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A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt

OBJECTIVE: To construct and validate a model to predict responsible nerve roots in lumbar degenerative disease with diagnostic doubt (DD). METHODS: From January 2009-January 2013, 163 patients with DD were assigned to the construction (n = 106) or validation sample (n = 57) according to different ad...

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Autores principales: Li, Xiaochuan, Bai, Xuedong, Wu, Yaohong, Ruan, Dike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792109/
https://www.ncbi.nlm.nih.gov/pubmed/26979618
http://dx.doi.org/10.1186/s12891-016-0973-3
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author Li, Xiaochuan
Bai, Xuedong
Wu, Yaohong
Ruan, Dike
author_facet Li, Xiaochuan
Bai, Xuedong
Wu, Yaohong
Ruan, Dike
author_sort Li, Xiaochuan
collection PubMed
description OBJECTIVE: To construct and validate a model to predict responsible nerve roots in lumbar degenerative disease with diagnostic doubt (DD). METHODS: From January 2009-January 2013, 163 patients with DD were assigned to the construction (n = 106) or validation sample (n = 57) according to different admission times to hospital. Outcome was assessed according to the Japanese Orthopedic Association (JOA) recovery rate as excellent, good, fair, and poor. The first two results were considered as effective clinical outcome (ECO). Baseline patient and clinical characteristics were considered as secondary variables. A multivariate logistic regression model was used to construct a model with the ECO as a dependent variable and other factors as explanatory variables. The odds ratios (ORs) of each risk factor were adjusted and transformed into a scoring system. Area under the curve (AUC) was calculated and validated in both internal and external samples. Moreover, calibration plot and predictive ability of this scoring system were also tested for further validation. RESULTS: Patients with DD with ECOs in both construction and validation models were around 76 % (76.4 and 75.5 % respectively). Risk factors: more preoperative visual analog pain scale (VAS) score (OR = 1.56, p < 0.01), stenosis levels of L4/5 or L5/S1 (OR = 1.44, p = 0.04), stenosis locations with neuroforamen (OR = 1.95, p = 0.01), neurological deficit (OR = 1.62, p = 0.01), and more VAS improvement of selective nerve route block (SNRB) (OR = 3.42, p = 0.02). Validation: the internal area under the curve (AUC) was 0.85, and the external AUC was 0.72, with a good calibration plot of prediction accuracy. Besides, the predictive ability of ECOs was not different from the actual results (p = 0.532). CONCLUSIONS: We have constructed and validated a predictive model for confirming responsible nerve roots in patients with DD. The associated risk factors were preoperative VAS score, stenosis levels of L4/5 or L5/S1, stenosis locations with neuroforamen, neurological deficit, and VAS improvement of SNRB. A tool such as this is beneficial in the preoperative counseling of patients, shared surgical decision making, and ultimately improving safety in spine surgery.
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spelling pubmed-47921092016-03-16 A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt Li, Xiaochuan Bai, Xuedong Wu, Yaohong Ruan, Dike BMC Musculoskelet Disord Technical Advance OBJECTIVE: To construct and validate a model to predict responsible nerve roots in lumbar degenerative disease with diagnostic doubt (DD). METHODS: From January 2009-January 2013, 163 patients with DD were assigned to the construction (n = 106) or validation sample (n = 57) according to different admission times to hospital. Outcome was assessed according to the Japanese Orthopedic Association (JOA) recovery rate as excellent, good, fair, and poor. The first two results were considered as effective clinical outcome (ECO). Baseline patient and clinical characteristics were considered as secondary variables. A multivariate logistic regression model was used to construct a model with the ECO as a dependent variable and other factors as explanatory variables. The odds ratios (ORs) of each risk factor were adjusted and transformed into a scoring system. Area under the curve (AUC) was calculated and validated in both internal and external samples. Moreover, calibration plot and predictive ability of this scoring system were also tested for further validation. RESULTS: Patients with DD with ECOs in both construction and validation models were around 76 % (76.4 and 75.5 % respectively). Risk factors: more preoperative visual analog pain scale (VAS) score (OR = 1.56, p < 0.01), stenosis levels of L4/5 or L5/S1 (OR = 1.44, p = 0.04), stenosis locations with neuroforamen (OR = 1.95, p = 0.01), neurological deficit (OR = 1.62, p = 0.01), and more VAS improvement of selective nerve route block (SNRB) (OR = 3.42, p = 0.02). Validation: the internal area under the curve (AUC) was 0.85, and the external AUC was 0.72, with a good calibration plot of prediction accuracy. Besides, the predictive ability of ECOs was not different from the actual results (p = 0.532). CONCLUSIONS: We have constructed and validated a predictive model for confirming responsible nerve roots in patients with DD. The associated risk factors were preoperative VAS score, stenosis levels of L4/5 or L5/S1, stenosis locations with neuroforamen, neurological deficit, and VAS improvement of SNRB. A tool such as this is beneficial in the preoperative counseling of patients, shared surgical decision making, and ultimately improving safety in spine surgery. BioMed Central 2016-03-15 /pmc/articles/PMC4792109/ /pubmed/26979618 http://dx.doi.org/10.1186/s12891-016-0973-3 Text en © Li et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Li, Xiaochuan
Bai, Xuedong
Wu, Yaohong
Ruan, Dike
A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
title A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
title_full A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
title_fullStr A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
title_full_unstemmed A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
title_short A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
title_sort valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792109/
https://www.ncbi.nlm.nih.gov/pubmed/26979618
http://dx.doi.org/10.1186/s12891-016-0973-3
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