1. 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

2. 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

3. 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)

4. Wavefront quality metrics Whole eye wavefront sensing with Wavefront Sciences COAS-HD Wavefront data exported to VOLPro software v7.25 (Sarver and Associates) and 10 th 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)

5. Results - visual acuity and wavefront quality metrics Visual acuity and logPFWc, r 2 =-0.37, p<0.001 The strongest correlate of all three measures of visual performance was the pupil fraction metric PFWc

6. Results – contrast sensitivity and wavefront quality metrics Pelli-Robson contrast sensitivity and logPFWc, r 2 =0.39, p<0.001 Pelli-Robson contrast sensitivity glare & logPFWc, r 2 =0.32, p<0.001 The strongest correlate of each measure of contrast sensitivity was the pupil fraction metric PFWc

7. 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 RMS w <criterion ( λ /4)

8. 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