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Objective Lumbar fusion with implantation of interbody cage is a common procedure for treatment of lumbar degenerative disease. This study aims to compare the fusion and subsidence rates of titanium (Ti) versus polyetheretherketone (PEEK) interbody cages after posterior lumbar interbody fusion and investigate the effect of clinical and radiological outcomes following fusion on patient-reported outcomes.
Methods A systematic search strategy of 4 electronic databases (MEDLINE, Embase, Web of Science, and Cochrane) was conducted using different MeSH (medical subject headings) terms until January 2020. Pooled odds ratios (ORs) with 95% confidence intervals (CI) were calculated using fixed and random-effect models based upon the heterogeneity (I2) to estimate the association between interbody cages and the measured outcomes.
Results A total of 1,094 patients from 11 studies were reviewed. The final analysis included 421 patients (38.5%) who had lumbar surgery using a Ti and/or a Ti-coated interbody cage and 673 patient (61.5%) who had lumbar surgery using a PEEK cage. Overall, PEEK interbody devices were associated with a significantly lower fusion rate compared with Ti interbody devices (OR, 0.62; 95% CI, 0.41–0.93; p = 0.02). There was no difference in subsidence rates between Ti and PEEK groups (OR, 0.91; 95% CI, 0.54–1.52; p = 0.71). Also, there were no statistically significant differences in visual analogue scale (VAS)-low back pain (p = 0.14) and Japanese Orthopedic Association scale (p = 0.86) between the 2 groups. However, the PEEK group had lower odds of leg pain after surgery compared to the Ti group (OR [VAS-leg], 0.61; 95% CI, 0.28–0.94; p = 0.003).
Conclusion Ti and Ti-coated PEEK cages used for posterior lumbar interbody fusion are associated with similar rates of subsidence, but a higher rate of fusion compared to PEEK interbody cages. Randomized controlled trials are needed to better assess the effect of cage materials and potential factors that could influence the outcomes of interbody lumbar fusion.
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Robert M. Koffie, Alexandra M. Giantini Larsen, Benjamin L. Grannan, Muhamed Hadzipasic, Vijay Yanamadala, Laura Van Beaver, Ganesh M. Shankar, John H. Shin
Neurospine 2020;17(3):659-665. Published online February 2, 2020
Objective Conventional techniques for atlantoaxial fixation and fusion typically pass cables or wires underneath C1 lamina to secure the bone graft between the posterior elements of C1–2, which leads to complications such as cerebrospinal fluid (CSF) leak and neurological injury. With the evolution of fixation hardware, we propose a novel C1–2 fixation technique that avoids the morbidity and complications associated with sublaminar cables and wires.
Methods This technique entails wedging and anchoring a structural iliac crest graft between C1 and C2 for interlaminar arthrodesis and securing it using a 0-Prolene suture at the time of C1 lateral mass and C2 pars interarticularis screw fixation.
Results We identified 32 patients who underwent surgery for atlantoaxial with our technique. A 60% improvement in pain-related disability from preoperative baseline was demonstrated by Neck Disability Index (p < 0.001). There were no neurologic deficits. Complications included 2 patients CSF leaks related to presenting trauma, 1 patient with surgical site infection, and 1 patient with transient dysphagia. The rate of radiographic atlantoaxial fusion was 96.8% at 6 months, with no evidence of instrumentation failure, graft dislodgement, or graft related complications.
Conclusion We demonstrate a novel technique for C1–2 arthrodesis that is a safe and effective option for atlantoaxial fusion.
