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Predicting visual performance from wavefront quality metrics in cataract. Konrad Pesudovs Katja Ullrich NH&MRC Centre for Clinical Eye Research, Flinders Medical Centre & Flinders University, Adelaide, South Australia. Financial disclosure: The authors have no financial interest.

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Predicting visual performance from wavefront quality metrics in cataract

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Predicting visual performance from wavefront quality metrics in cataract l.jpg

Predicting visual performance from wavefront quality metrics in cataract

Konrad Pesudovs

Katja Ullrich

NH&MRC Centre for Clinical Eye Research, Flinders Medical Centre & Flinders University, Adelaide, South Australia

Financial disclosure: The authors have no financial interest


Background and purpose l.jpg

Background and Purpose

  • Cataract affects visual performance via higher order aberrations and light scatter

  • Wavefront aberrations occurring in cataract have been described in terms of the Zernike polynomial decomposition but neither Zernike terms nor RMS predict visual performance

  • Other methods for organising wavefront data exist – wavefront quality metrics

  • Attempts to connect wavefront quality metrics to visual performance in cataract are lacking

    PURPOSE: To determine which wavefront quality metrics are most predictive of visual performance in patients with cataract


Population and visual performance l.jpg

Prospective, cross-sectional study of consecutive patients attending the Cataract Assessment Clinic at Flinders Medical Centre

Inclusions – all types of cataract

Exclusions – ocular comorbidity, unable to measure whole eye wavefront

206 eyes, age 73 years, 58% female

The clinical assessment was conducted by one clinician-KP

Refraction and best corrected

High contrast visual acuity(VA)

Pelli-Robson contrast sensitivity(PRCS)

Pelli-Robson contrast sensitivity under glare (PRCSglare)

Population and Visual Performance


Wavefront quality metrics l.jpg

Whole eye wavefront sensing with Wavefront Sciences COAS-HD

Wavefront data exported to VOLPro software v7.25 (Sarver and Associates) and 10th order Zernike expansion derived

Zernike data exported to GetMetrics v.2.02.006 (University of Houston, College of Optometry) by Thibos and Applegate for calculation of wavefront quality metrics

31 metrics of wavefront quality designed to be predictive of visual performance were calculated for the pupil plane and the image plane as per: Thibos LN, Hong X, Bradley A, Applegate RA. Accuracy and precision of objective refraction from wavefront aberrations. J Vis 2004;4(4):329-51.

Linear Regression with SPSS Software V15.0 (SPSS Inc)

Wavefront quality metrics


Results visual acuity and wavefront quality metrics l.jpg

Visual acuity and logPFWc, r2=-0.37, p<0.001

The strongest correlate of all three measures of visual performance was the pupil fraction metric PFWc

Results - visual acuity and wavefront quality metrics


Results contrast sensitivity and wavefront quality metrics l.jpg

Pelli-Robson contrast sensitivity and logPFWc, r2=0.39, p<0.001

Pelli-Robson contrast sensitivity glare & logPFWc, r2=0.32, p<0.001

The strongest correlate of each measure of contrast sensitivity was the pupil fraction metric PFWc

Results – contrast sensitivity and wavefront quality metrics


Pupil fraction metrics l.jpg

Pupil fraction is defined as the fraction of the pupil area for which the optical quality of the eye is good

The critical pupil method uses an “area of good pupil” which is a concentric zone

The red circle indicate the largest concentric zone for which the wavefront has reasonably good quality

PFWc which is a critical pupil defined as the concentric area for which RMSw<criterion (λ/4)

Pupil fraction metrics


Conclusion l.jpg

Conclusion

  • Pupil fraction metrics are the best correlates with visual performance in cataract, and also have performed well in normal eyes

  • Pupil fraction metrics should be used to organise wavefront aberration data so as to be predictive of visual performance


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