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Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After 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 lu...

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Autores principales: André, Arthur, Peyrou, Bruno, Carpentier, Alexandre, Vignaux, Jean-Jacques
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9344503/
https://www.ncbi.nlm.nih.gov/pubmed/33207969
http://dx.doi.org/10.1177/2192568220969373
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author André, Arthur
Peyrou, Bruno
Carpentier, Alexandre
Vignaux, Jean-Jacques
author_facet André, Arthur
Peyrou, Bruno
Carpentier, Alexandre
Vignaux, Jean-Jacques
author_sort André, Arthur
collection PubMed
description 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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spelling pubmed-93445032022-08-03 Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery André, Arthur Peyrou, Bruno Carpentier, Alexandre Vignaux, Jean-Jacques Global Spine J Original Articles 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. SAGE Publications 2020-11-19 2022-06 /pmc/articles/PMC9344503/ /pubmed/33207969 http://dx.doi.org/10.1177/2192568220969373 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Articles
André, Arthur
Peyrou, Bruno
Carpentier, Alexandre
Vignaux, Jean-Jacques
Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
title Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
title_full Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
title_fullStr Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
title_full_unstemmed Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
title_short Feasibility and Assessment of a Machine Learning-Based Predictive Model of Outcome After Lumbar Decompression Surgery
title_sort feasibility and assessment of a machine learning-based predictive model of outcome after lumbar decompression surgery
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9344503/
https://www.ncbi.nlm.nih.gov/pubmed/33207969
http://dx.doi.org/10.1177/2192568220969373
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