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Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study

BACKGROUND: Spinal manipulative therapy (SMT) is among the nonpharmacologic interventions that has been recommended in clinical guidelines for patients with low back pain, however, some patients appear to benefit substantially more from SMT than others. Several investigations have examined potential...

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Autores principales: Hadizadeh, Maliheh, Kawchuk, Gregory Neil, Prasad, Narasimha, Fritz, Julie M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685475/
https://www.ncbi.nlm.nih.gov/pubmed/33232379
http://dx.doi.org/10.1371/journal.pone.0242831
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author Hadizadeh, Maliheh
Kawchuk, Gregory Neil
Prasad, Narasimha
Fritz, Julie M.
author_facet Hadizadeh, Maliheh
Kawchuk, Gregory Neil
Prasad, Narasimha
Fritz, Julie M.
author_sort Hadizadeh, Maliheh
collection PubMed
description BACKGROUND: Spinal manipulative therapy (SMT) is among the nonpharmacologic interventions that has been recommended in clinical guidelines for patients with low back pain, however, some patients appear to benefit substantially more from SMT than others. Several investigations have examined potential factors to modify patients’ responses prior to SMT application. The objective of this study was to determine if the baseline prediction of SMT responders can be improved through the use of a restricted, non-pragmatic methodology, established variables of responder status, and newly developed physical measures observed to change with SMT. MATERIALS AND METHODS: We conducted a secondary analysis of a prior study that provided two applications of standardized SMT over a period of 1 week. After initial exploratory analysis, principal component analysis and optimal scaling analysis were used to reduce multicollinearity among predictors. A multiple logistic regression model was built using a forward Wald procedure to explore those baseline variables that could predict response status at 1-week reassessment. RESULTS: Two hundred and thirty-eight participants completed the 1-week reassessment (age 40.0± 11.8 years; 59.7% female). Response to treatment was predicted by a model containing the following 8 variables: height, gender, neck or upper back pain, pain frequency in the past 6 months, the STarT Back Tool, patients’ expectations about medication and strengthening exercises, and extension status. Our model had a sensitivity of 72.2% (95% CI, 58.1–83.1), specificity of 84.2% (95% CI, 78.0–89.0), a positive likelihood ratio of 4.6 (CI, 3.2–6.7), a negative likelihood ratio of 0.3 (CI, 0.2–0.5), and area under ROC curve, 0.79. CONCLUSION: It is possible to predict response to treatment before application of SMT in low back pain patients. Our model may benefit both patients and clinicians by reducing the time needed to re-evaluate an initial trial of care.
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spelling pubmed-76854752020-12-02 Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study Hadizadeh, Maliheh Kawchuk, Gregory Neil Prasad, Narasimha Fritz, Julie M. PLoS One Research Article BACKGROUND: Spinal manipulative therapy (SMT) is among the nonpharmacologic interventions that has been recommended in clinical guidelines for patients with low back pain, however, some patients appear to benefit substantially more from SMT than others. Several investigations have examined potential factors to modify patients’ responses prior to SMT application. The objective of this study was to determine if the baseline prediction of SMT responders can be improved through the use of a restricted, non-pragmatic methodology, established variables of responder status, and newly developed physical measures observed to change with SMT. MATERIALS AND METHODS: We conducted a secondary analysis of a prior study that provided two applications of standardized SMT over a period of 1 week. After initial exploratory analysis, principal component analysis and optimal scaling analysis were used to reduce multicollinearity among predictors. A multiple logistic regression model was built using a forward Wald procedure to explore those baseline variables that could predict response status at 1-week reassessment. RESULTS: Two hundred and thirty-eight participants completed the 1-week reassessment (age 40.0± 11.8 years; 59.7% female). Response to treatment was predicted by a model containing the following 8 variables: height, gender, neck or upper back pain, pain frequency in the past 6 months, the STarT Back Tool, patients’ expectations about medication and strengthening exercises, and extension status. Our model had a sensitivity of 72.2% (95% CI, 58.1–83.1), specificity of 84.2% (95% CI, 78.0–89.0), a positive likelihood ratio of 4.6 (CI, 3.2–6.7), a negative likelihood ratio of 0.3 (CI, 0.2–0.5), and area under ROC curve, 0.79. CONCLUSION: It is possible to predict response to treatment before application of SMT in low back pain patients. Our model may benefit both patients and clinicians by reducing the time needed to re-evaluate an initial trial of care. Public Library of Science 2020-11-24 /pmc/articles/PMC7685475/ /pubmed/33232379 http://dx.doi.org/10.1371/journal.pone.0242831 Text en © 2020 Hadizadeh et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hadizadeh, Maliheh
Kawchuk, Gregory Neil
Prasad, Narasimha
Fritz, Julie M.
Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
title Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
title_full Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
title_fullStr Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
title_full_unstemmed Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
title_short Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study
title_sort predicting who responds to spinal manipulative therapy using a short-time frame methodology: results from a 238-participant study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7685475/
https://www.ncbi.nlm.nih.gov/pubmed/33232379
http://dx.doi.org/10.1371/journal.pone.0242831
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