Cargando…

AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors

The treatment options for neuropathic pain caused by lumbar disc herniation have been debated controversially in the literature. Whether surgical or conservative therapy makes more sense in individual cases can hardly be answered. We have investigated whether a machine learning-based prediction of o...

Descripción completa

Detalles Bibliográficos
Autores principales: Wirries, André, Geiger, Florian, Hammad, Ahmed, Bäumlein, Martin, Schmeller, Julia Nadine, Blümcke, Ingmar, Jabari, Samir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219728/
https://www.ncbi.nlm.nih.gov/pubmed/35740341
http://dx.doi.org/10.3390/biomedicines10061319
_version_ 1784732188673048576
author Wirries, André
Geiger, Florian
Hammad, Ahmed
Bäumlein, Martin
Schmeller, Julia Nadine
Blümcke, Ingmar
Jabari, Samir
author_facet Wirries, André
Geiger, Florian
Hammad, Ahmed
Bäumlein, Martin
Schmeller, Julia Nadine
Blümcke, Ingmar
Jabari, Samir
author_sort Wirries, André
collection PubMed
description The treatment options for neuropathic pain caused by lumbar disc herniation have been debated controversially in the literature. Whether surgical or conservative therapy makes more sense in individual cases can hardly be answered. We have investigated whether a machine learning-based prediction of outcome, regarding neuropathic pain development, after lumbar disc herniation treatment is possible. The extensive datasets of 123 consecutive patients were used to predict the development of neuropathic pain, measured by a visual analogue scale (VAS) for leg pain and the Oswestry Disability Index (ODI), at 6 weeks, 6 months and 1 year after treatment of lumbar disc herniation in a machine learning approach. Using a decision tree regressor algorithm, a prediction quality within the limits of the minimum clinically important difference for the VAS and ODI value could be achieved. An analysis of the influencing factors of the algorithm reveals the important role of psychological factors as well as body weight and age with pre-existing conditions for an accurate prediction of neuropathic pain. The machine learning algorithm developed here can enable an assessment of the course of treatment after lumbar disc herniation. The early, comparative individual prediction of a therapy outcome is important to avoid unnecessary surgical therapies as well as insufficient conservative therapies and prevent the chronification of neuropathic pain.
format Online
Article
Text
id pubmed-9219728
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-92197282022-06-24 AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors Wirries, André Geiger, Florian Hammad, Ahmed Bäumlein, Martin Schmeller, Julia Nadine Blümcke, Ingmar Jabari, Samir Biomedicines Article The treatment options for neuropathic pain caused by lumbar disc herniation have been debated controversially in the literature. Whether surgical or conservative therapy makes more sense in individual cases can hardly be answered. We have investigated whether a machine learning-based prediction of outcome, regarding neuropathic pain development, after lumbar disc herniation treatment is possible. The extensive datasets of 123 consecutive patients were used to predict the development of neuropathic pain, measured by a visual analogue scale (VAS) for leg pain and the Oswestry Disability Index (ODI), at 6 weeks, 6 months and 1 year after treatment of lumbar disc herniation in a machine learning approach. Using a decision tree regressor algorithm, a prediction quality within the limits of the minimum clinically important difference for the VAS and ODI value could be achieved. An analysis of the influencing factors of the algorithm reveals the important role of psychological factors as well as body weight and age with pre-existing conditions for an accurate prediction of neuropathic pain. The machine learning algorithm developed here can enable an assessment of the course of treatment after lumbar disc herniation. The early, comparative individual prediction of a therapy outcome is important to avoid unnecessary surgical therapies as well as insufficient conservative therapies and prevent the chronification of neuropathic pain. MDPI 2022-06-04 /pmc/articles/PMC9219728/ /pubmed/35740341 http://dx.doi.org/10.3390/biomedicines10061319 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wirries, André
Geiger, Florian
Hammad, Ahmed
Bäumlein, Martin
Schmeller, Julia Nadine
Blümcke, Ingmar
Jabari, Samir
AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors
title AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors
title_full AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors
title_fullStr AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors
title_full_unstemmed AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors
title_short AI Prediction of Neuropathic Pain after Lumbar Disc Herniation—Machine Learning Reveals Influencing Factors
title_sort ai prediction of neuropathic pain after lumbar disc herniation—machine learning reveals influencing factors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219728/
https://www.ncbi.nlm.nih.gov/pubmed/35740341
http://dx.doi.org/10.3390/biomedicines10061319
work_keys_str_mv AT wirriesandre aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors
AT geigerflorian aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors
AT hammadahmed aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors
AT baumleinmartin aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors
AT schmellerjulianadine aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors
AT blumckeingmar aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors
AT jabarisamir aipredictionofneuropathicpainafterlumbardischerniationmachinelearningrevealsinfluencingfactors