Sciatic Neuritis After Lumbar Decompression Surgery

Spine ◽  
2013 ◽  
Vol 38 (11) ◽  
pp. E687-E689 ◽  
Author(s):  
Marios G. Lykissas ◽  
Alexander Aichmair ◽  
James Farmer
Keyword(s):  
Decompression Surgery ◽  
Lumbar Decompression ◽  
Lumbar Decompression Surgery ◽  
Sciatic Neuritis

Routine Postoperative Imaging Early After Lumbar Decompression Surgery

Spine ◽  
2013 ◽  
Vol 38 (20) ◽  
pp. E1263-E1268 ◽  
Author(s):  
Gerrit Alexander Schubert ◽  
Michaels Diepers ◽  
Aldemar Andres Hegewald ◽  
Marcel Seiz ◽  
Claudius Thomé
Keyword(s):  
Decompression Surgery ◽  
Postoperative Imaging ◽  
Lumbar Decompression ◽  
Lumbar Decompression Surgery

Outcomes of lumbar decompression surgery in patients with diffuse idiopathic skeletal hyperostosis (DISH)

2019 ◽  
Vol 24 (6) ◽  
pp. 957-962 ◽  
Author(s):  
Bungo Otsuki ◽  
Shunsuke Fujibayashi ◽  
Shimei Tanida ◽  
Takayoshi Shimizu ◽  
Stephen Lyman ◽  
...  
Keyword(s):  
Diffuse Idiopathic Skeletal Hyperostosis ◽  
Decompression Surgery ◽  
Lumbar Decompression ◽  
Lumbar Decompression Surgery

scholarly journals Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery

2020 ◽  
pp. 219256822096937
Author(s):  
Arthur André ◽  
Bruno Peyrou ◽  
Alexandre Carpentier ◽  
Jean-Jacques Vignaux
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Retrospective Study ◽  
Decompression Surgery ◽  
Virtual Patients ◽  
Ann Model ◽  
Lumbar Decompression ◽  
Green Zone ◽  
Artificial Neural ◽  
Lumbar Decompression Surgery

Study design: Retrospective study at a unique center. Objective: The aim of this study is twofold, to develop a virtual patients model for lumbar decompression surgery and to evaluate the precision of an artificial neural network (ANN) model designed to accurately predict the clinical outcomes of lumbar decompression surgery. Methods: We performed a retrospective study of complete Electronic Health Records (EHR) to identify potential unfavorable criteria for spine surgery (predictors). A cohort of synthetics EHR was created to classify patients by surgical success (green zone) or partial failure (orange zone) using an Artificial Neural Network which screens all the available predictors. Results: In the actual cohort, we included 60 patients, with complete EHR allowing efficient analysis, 26 patients were in the orange zone (43.4%) and 34 were in the green zone (56.6%). The average positive criteria amount for actual patients was 8.62 for the green zone (SD+/- 3.09) and 10.92 for the orange zone (SD 3.38). The classifier (a neural network) was trained using 10,000 virtual patients and 2000 virtual patients were used for test purposes. The 12,000 virtual patients were generated from the 60 EHR, of which half were in the green zone and half in the orange zone. The model showed an accuracy of 72% and a ROC score of 0.78. The sensitivity was 0.885 and the specificity 0.59. Conclusion: Our method can be used to predict a favorable patient to have lumbar decompression surgery. However, there is still a need to further develop its ability to analyze patients in the “failure of treatment” zone to offer precise management of patient health before spinal surgery.

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