Multiple retinal detachment surgery: A functional outcome study. The Houston Vision Assessment Test-Retina (HVAT-Retina)

Retinal detachment is a common ophthalmologic procedure, and outcome is typically measured by a single factor-improvement in visual acuity. Health related functional outcome testing, which quantifies patient's self-reported perception of impairment, can be integrated with objective clinical findings. Based on the patient's self-assessed lifestyle impairment, the physician and patient together can make an informed decision on the treatment that is most likely to benefit the patient. ^ A functional outcome test (the Houston Vision Assessment Test-Retina; HVAT-Retina) was developed and validated in patients with multiple retinal detachments in the same eye. The HVAT-Retina divides an estimated total impairment into subcomponents: contribution of visual disability (potentially correctable by retinal detachment surgery) and nonvisual physical disabilities (co-morbidities not affected by retinal detachment surgery. ^ Seventy-six patients participated in this prospective multicenter study. Seven patients were excluded from the analysis because they were not certain of their answers. Cronbach's alpha coefficient was 0.91 for presurgery HVAT-Retina and 0.94 post-surgery. The item-to-total correlation ranged from 0.50 to 0.88. Visual impairment score improved by 9 points from pre-surgery (p = 0.0003). Physical impairment score also improved from pre-surgery (p = 0.0002). ^ In conclusion, the results of this study demonstrate that the instrument is reliable and valid in patients presenting with recurrent retinal detachments. The HVAT-Retina is a simple instrument and does not burden the patient or the health professional in terms of time or cost. It may be self-administrated, not requiring an interviewer. Because the HVAT-Retina was designed to demonstrate outcomes perceivable by the patient, it has the potential to guide the decision making process between patient and physician. ^

Validity of using item response theory to analyze data from the Work Limitations Questionnaire

The Work Limitations Questionnaire (WLQ) is used to determine the amount of work loss and productivity which stem from certain health conditions, including rheumatoid arthritis and cancer. The questionnaire is currently scored using methodology from Classical Test Theory. Item Response Theory, on the other hand, is a theory based on analyzing item responses. This study wanted to determine the validity of using Item Response Theory (IRT), to analyze data from the WLQ. Item responses from 572 employed adults with dysthymia, major depressive disorder (MDD), double depressive disorder (both dysthymia and MDD), rheumatoid arthritis and healthy individuals were used to determine the validity of IRT (Adler et al., 2006).^ PARSCALE, which is IRT software from Scientific Software International, Inc., was used to calculate estimates of the work limitations based on item responses from the WLQ. These estimates, also known as ability estimates, were then correlated with the raw score estimates calculated from the sum of all the items responses. Concurrent validity, which claims a measurement is valid if the correlation between the new measurement and the valid measurement is greater or equal to .90, was used to determine the validity of IRT methodology for the WLQ. Ability estimates from IRT were found to be somewhat highly correlated with the raw scores from the WLQ (above .80). However, the only subscale which had a high enough correlation for IRT to be considered valid was the time management subscale (r = .90). All other subscales, mental/interpersonal, physical, and output, did not produce valid IRT ability estimates.^ An explanation for these lower than expected correlations can be explained by the outliers found in the sample. Also, acquiescent responding (AR) bias, which is caused by the tendency for people to respond the same way to every question on a questionnaire, and the multidimensionality of the questionnaire (the WLQ is composed of four dimensions and thus four different latent variables) probably had a major impact on the IRT estimates. Furthermore, it is possible that the mental/interpersonal dimension violated the monotonocity assumption of IRT causing PARSCALE to fail to run for these estimates. The monotonicity assumption needs to be checked for the mental/interpersonal dimension. Furthermore, the use of multidimensional IRT methods would most likely remove the AR bias and increase the validity of using IRT to analyze data from the WLQ.^