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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Humans, Hydromorphone, Low Back Pain, Osteoarthritis, Quality-Adjusted Life Years, Sleep.
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Do mapping MOS Sleep Scale scores to SF-6D utility index? Context: Deriving preference scores for the Medical Outcomes Study (MOS) Sleep Scale would enable its use in cost-utility analyses. The objective of this study was to map scores of the MOS Sleep Scale to a preference-based health-state utility index (SF-6D) scored from the SF-36 Health Survey (SF-36). Three datasets were used: (1) the MOS study, a 4-year observational study of chronically ill patients, (2) a 7-week open-label, non-comparative clinical trial of an osmotic controlled-release oral delivery system (OROS) hydromorphone in the treatment of chronic low back pain (CLBP), and (3) a 6-week open-label randomized controlled trial of OROS hydromorphone in the treatment of pain associated with chronic osteoarthritis (OA). Various models were tested, where SF-6D was regressed onto the Sleep Problem Index-II (SLP9) in 1000 random half (developmental) samples of the MOS (n = 1413). The best fitting model was applied to the other 1000 random half (cross-validation) samples of the MOS (n = 1412), and to the two trial samples (n = 199 in the CLBP trial; n = 124 in the OA trial). The best fitting model in the MOS samples included a quadratic term for the SLP9 which explained 34% of the variance in SF-6D in the developmental samples. Errors in prediction were greatest at higher SLP9 scores. Addition of demographic and clinical variables to the model explained minimal incremental amounts of variance (< 5%) in SF-6D scores. These results were replicated in the cross-validation MOS samples. In both developmental and cross-validation MOS samples, mean predicted and observed SF-6D scores were nearly identical. When the mapping algorithm developed in the MOS was applied to the CLBP sample, mean predicted SF-6D scores were 0.09 points higher than observed SF-6D scores at both baseline and final visits, while changes in predicted and observed SF-6D scores were identical.
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Results indicate that it is possible to map MOS SLP9 to SF-6D yielding useable preference-based scores essential for cost-utility analyses. A limitation concerns the interpretation of SF-6D scores estimated from SLP9 scores above 60, where the prediction errors increased considerably. Final Decision: yes