Surgical correction of fixed thoracolumbar deformity is usually achieved by estimating the preoperatively planned correction angles during surgery and is therefore prone to inaccuracy. This is particularly problematic in biplanar deformities. To overcome these difficulties, 3D model for planning, preparation, and simulation of an asymmetric pedicle subtraction osteotomy (aPSO) was printed and used to realign coronal and sagittal balance in case of rigid degenerative kyphoscoliosis. A 59-year-old woman presented with severe back pain and spinal claudication and was diagnosed with a rigid kyphoscoliosis with multilevel spinal stenosis. Spino-pelvic parameters were measured preoperatively (pelvic incidence 47° [PI], lumbar lordosis 18° [LL]; pelvic tilt 42° [PT], T1 pelvic angle 40° [TPA], Cobb angle 33°, sagittal vertical axis 10.5 cm [SVA]). To aid the complex deformity in the sagittal and coronal plane, a 1:1 3D model of the spine was printed according to the preoperative computed tomography (CT). With the use of a rebalancing software, the spine was prepared in vitro as a model for intraoperative realignment and the correction was preoperatively simulated. Surgery was accomplished according to the preoperative software-guided plan. Asymmetric pedicle subtraction osteotomy (aPSO) of L3 identical to the 3D model was performed. Additionally, a Smith-Peterson osteotomy of L4/5 with transforaminal lumbar interbody fusion (TLIF) and laminectomy of L2-S1 with pedicle screw instrumentation TH12-S1 was accomplished. Postoperative radiological parameters revealed good success (LL 40°, SVA 6 cm, PT 19°, TPA 22°, and a Cobb angle of 8°). Improvement of the Oswestry disability index (ODI) of 42 to 18, the visual analog scale (VAS) of 8 to 1, and walking distance 100 to 8000 m compared to preoperatively resulted at 24 months follow-up. The precise coronal and sagittal correction of a rigid degenerative kyphoscoliosis presents a major challenge. Asymmetric PSO is able to realign the thoracolumbar spine in both the coronal and sagittal planes. The creation of an in vitro 3D-printed model of a patient's spinal deformity in combination with a software to calculate the correction angles facilitates preoperative planning and implementation of aPSO.
IntroductionRobotic guidance (RG) and computer-assisted navigation (NV) have seen increased adoption in instrumented spine surgery over the last decade. Although there exists some evidence that these techniques increase radiological pedicle screw accuracy compared with conventional freehand (FH) surgery, this may not directly translate to any tangible clinical benefits, especially considering the relatively high inherent costs. As a non-randomised, expertise-based study, the European Robotic Spinal Instrumentation Study aims to create prospective multicentre evidence on the potential comparative clinical benefits of RG, NV and FH in a real-world setting.Methods and analysisPatients are allocated in a non-randomised, non-blinded fashion to the RG, NV or FH arms. Adult patients that are to undergo thoracolumbar pedicle screw instrumentation for degenerative pathologies, infections, vertebral tumours or fractures are considered for inclusion. Deformity correction and surgery at more than five levels represent exclusion criteria. Follow-up takes place at 6 weeks, as well as 12 and 24 months. The primary endpoint is defined as the time to revision surgery for a malpositioned or loosened pedicle screw within the first postoperative year. Secondary endpoints include patient-reported back and leg pain, as well as Oswestry Disability Index and EuroQOL 5-dimension questionnaires. Use of analgesic medication and work status are recorded. The primary analysis, conducted on the 12-month data, is carried out according to the intention-to-treat principle. The primary endpoint is analysed using crude and adjusted Cox proportional hazards models. Patient-reported outcomes are analysed using baseline-adjusted linear mixed models. The study is monitored according to a prespecified monitoring plan.Ethics and disseminationThe study protocol is approved by the appropriate national and local authorities. Written informed consent is obtained from all participants. The final results will be published in an international peer-reviewed journal.Trial registration numberClinical Trials.gov registryNCT03398915; Pre-results, recruiting stage.
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.
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