Clinical and radiological outcome 1-year after cervical total disc replacement using the Signus ROTAIO - Prosthesis.

INTRODUCTION The instantaneous center of rotation (iCOR) of a motion segment has been shown to correlate with its total range of motion (ROM). Importantly, a correlation of the correct placement of cervical total disc replacement (cTDR) to preserve a physiological iCOR has been previously identified. However, changes of these parameters and the corresponding clinical relevance have hardly been analyzed. This study assesses the radiological and clinical correlation of iCOR and ROM following cTDR. MATERIALS/METHODS A retrospective multi-center observational study was conducted and radiological as well as clinical parameters were evaluated preoperatively and 1 year after cTDR with an unconstrained device. Radiographic parameters including flexion/extension X-rays (flex/ex), ROM, iCOR and the implant position in anterior-posterior direction (IP ap), as well as corresponding clinical parameters [(Neck Disability Index (NDI) and the visual analogue scale (VAS)] were assessed. RESULTS 57 index segments of 53 patients treated with cTDR were analyzed. Pre- and post-operative ROM showed no significant changes (8.0° vs. 10.9°; p > 0.05). Significant correlations between iCOR and IP (Pearson's R: 0.6; p < 0.01) as well as between ROM and IP ap (Pearson's R: - 0.3; p = 0.04) were identified. NDI and VAS improved significantly (p < 0.01). A significant correlation between NDI and IP ap after 12 months (Pearson's R: - 0.39; p < 0.01) was found. CONCLUSION Implantation of the tested prosthesis maintains the ROM and results in a physiological iCOR. The exact position of the device correlates with the clinical outcome and emphasize the importance of implant design and precise implant positioning

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Background: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. Methods: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. Results: Models were developed and integrated into a web-app (https://neurosurgery.shinyapps.io/fuseml/) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59–0.74], back pain (0.72, 95%CI: 0.64–0.79), and leg pain (0.64, 95%CI: 0.54–0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. Conclusions: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk–benefit estimation, truly impacting clinical practice in the era of “personalized medicine” necessitates more robust tools in this patient population

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Background: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. Methods: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. Results: Models were developed and integrated into a web-app (https://neurosurgery.shinyapps.io/fuseml/) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59–0.74], back pain (0.72, 95%CI: 0.64–0.79), and leg pain (0.64, 95%CI: 0.54–0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. Conclusions: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk–benefit estimation, truly impacting clinical practice in the era of “personalized medicine” necessitates more robust tools in this patient population