Predicting visual performance from wavefront quality metrics in cataract Konrad Pesudovs Katja Ullrich NH&MRC Centre for Clinical Eye Research, Flinders.

Predicting visual performance from wavefront quality metrics in

cataract

Konrad Pesudovs

Katja UllrichNH&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• 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

• 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)

Wavefront quality metrics• 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)

Results - visual acuity and wavefront quality metrics

• 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 – contrast sensitivity and wavefront quality metrics

• 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

Pupil fraction metrics• 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)

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