Factors Accounting for the 4-Year Change in Acuity in Patients Between 50 and 80 Years

Abstract

Purpose It is well known acuity slowly decreases in later decades of life. We wish to determine the extent 4 year longitudinal acuity changes can be accounted for by changes in optical quality, or combination of optical quality metrics and age between 50 and 80. Methods High contrast logMAR acuity, 35 image quality metrics, 4 intraocular scatter metrics, and 4 Lens Opacification Classification System-III metrics and entry age were measured on one eye of each of 148 subjects. Acuity change between baseline and the last visit was regressed against change in each metric for all eyes and a faster changing subset of 50 eyes with a gain or loss of 4 or more letters. Results Average change across 148 subjects was a 1.6 ± 4 letter loss (t148 = 4.31, p < 0.001) and loss for the faster changing subset was 3.4 ± 6.1 letters (t50 = 2.73, p = 0.008). The multiple-regression model for faster changing eyes included change in point spread function entropy, posterior subcapsular cataract, and trefoil and baseline age (sequential r2-adj values 0.19, 0.27, 0.32, 0.34 respectively p = 1.48×10−4 for the full 4 factor model). The same variables entered the multiple-regression model for the full 148 data set where the majority of the acuity measurements were within test re-test error and accounted for less of the variance (r2-adj = 0.15, p = 2.37×10−5). Conclusions Despite being near noise levels for the measurement of acuity, change in optical quality metrics were the most important factors in eyes that lost or gained 4 or more letters of acuity. These findings should be generalizable given our four year acuity change is essentially identical to other studies, and indicate these optical quality markers can be used to help identify those on a faster track to an acuity change.

Correlation between visual acuity and cognitive functions

“…Our findings might become more useful if the ability to predict visual deterioration in the elderly is substantiated 32. This study, given its large cohort, further establishes this association and discloses its relevance at an earlier point than previously thought.…”

Should Pearson's correlation coefficient be avoided?

“…Hence, the best image quality metric accounted for 2.6%, 15.1%, 27.6% and 40% of the variance taking into account various types of logMAR acuity. Koenig et al . studied the effect of various metrics on changes in logMAR and reported r 2 values in the range 3–21%.…”

“…Hence, the best image quality metric accounted for 2.6%, 15.1%, 27.6% and 40% of the variance taking into account various types of logMAR acuity. Koenig et al 20 studied the effect of various metrics on changes in logMAR and reported r 2 values in the range 3-21%. One of the more extreme examples was reported by Nomura et al, 21 intraocular pressure (IOP) in a large Japanese population was inversely correlated with age in men (r = À0.14, p < 0.001).…”

Is there a large sample size problem?

“…As a result, some studies report a significant value of r but very low values of r 2 . Koenig et al . ( N = 148), for example, studied the effect of various metrics on changes in logMAR and reported r 2 values in the range 3–21%.…”

“…As a result, some studies report a significant value of r but very low values of r 2 . Koenig et al 6 (N = 148), for example, studied the effect of various metrics on changes in logMAR and reported r 2 values in the range 3-21%. Moreover, Nomura et al 1 studied intraocular pressure (IOP) in a large Japanese population (N = 1317) and found that IOP was inversely correlated with age in men (r = À0.14, p < 0.001).…”