Computerized Adaptive Tests: Efficient and Precise Assessment of the Patient-Centered Impact of Diabetic Retinopathy.

Autor:	Fenwick EK; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.; Duke-NUS Medical School, Singapore.; Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia., Barnard J; Excel Psychological & Educational Consultancy, Melbourne, Australia.; School of Medical Sciences, University of Sydney, Sydney, Australia., Gan A; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore., Loe BS; The Psychometrics Centre, University of Cambridge, Cambridge, UK., Khadka J; Institute for Choice, University of South Australia, Adelaide, Australia.; Registry of Older South Australians, South Australian Health and Medical Research Institute, Adelaide, Australia.; Health and Social Care Economics Group, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia., Pesudovs K; University of New South Wales, Sydney, Australia.; Anglia Ruskin University, Cambridge, UK., Man R; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.; Duke-NUS Medical School, Singapore., Lee SY; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore., Tan G; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore., Wong TY; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.; Duke-NUS Medical School, Singapore., Lamoureux EL; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.; Duke-NUS Medical School, Singapore.; Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia.
Jazyk:	angličtina
Zdroj:	Translational vision science & technology [Transl Vis Sci Technol] 2020 Jun 03; Vol. 9 (7), pp. 3. Date of Electronic Publication: 2020 Jun 03 (Print Publication: 2020).
DOI:	10.1167/tvst.9.7.3
Abstrakt:	Purpose: Evaluate efficiency, precision, and validity of RetCAT, which comprises ten diabetic retinopathy (DR) quality of life (QoL) computerized adaptive tests (CATs). Methods: In this cross-sectional clinical study, 183 English and/or Mandarin-speaking participants with DR (mean age ± standard deviation [SD] 56.4 ± 11.9 years; 38% proliferative DR [worse eye]) were recruited from retinal clinics in Singapore. Participants answered the RetCAT tests (Symptoms, Activity Limitation, Mobility, Emotional, Health Concerns, Social, Convenience, Economic, Driving, and Lighting), which were capped at seven items each, and other questionnaires, and underwent eye tests. Our primary evaluation focused on RetCAT efficiency (i.e. standard error of measurement [SEM] ± SD achieved and time needed to complete each CAT). Secondary evaluations included an assessment of RetCAT's test precision and validity. Results: Mean SEM across all RetCAT tests was 0.351, ranging from 0.272 ± 0.130 for Economic to 0.484 ± 0.130 for Emotional. Four tests (Mobility, Social, Convenience, and Driving) had a high level of measurement error. The median time to take each RetCAT test was 1.79 minutes, ranging from 1.12 (IQR [interquartile range] 1.63) for Driving to 3.28 (IQR 2.52) for Activity Limitation. Test precision was highest for participants at the most impaired end of the spectrum. Most RetCAT tests displayed expected correlations with other scales (convergent/divergent validity) and were sensitive to DR and/or vision impairment severity levels (criterion validity). Conclusions: RetCAT can provide efficient, precise, and valid measurement of DR-related QoL impact. Future application of RetCAT will employ a stopping rule based on SE rather than number of items to ensure that all tests can detect meaningful differences in person abilities. Responsiveness of RetCAT to treatment interventions must also be determined. Translational Relevance: RetCAT may be useful for measuring the patient-centered impact of DR severity and disease progression and evaluating the effectiveness of new therapies. Competing Interests: Disclosure: E.K. Fenwick, None; J. Barnard, None; A. Gan, None; B.S. Loe, None; J. Khadka, None; K. Pesudovs, None; R. Man, None; S.Y. Lee, None; G. Tan, None; T.Y. Wong, None; E.L. Lamoureux, None (Copyright 2020 The Authors.)
Databáze:	MEDLINE
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