Simple Clinical Techniques to Evaluate Visual Function in Patients with Early Cataract

Clinical Vision Research Unit, University of Bradford, West Yorkshire, United Kingdom.
Optometry and Vision Science (Impact Factor: 1.6). 12/1990; 67(11):822-5. DOI: 10.1097/00006324-199011000-00006
Source: PubMed

ABSTRACT Among the 80 subjects who were recruited with normal retinal and neural function, 54 had cataract and a visual acuity (VA) better than 6/24. The 26 age-matched subjects had clear media. Contrast sensitivity (CS) at low and intermediate spatial frequencies was measured using the Pelli-Robson letter chart. Two measures of glare disability (GD) were obtained using the Mentor Brightness Acuity Tester (BAT) in conjunction with a logMAR VA chart and the Pelli-Robson chart. Although CS is predominantly affected at high spatial frequencies in early cataract, we found that some subjects had reduced scores on the Pelli-Robson chart. This CS loss could not be predicted from VA measurements and was particularly found in subjects with posterior subcapsular cataract. High GD scores were found in a number of subjects with relatively good VA and could not be predicted from results of VA or CS. We suggest that CS and GD measurements using the Pelli-Robson chart and the BAT provide valuable information regarding the management of patients with early cataract.

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    • "However, a major limitation of VA measurement techniques is that they measure minimum angle of visual resolution using high-contrast targets only, whereas the real world is made up of objects across a range of spatial frequencies and contrast. It has been demonstrated that contrast sensitivity (CS) is more closely related to levels of disability and health-related quality of life related to vision in patients with ocular disease [7] [8] [9] and as such has been recommended for use in the clinical setting, particularly in low vision clinics [10], and also in the detection and monitoring of eye diseases such as glaucoma [11], cataract [12], diabetic retinopathy [13], and optic neuritis [14], as well as in the postoperative assessment of patients having undergone laser refractive procedures [15]. However, and in spite of these observations , CS is still not widely measured in the clinical setting [16], possibly reflecting difficulties in incorporating these measures into a busy clinical practice and a lack of appreciation of its value amongst eye care professionals. "
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    ABSTRACT: Purpose. To compare measures of visual acuity (VA) and contrast sensitivity (CS) from the Thompson Xpert 2000 and MultiQuity (MiQ) devices. Methods. Corrected distance VA (CDVA) and CS were measured in the right eye of 73 subjects, on an established system (Thompson Xpert) and a novel system (MiQ 720). Regression was used to convert MiQ scores into the Thompson scale. Agreement between the converted MiQ and Thompson scores was investigated using standard agreement indices. Test-retest variability for both devices was also investigated, for a separate sample of 24 subjects. Results. For CDVA, agreement was strong between the MiQ and Thomson devices (accuracy = 0.993, precision = 0.889, CCC = 0.883). For CS, agreement was also strong (accuracy = 0.996, precision = 0.911, CCC = 0.907). Agreement was unaffected by demographic variables or by presence/absence of ocular pathology. Test-retest agreement indices for both devices were excellent: in the range 0.88-0.96 for CDVA and in the range 0.90-0.98 for CS. Conclusion. MiQ measurements exhibit strong agreement with corresponding Thomson measurements, and test-retest results are good for both devices. Agreement between the two devices is unaffected by age or ocular pathology.
    Journal of Ophthalmology 12/2014; 2014. DOI:10.1155/2014/180317 · 1.43 Impact Factor
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    • "On the other hand, contrast sensitivity is also impaired by several other ocular diseases (e.g. cataract, glaucoma, diabetic retinopathy) [95-97], suggesting that multiple mechanisms contribute to diminished contrast sensitivity with age. It is interesting to note that contrast sensitivity may represent a general indicator of a subject's sensory perception, and that useful prognostic data might have been obtained from other sensory systems not evaluated during baseline exams (e.g., hearing test). "
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    ABSTRACT: Prediction of long-term survival in healthy adults requires recognition of features that serve as early indicators of successful aging. The aims of this study were to identify predictors of long-term survival in older women and to develop a multivariable model based upon longitudinal data from the Study of Osteoporotic Fractures (SOF). We considered only the youngest subjects (n = 4,097) enrolled in the SOF cohort (65 to 69 years of age) and excluded older SOF subjects more likely to exhibit a "frail" phenotype. A total of 377 phenotypic measures were screened to determine which were of most value for prediction of long-term (19-year) survival. Prognostic capacity of individual predictors, and combinations of predictors, was evaluated using a cross-validation criterion with prediction accuracy assessed according to time-specific AUC statistics. Visual contrast sensitivity score was among the top 5 individual predictors relative to all 377 variables evaluated (mean AUC = 0.570). A 13-variable model with strong predictive performance was generated using a forward search strategy (mean AUC = 0.673). Variables within this model included a measure of physical function, smoking and diabetes status, self-reported health, contrast sensitivity, and functional status indices reflecting cumulative number of daily living impairments (HR >or= 0.879 or RH <or= 1.131; P < 0.001). We evaluated this model and show that it predicts long-term survival among subjects assigned differing causes of death (e.g., cancer, cardiovascular disease; P < 0.01). For an average follow-up time of 20 years, output from the model was associated with multiple outcomes among survivors, such as tests of cognitive function, geriatric depression, number of daily living impairments and grip strength (P < 0.03). The multivariate model we developed characterizes a "healthy aging" phenotype based upon an integration of measures that together reflect multiple dimensions of an aging adult (65-69 years of age). Age-sensitive components of this model may be of value as biomarkers in human studies that evaluate anti-aging interventions. Our methodology could be applied to data from other longitudinal cohorts to generalize these findings, identify additional predictors of long-term survival, and to further develop the "healthy aging" concept.
    BMC Geriatrics 08/2010; 10:55. DOI:10.1186/1471-2318-10-55 · 1.68 Impact Factor
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