Glaucomatous Visual Field Defect Severity and the Prevalence of Motor Vehicle Collisions in Japanese: A Hospital/Clinic-Based Cross-Sectional Study

Purpose. This study examined the association between the severity of visual field defects and the prevalence of motor vehicle collisions (MVCs) in subjects with primary open-angle glaucoma (POAG). Methods. This is a cross-sectional study. Japanese patients who have had driver’s licence between 40 and 85 years of age were screened for eligibility. Participants answered a questionnaire about MVCs experienced during the previous 5 years. Subjects with POAG were classified as having mild, moderate, or severe visual field defect. We evaluated associations between the severity of POAG and the prevalence of MVCs by logistic regression models. Results. The prevalence of MVCs was significantly associated with the severity of POAG categorized by worse eye MD (control: 30/187 = 16.0%; mild POAG: 17/92 = 18.5%; moderate POAG: 14/60 = 23.3%; severe POAG: 14/47 = 29.8%; P=0.025, Cochran-Armitage trend test). Compared to the control group, the adjusted OR for MVC prevalence in subjects with mild, moderate, or severe POAG in the worse eye was 1.07 (95% CI: 0.55 to 2.10), 1.44 (95% CI: 0.68 to 3.08), and 2.28 (95% CI: 1.07 to 4.88). Conclusions. There is a significant association between the severity of glaucoma in the worse eye MD and the prevalence of MVCs

Multivariable Logistic Regression Model: A Novel Mathematical Model that Predicts Visual Field Sensitivity from Macular Ganglion Cell Complex Thickness in Glaucoma

<div><p>Purpose</p><p>To design a mathematical model that can predict the relationship between the ganglion cell complex (GCC) thickness and visual field sensitivity (VFS) in glaucoma patients.</p><p>Design</p><p>Retrospective cross-sectional case series.</p><p>Method</p><p>Within 3 months from VFS measurements by the Humphrey field analyzer 10-2 program, 83 eyes underwent macular GCC thickness measurements by spectral-domain optical coherence tomography (SD-OCT). Data were used to construct a multiple logistic model that depicted the relationship between the explanatory variables (GCC thickness, age, sex, and spherical equivalent of refractive errors) determined by a regression analysis and the mean VFS corresponding to the SD-OCT scanned area. Analyses were performed in half or 8 segmented local areas as well as in whole scanned areas. A simple logistic model that included GCC thickness as the single explanatory variable was also constructed. The ability of the logistic models to depict the real GCC thickness/VFS in SAP distribution was analyzed by the χ² test of goodness-of-fit. The significance of the model effect was analyzed by analysis of variance (ANOVA).</p><p>Results</p><p>Scatter plots between the GCC thickness and the mean VFS showed sigmoid curves. The χ² test of goodness-of-fit revealed that the multiple logistic models showed a good fit for the real GCC thickness/VFS distribution in all areas except the nasal-inferior-outer area. ANOVA revealed that all of the multiple logistic models significantly predicted the VFS based on the explanatory variables. Although simple logistic models also exhibited significant VFS predictability based on the GCC thickness, the model effect was less than that observed for the multiple logistic models.</p><p>Conclusions</p><p>The currently proposed logistic models are useful methods for depicting relationships between the explanatory variables, including the GCC thickness, and the mean VFS in glaucoma patients.</p></div

Comparison of R² values between presented models and previous models.

<p>GCA = ganglion cell layer and inner plexiform layer.</p><p>* R² values between ganglion cell complex thickness and mean deviation value of 24-2.</p>†<p>R² values between GCA thickness and mean visual filed sensitivity by microperimetry.</p

The outcome of the χ² test of goodness-of-fit, ANOVA, and the R² of the multiple logistic models.

<p>The outcome of the χ² test of goodness-of-fit, ANOVA, and the R² of the multiple logistic models.</p

The estimated parameters of the explanatory variables obtained from the simple logistic regression analysis.

<p>The estimated parameters of the explanatory variables obtained from the simple logistic regression analysis.</p