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A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale

Treatment of chronic lower back pain (CLBP) should be stratified for best medical and economic outcome. To improve the targeting of potential participants for exclusive therapy offers from payers, Freytag et al. developed a tool to classify back pain chronicity classes (CC) based on claim data. The...

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Autores principales: Hochheim, M., Ramm, P., Wunderlich, M., Amelung, V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547910/
https://www.ncbi.nlm.nih.gov/pubmed/36209228
http://dx.doi.org/10.1038/s41598-022-21422-x
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author Hochheim, M.
Ramm, P.
Wunderlich, M.
Amelung, V.
author_facet Hochheim, M.
Ramm, P.
Wunderlich, M.
Amelung, V.
author_sort Hochheim, M.
collection PubMed
description Treatment of chronic lower back pain (CLBP) should be stratified for best medical and economic outcome. To improve the targeting of potential participants for exclusive therapy offers from payers, Freytag et al. developed a tool to classify back pain chronicity classes (CC) based on claim data. The aim of this study was to evaluate the criterion validity of the model. Administrative claim data and self-reported patient information from 3,506 participants (2014–2021) in a private health insurance health management programme in Germany were used to validate the tool. Sensitivity, specificity, and Matthews’ correlation coefficient (MCC) were calculated comparing the prediction with actual grades based on von Korff’s graded chronic pain scale (GCPS). The secondary outcome was an updated view on direct health care costs (€) of patients with back pain (BP) grouped by GCPS. Results showed a fair correlation between predicted CC and actual GCPS grades. A total of 69.7% of all cases were correctly classified. Sensitivity and specificity rates of 54.6 and 76.4% underlined precision. Correlation between CC and GCPS with an MCC of 0.304 also indicated a fair relationship between prediction and observation. Cost data could be clearly grouped by GCPS: the higher the grade, the higher the costs and the use of health care. This was the first study to compare the predicted severity of BP using claim data with the actual severity of BP by GCPS. Based on the results, the usage of CC as a single tool to determine who receives CLBP treatment cannot be recommended. CC is a good tool to segment candidates for specific types of intervention in BP. However, it cannot replace a medical screening at the beginning of an intervention, as the rate of false negatives is too high. Trial registration The study was conducted using routinely collected data from an intervention, which was previously evaluated and registered retrospectively in the German Registry of Clinical Trials under DRKS00015463 (04/09/2018). Informed consent and the self-reported questionnaire have remained unchanged since the study and, therefore, are still valid according to the ethics proposal.
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spelling pubmed-95479102022-10-10 A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale Hochheim, M. Ramm, P. Wunderlich, M. Amelung, V. Sci Rep Article Treatment of chronic lower back pain (CLBP) should be stratified for best medical and economic outcome. To improve the targeting of potential participants for exclusive therapy offers from payers, Freytag et al. developed a tool to classify back pain chronicity classes (CC) based on claim data. The aim of this study was to evaluate the criterion validity of the model. Administrative claim data and self-reported patient information from 3,506 participants (2014–2021) in a private health insurance health management programme in Germany were used to validate the tool. Sensitivity, specificity, and Matthews’ correlation coefficient (MCC) were calculated comparing the prediction with actual grades based on von Korff’s graded chronic pain scale (GCPS). The secondary outcome was an updated view on direct health care costs (€) of patients with back pain (BP) grouped by GCPS. Results showed a fair correlation between predicted CC and actual GCPS grades. A total of 69.7% of all cases were correctly classified. Sensitivity and specificity rates of 54.6 and 76.4% underlined precision. Correlation between CC and GCPS with an MCC of 0.304 also indicated a fair relationship between prediction and observation. Cost data could be clearly grouped by GCPS: the higher the grade, the higher the costs and the use of health care. This was the first study to compare the predicted severity of BP using claim data with the actual severity of BP by GCPS. Based on the results, the usage of CC as a single tool to determine who receives CLBP treatment cannot be recommended. CC is a good tool to segment candidates for specific types of intervention in BP. However, it cannot replace a medical screening at the beginning of an intervention, as the rate of false negatives is too high. Trial registration The study was conducted using routinely collected data from an intervention, which was previously evaluated and registered retrospectively in the German Registry of Clinical Trials under DRKS00015463 (04/09/2018). Informed consent and the self-reported questionnaire have remained unchanged since the study and, therefore, are still valid according to the ethics proposal. Nature Publishing Group UK 2022-10-08 /pmc/articles/PMC9547910/ /pubmed/36209228 http://dx.doi.org/10.1038/s41598-022-21422-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hochheim, M.
Ramm, P.
Wunderlich, M.
Amelung, V.
A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
title A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
title_full A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
title_fullStr A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
title_full_unstemmed A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
title_short A cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
title_sort cross-sectional study to validate an administrative back pain severity classification tool based on the graded chronic pain scale
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547910/
https://www.ncbi.nlm.nih.gov/pubmed/36209228
http://dx.doi.org/10.1038/s41598-022-21422-x
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