Author(s): Paradowski PT, Witoski D, Kska R, Roos EM
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Abstract BACKGROUND: Knee Injury and Osteoarthritis Outcome Score (KOOS) is available in over 30 languages and a commonly used Patient-Reported Outcome (PRO) for assessment of treatment effects following knee surgery. The aim of the study was to report the linguistic translational process and evaluate the psychometric properties of the Polish version of the KOOS questionnaire. METHODS: We translated and culturally adapted the KOOS according to current guidelines for use in Poland. Patients who had undergone anterior cruciate ligament reconstruction (ACLR) completed the KOOS and Short Form 36 Health Survey (SF-36). We evaluated floor/ceiling effects, reliability (using Cronbach's alpha, intraclass correlation coefficients (ICC) and measurement error), convergent and divergent construct validity (using four a priori stated hypotheses) and responsiveness (using data obtained prior to and one year after ACLR and described by both effect size (ES) and standardized response mean (SRM)). RESULTS: The clinical study population consisted of 72 subjects (mean age 29.8, 28\% women). We did not observe floor effects in any KOOS subscales neither pre- nor postoperatively. As expected, ceiling effects were found postoperatively for the subscales Pain and ADL in this cohort assessed on average 1.3 year after surgery as more than 15\% reported no pain or limitations in daily activities. The Cronbach's alpha was above 0.9 for all subscales indicating excellent internal consistency. The test-retest reliability of all KOOS subscales at one-year postoperatively was excellent with ICCs exceeding 0.86 for all subscales. The minimal detectable change on group level ranged from 1.3 to 2.4, and on an individual level from 10.9 to 20.2. Responsiveness was demonstrated since the expected pattern of effect sizes between subscales following ACLR was found. CONCLUSIONS: We found the Polish version of the KOOS to be a valid and reliable instrument for use in patient groups having ACLR. We caution against monitoring individual patients since the smallest change considered clinically relevant cannot reliably be detected.
This article was published in Health Qual Life Outcomes
and referenced in Advances in Robotics & Automation