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The National Eye Institute developed a visual functioning questionnaire (NEI-VFQ) designed to assess health-related quality of life of patients with visual impairments. The developers of the NEI-VFQ distributed the original 52 items into 13 different domains. The recommended method for scoring the NEI-VFQ is to linearly transform the sum of the ordinal ratings to each item within each domain to produce 13 scores. The major shortcoming of this scoring method is that sums of ordinal numbers do not necessarily generate valid measurement scales. However, Rasch models can be used to estimate interval measurement scales from ordinal responses to items. We administered 27 items from the 52-item NEI-VFQ to 341 patients with low vision. Rasch analysis was used to estimate the 'visual ability' required by each item for a particular response (item measures) and to estimate the 'visual ability' of each patient (person measures). The validity of the model was evaluated by examining the distributions of residuals for item and person measures. We observed that the 17 items we tested from the NEI-VFQ that require difficulty ratings produce a valid interval scale for low-vision patients. The estimated person measures of visual ability are linear with log MAR acuity. The ten items that require frequency or level of agreement ratings do not work together to produce a valid interval scale. Rather, these items appear to be confounded by other variables distributed in the patient sample (e.g. psychological state). The visual ability scale estimated from the 17 NEI-VFQ items is proportional to the visual ability scales estimated from two earlier studies that also elicited difficulty ratings from low-vision patients.

Understanding the impact of vision loss on patients with CNV resulting from AMD and assessing treatment benefits requires assessment of overall quality of vision. Primary care physicians and optometrists have an important role in ensuring that patients receive the best possible care, which can be aided by prompt referral to an ophthalmologist or retina specialist and collaboration with low-vision specialists and optometrists who together can make detailed assessments of overall quality of vision, implement appropriate treatment, and design effective rehabilitation strategies.

IMPORTANCE Most patients with low vision are elderly and have functional limitations from other health problems that could add to the functional limitations caused by their visual impairments. OBJECTIVE To identify factors that contribute to visual ability measures in patients who present for outpatient low vision rehabilitation (LVR) services. DESIGN, SETTING, AND PARTICIPANTS As part of a prospective, observational study of new patients seeking outpatient LVR, 779 patients from 28 clinical centers in the United States were enrolled in the Low Vision Rehabilitation Outcomes Study (LVROS) from April 25, 2008, through May 2, 2011. The Activity Inventory (AI), an adaptive visual function questionnaire, was administered to measure overall visual ability and visual ability in 4 functional domains (reading, mobility, visual motor function, and visual information processing) at baseline before LVR. The Geriatric Depression Scale, Telephone Interview for Cognitive Status, and Medical Outcomes Study 36-Item Short-Form Health Survey physical functioning questionnaires were also administered to measure patients’ psychological, cognitive, and physical health states, respectively. MAIN OUTCOMES AND MEASURES Predictors of visual ability and functional domains as measured by the AI. RESULTS Among the 779 patients in the LVROS sample, the mean age was 76.4 years, 33% were male, and the median logMAR visual acuity score was 0.60 (0.40–0.90 interquartile range). Correlations were observed between logMAR visual acuity and baseline visual ability overall (r = −0.42) and for all functional domains. Visual acuity was the strongest predictor of visual ability (P < .001) and reading ability (P < .001) and had a significant independent effect on the other functional domains. Physical ability was independently associated with (P < .001) overall visual ability as well as mobility and visual motor function. Depression had a consistent independent effect (P < .001) on overall visual ability and on all functional domains, whereas cognition had an effect on only reading and mobility (P < .001). CONCLUSIONS AND RELEVANCE Visual ability is a multidimensional construct, with visual acuity, depression, physical ability, and cognition explaining more than one-third of the variance in visual ability as measured by the AI. The significant contributions of the nonvisual factors to visual ability measures and the rehabilitation potential (ie, ceiling) effects they may impose on LVR are important considerations when measuring baseline visual ability and ultimately LVR outcomes in ongoing clinical research.