (P); Michael Dorr, Adaptive Sensory Technology, Inc (I), Adaptive Sensory Technology, Inc (P); Peter Bex, Adaptive Sensory Technology, Inc (I), Adaptive Sensory Technology, Inc (P); Zhong-Lin Lu, Adaptive Sensory Technology, Inc (I), Adaptive Sensory Technology, Inc (P)
  • Footnotes
    Support  NEI grants EY017491 and EY021553
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 5908. doi:
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      Yukai Zhao, Luis Andres Lesmes, Michael Dorr, Peter Bex, Zhong-Lin Lu; Accuracy and Precision of the ETDRS Chart, E-ETDRS and Bayesian qVA Method. Invest. Ophthalmol. Vis. Sci. 2019;60(9):5908.

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

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Abstract

Purpose : Visual acuity (VA) remains a fundamental measure of visual function. The accuracy and precision of VA assessment are extremely important for its use in disease management, therapeutic development, and occupation qualification. Although the ETDRS chart (Ferris III, et al., 1982) with different termination rules and E-ETDRS (Beck et al., 2003) provide the standard VA assessment in clinical trials, different termination rules may yield different VA scores for the same observer (Carkeet, 2001). Recently, Lesmes (2018) introduced a Bayesian adaptive qVA test that estimates the threshold and range of the VA psychometric function (PF) via higher sampling resolution of optotype size and a rich model of row-based PFs (Figure 1). In this study, we use Monte Carlo simulations to evaluate the accuracy and precision of VA assessment using ETDRS with 6 termination rules in current practice, E-ETDRS, and qVA.

Methods : Observers with three different “true” VA thresholds (-0.3, 0.25, and 1 logMAR) and range (0.15, 0.3 and 0.6 logMAR) were simulated. The row-based PFs in qVA were used to simulate observer performance. The six termination rules were: reading the whole chart, or stopping at the line with at least 1, 2, 3, 4, or 5 mistakes. Each qVA run consisted of 45 trials with a row of 3 optotypes in each trial. Each observer was assessed 1000 times by each method.

Results : The qVA generated the most accurate (bias: -0.004 to 0.004 logMAR) and precise (SD: 0.010 to 0.037 logMAR) assessment of VA thresholds, across observers (Figure 2). The ETDRS chart with different termination rules yielded VA scores with biases between -0.228 and 0.173 logMAR and SDs between 0.025 and 0.126 logMAR. Among the 6 termination rules, the ETDRS with the 3-mistake termination rule yielded the smallest bias (-0.018 to 0.079 logMAR), and the ETDRS with the whole-chart termination rule yielded the smallest SD (0.026 to 0.071 logMAR). The bias (0.096 to 0.057 logMAR) and SD (0.025 to 0.090 logMAR) of the E-ETDRS were similar to those of the ETDRS with the 5-mistake termination rule.

Conclusions : The ETDRS with the 6 termination rules and E-ETDRS do not converge to the true acuity of the simulated observers. The qVA provides unbiased and most precise VA assessment.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Fig.1 Row-based psychometric functions describe the probability of reporting 0-6 letters correct from a row of 5 letters presented (Lesmes, 2018).

Fig.1 Row-based psychometric functions describe the probability of reporting 0-6 letters correct from a row of 5 letters presented (Lesmes, 2018).

 

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