WeChat Mini Program
Old Version Features

Analysis of Risk Factors for Adjacent Segment Degeneration after Minimally Invasive Transforaminal Interbody Fusion at Lumbosacral Spine.

Computational intelligence and neuroscience(2022)

Dalian Med Univ

Cited 2|Views8
Abstract
Background:Adjacent segment degeneration (ASD) has been considered as a serious complication from changes in the biological stress pattern after spinal fusion. The sagittal balance significantly associated with lumbar loading is largely dependent on L5-S1 segment. However, the evidence indicating risk factors for radiological and symptomatic ASD after minimally invasive transforaminal interbody fusion (MIS-TLIF) remains insufficient.Methods:This single-central retrospective study recruited patients with lumbosacral degeneration receiving MIS-TLIF at the L5-S1 level from January 2015 to December 2018. The targeted variables included demographic information, radiological indicators, surgery-related parameters, and patient-reported outcomes (PROs) extracted from the electronic medical system by natural language processing. In these patients, a minimum of 3-year follow-up was done. After reviewing the preoperative and postoperative follow-up digital radiographs, patients were assigned to radiological ASD group (disc height narrowing ≥3 mm, progressive slipping ≥3 mm, angular motion >10°, and osteophyte formation >3 mm), symptomatic ASD group, and control group. We identified potential predictors for radiological and symptomatic ASD with the service of stepwise logistic regression analysis.Results:Among the 157 consecutive patients treated with MIS-TLIF in our department, 16 cases (10.2%) were diagnosed with radiological ASD at 3-year follow-up. The clinical evaluation did not reveal suspicious risk factors, but several significant differences were confirmed in radiological indicators. Multivariate logistic regression analysis showed postoperative PI, postoperative DA, and ∆PI-LL in radiological ASD group were significantly different from those in control group. Nevertheless, for patients diagnosed with simultaneously radiological and symptomatic ASD, postoperative DA and postoperative PT as risk factors significantly affected the clinical outcome following MIS-TLIF.Conclusion:In this study, while approximately 10% of lumbosacral degenerations develop radiographic ASD, prognosis-related symptomatic ASD was shown not to be a frequent postoperative complication. Postoperative PI, postoperative DA, and mismatched PI-LL are risk factors for radiological ASD, and postoperative DA and postoperative PT are responsible for the occurrence of symptomatic ASD following MIS-TLIF. These radiological risk factors demonstrate that restoration of normal sagittal balance is an effective measure to optimize treatment strategies for secondary ASD prevention.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined