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Background: Patients surgically treated for lumbar spinal stenosis or cervical radiculopathy report improvement in approximately two out of three cases. Advancements in Machine Learning and the utility of large datasets have enabled the development of prognostic prediction models within spine surgery. This trial investigates if the use of the postoperative outcome prediction model, the Dialogue Support, can alter patient-reported outcome and satisfaction compared to current practice., Methods: This is a prospective, multicenter clinical trial. Patients referred to a spine clinic with cervical radiculopathy or lumbar spinal stenosis will be screened for eligibility. Participants will be assessed at baseline upon recruitment and at 12 months follow-up. The Dialogue Support will be used on all participants, and they will thereafter be placed into either a surgical or a non-surgical treatment arm, depending on the decision made between patient and surgeon. The surgical treatment group will be studied separately based on diagnosis of either cervical radiculopathy or lumbar spinal stenosis. Both the surgical and the non-surgical group will be compared to a retrospective matched control group retrieved from the Swespine register, on which the Dialogue Support has not been used. The primary outcome measure is global assessment regarding leg/arm pain in the surgical treatment group. Secondary outcome measures include patient satisfaction, Oswestry Disability Index (ODI), EQ-5D, and Numeric Rating Scales (NRS) for pain. In the non-surgical treatment group primary outcome measures are EQ-5D and mortality, as part of a selection bias analysis., Discussion: The findings of this study may provide evidence on whether the use of an advanced digital decision tool can alter patient-reported outcomes after surgery., Trial Registration: The trial was retrospectively registered at ClinicalTrials.gov on April 17th, 2023, NCT05817747., Protocol Version: 1., Trial Design: Clinical multicenter trial., (© 2024. The Author(s).)

Purpose: The Oswestry Disability Index (ODI) and the Neck Disability Index (NDI) scoring algorithms used by the Swedish spine register (Swespine) until April 2022 handled missing items somewhat differently than the original algorithms. The purpose of the current study was to evaluate possible differences in the ODI and NDI scores between the Swespine and the original scoring algorithms., Methods: Patients surgically treated for degenerative conditions of the lumbar or cervical spine between 2003-2019 (lumbar) and 2006-2019 (cervical) were identified in Swespine. Preoperative and 1-year postoperative ODI/NDI data were used to evaluate differences between the Swespine and the original ODI/NDI algorithms with adjustment for at most 1 or 2 missing items using mean imputation., Results: The preoperative as well as the 1-year postoperative ODI/NDI were approximately 1 unit out of 100 smaller for the Swespine algorithm, irrespective of adjustment model. The differences between preoperative and postoperative ODI/NDI scores were similar between the Swespine and the original scoring algorithms. There were occasional statistically significant differences between the preoperative-postoperative differences due to large sample sizes., Conclusions: The Swespine algorithms, used until April 2022, underestimated the ODI and NDI by approximately 1 out of 100 units compared with the original algorithms. In addition, there were no statistically significant differences between the original algorithms when adjusting for at most 1 or 2 missing items. The algorithm has now been changed, also for historical data., (© 2022. The Author(s).)