Lower visual acuity predicts worse utility values among patients with type 2 diabetes

Smith, David H.; Johnson, Eric S.; Russell, Allen; Hazlehurst, Brian; Muraki, Cecilia S.; Nichols, Gregory A.; Oglesby, Allen; Betz-Brown, Jonathan

doi:10.1007/s11136-008-9399-1

Cited by 41 publications

(32 citation statements)

References 20 publications

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“…Smith et al [31] fitted a linear regression and found visual angle to be a predictor of EQ-5D utility values. They also fitted a nonparametric ordinal logistic regression and this estimated that any degree of visual impairment would see an increased likelihood of reporting nonperfect utility values.…”

Section: Resultsmentioning

confidence: 99%

A Review of Generic Preference-Based Measures of Health-Related Quality of Life in Visual Disorders

et al. 2012

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ObjectiveThis review examines generic preference-based measures and their ability to reflect health-related quality of life in patients with visual disorders.MethodsA systematic search was undertaken to identify clinical studies of patients with visual disorders where health state utility values were measured and reported. Data were extracted to assess the validity and responsiveness of the measures. A narrative synthesis of the data was undertaken due to the heterogeneity between different studies.ResultsThere was considerable heterogeneity in the 31 studies identified in terms of patient characteristics, visual disorders, and outcomes reported. Vision loss was associated with a reduction in scores across the preference-based measure, but the evidence on validity and responsiveness was mixed. The EQ-5D health-related assessment instrument's performance differed according to condition, with poor performance in age-related macular degeneration (AMD) and diabetic retinopathy. The more limited evidence on the HUI-3 instrument found it performed best in differentiating between severity groups of patients with glaucoma, AMD, cataracts, and diabetic retinopathy. One study reported data on the SF-6D instrument and showed it was able to differentiate between patients with AMD.ConclusionsThe performance of the EQ-5D in visual disorders was mixed. The HUI-3 seemed to perform better in some conditions, but the evidence on this and SF-6D is limited. More head to head comparisons of these three measures are required. The new five-level version of EQ-5D may do better at the milder end of visual function.

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Section: Resultsmentioning

confidence: 99%

A Review of Generic Preference-Based Measures of Health-Related Quality of Life in Visual Disorders

et al. 2012

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“…Mean DR preferences were generally higher than other studies, decreasing incrementally from 0.98 to 0.67, most probably due to methodological differences 47. Data for multi-attribute utilities are scarce, with only two studies using the EQ-5D 49 50. Critically, for all utility measures, the values tended to systematically decrease with worsening levels of visual acuity of DR severity.…”

Section: Impact Of Dr On Prosmentioning

confidence: 72%

Republished article: The impact of diabetic retinopathy: understanding the patient's perspective

Fenwick

Pesudovs²,

Rees³

et al. 2012

Postgraduate Medical Journal

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Understanding the impact of a condition from the patient's perspective is important, and different types of patient-reported outcomes or instruments are available to help with this. This review article summarises the current evidence on the impact of diabetic retinopathy (DR) and its associated vision impairment on patient-reported outcomes. We have included research that has used a range of outcome measures to assess the impact of DR on generic health-related quality of life, utility, vision-functioning and vision-specific quality of life. This review also offers clarification on frequently misused psychometric terminologies to help clinicians and researchers better understand the literature associated with patient-reported outcome research. Overall, the evidence suggests that DR, particularly in its vision-threatening stages, has a substantial, negative impact on the patient. However, our understanding of the impact of DR is currently restricted due to limitations inherent in currently available patient-reported outcome measures. We conclude by discussing potential directions for future research in this area, such as item banking and computer adaptive testing.

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“…In a retrospective study within the Kaiser Permanente Northwest health care system, Smith et al 13 extracted best corrected VA from 2074 free text notes using a computer program written in Python programming language. They validated their results by manual chart review of 100 notes, but no details about the algorithm logic or results of the validation were reported.…”

Section: Discussionmentioning

confidence: 99%

Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records

Baughman

Tsui

et al. 2017

Trans. Vis. Sci. Tech.

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PurposeWith increasing volumes of electronic health record data, algorithm-driven extraction may aid manual extraction. Visual acuity often is extracted manually in vision research. The total visual acuity extraction algorithm (TOVA) is presented and validated for automated extraction of visual acuity from free text, unstructured clinical notes.MethodsConsecutive inpatient ophthalmology notes over an 8-year period from the University of Washington healthcare system in Seattle, WA were used for validation of TOVA. The total visual acuity extraction algorithm applied natural language processing to recognize Snellen visual acuity in free text notes and assign laterality. The best corrected measurement was determined for each eye and converted to logMAR. The algorithm was validated against manual extraction of a subset of notes.ResultsA total of 6266 clinical records were obtained giving 12,452 data points. In a subset of 644 validated notes, comparison of manually extracted data versus TOVA output showed 95% concordance. Interrater reliability testing gave κ statistics of 0.94 (95% confidence interval [CI], 0.89–0.99), 0.96 (95% CI, 0.94–0.98), 0.95 (95% CI, 0.92–0.98), and 0.94 (95% CI, 0.90–0.98) for acuity numerators, denominators, adjustments, and signs, respectively. Pearson correlation coefficient was 0.983. Linear regression showed an R2 of 0.966 (P < 0.0001).ConclusionsThe total visual acuity extraction algorithm is a novel tool for extraction of visual acuity from free text, unstructured clinical notes and provides an open source method of data extraction.Translational RelevanceAutomated visual acuity extraction through natural language processing can be a valuable tool for data extraction from free text ophthalmology notes.

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Lower visual acuity predicts worse utility values among patients with type 2 diabetes

Cited by 41 publications

References 20 publications

A Review of Generic Preference-Based Measures of Health-Related Quality of Life in Visual Disorders

A Review of Generic Preference-Based Measures of Health-Related Quality of Life in Visual Disorders

Republished article: The impact of diabetic retinopathy: understanding the patient's perspective

Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records

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