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Artificial Intelligence-Driven Prediction Modeling and Decision Making in Spine Surgery Using Hybrid Machine Learning Models Babak Saravi, Frank Hassel, Sara Ülkümen, Alisia Zink, Veronika Shavlokhova, Sebastien Couillard-Despres, Martin Boeker, Peter Obid, Gernot Lang Journal of Personalized Medicine.2022; 12(4): 509. CrossRef
Mechanical Failure After Total En Bloc Spondylectomy and Salvage Surgery Shin Won Kwon, Chun Kee Chung, Young Il Won, Woon Tak Yuh, Sung Bae Park, Seung Heon Yang, Chang Hyun Lee, John M. Rhee, Kyoung-Tae Kim, Chi Heon Kim Neurospine.2022; 19(1): 146. CrossRef
Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI James Thomas Patrick Decourcy Hallinan, Lei Zhu, Wenqiao Zhang, Desmond Shi Wei Lim, Sangeetha Baskar, Xi Zhen Low, Kuan Yuen Yeong, Ee Chin Teo, Nesaretnam Barr Kumarakulasinghe, Qai Ven Yap, Yiong Huak Chan, Shuxun Lin, Jiong Hao Tan, Naresh Kumar, Bala Frontiers in Oncology.2022;[Epub] CrossRef
Deep Learning Model for Grading Metastatic Epidural Spinal Cord Compression on Staging CT James Thomas Patrick Decourcy Hallinan, Lei Zhu, Wenqiao Zhang, Tricia Kuah, Desmond Shi Wei Lim, Xi Zhen Low, Amanda J. L. Cheng, Sterling Ellis Eide, Han Yang Ong, Faimee Erwan Muhamat Nor, Ahmed Mohamed Alsooreti, Mona I. AlMuhaish, Kuan Yuen Yeong, Ee Cancers.2022; 14(13): 3219. CrossRef
Current and Emerging Approaches for Spine Tumor Treatment Bogdan Costăchescu, Adelina-Gabriela Niculescu, Bogdan Florin Iliescu, Marius Gabriel Dabija, Alexandru Mihai Grumezescu, Daniel Rotariu International Journal of Molecular Sciences.2022; 23(24): 15680. CrossRef
Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach Kevin J. DiSilvestro, Ashwin Veeramani, Christopher L. McDonald, Andrew S. Zhang, Eren O. Kuris, Wesley M. Durand, Eric M. Cohen, Alan H. Daniels World Neurosurgery.2021; 146: e917. CrossRef
Is the Spinal Instability Neoplastic Score Accurate and Reliable in Predicting Vertebral Compression Fractures for Spinal Metastasis? A Systematic Review and Qualitative Analysis Chang-Hyun Lee, Jae Taek Hong, Sun-Ho Lee, Seong Yi, Moon-Jun Sohn, Sung Hwan Kim, Chun Kee Chung Journal of Korean Neurosurgical Society.2021; 64(1): 4. CrossRef
Accuracy and precision of the spinal instability neoplastic score (SINS) for predicting vertebral compression fractures after radiotherapy in spinal metastases: a meta-analysis Young Rak Kim, Chang-Hyun Lee, Seung Heon Yang, Seung-Jae Hyun, Chi Heon Kim, Sung Bae Park, Ki-Jeong Kim, Chun Kee Chung Scientific Reports.2021;[Epub] CrossRef
Machine learning in spine surgery: Predictive analytics, imaging applications and next steps Rushikesh S. Joshi, Darryl Lau, Christopher P. Ames Seminars in Spine Surgery.2021; 33(2): 100878. CrossRef
Inclusion of Frailty Improves Predictive Modeling for Postoperative Outcomes in Surgical Management of Primary and Secondary Lumbar Spine Tumors Shane Shahrestani, Joshua Bakhsheshian, Samantha Solaru, Andy Ton, Alexander M. Ballatori, Xiao T. Chen, Rojine Ariani, Patrick Hsieh, Zorica Buser, Jeffrey C. Wang World Neurosurgery.2021; 153: e454. CrossRef
Safety and efficacy of cement augmentation with fenestrated pedicle screws for tumor-related spinal instability Elie Massaad, Myron Rolle, Muhamed Hadzipasic, Ali Kiapour, Ganesh M. Shankar, John H. Shin Neurosurgical Focus.2021; 50(5): E12. CrossRef
Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions Elie Massaad, Natalie Williams, Muhamed Hadzipasic, Shalin S. Patel, Mitchell S. Fourman, Ali Kiapour, Andrew J. Schoenfeld, Ganesh M. Shankar, John H. Shin Neurosurgical Focus.2021; 50(5): E5. CrossRef
Utility of prediction model score: a proposed tool to standardize the performance and generalizability of clinical predictive models based on systematic review Jeff Ehresman, Daniel Lubelski, Zach Pennington, Bethany Hung, A. Karim Ahmed, Tej D. Azad, Kurt Lehner, James Feghali, Zorica Buser, James Harrop, Jefferson Wilson, Shekar Kurpad, Zoher Ghogawala, Daniel M. Sciubba Journal of Neurosurgery: Spine.2021; 34(5): 779. CrossRef
Commentary: Sarcopenia as a Prognostic Factor for 90-Day and Overall Mortality in Patients Undergoing Spine Surgery for Metastatic Tumors: A Multi-Center Retrospective Cohort Study Elie Massaad, John H Shin Neurosurgery.2020; 87(5): E550. CrossRef
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