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Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information

BACKGROUND: In cases of neck and shoulder pain, the responsibility for assessing work prognosis is held by clinicians with access to different domains of information. One of these domains is magnetic resonance imaging (MRI), and although MRI is increasingly used, it is unknown which domains of infor...

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Autores principales: Moll, Line Thorndal, Schmidt, Anne Mette, Stapelfeldt, Christina Malmose, Labriola, Merete, Jensen, Ole Kudsk, Kindt, Morten Wasmod, Jensen, Tue Secher, Schiøttz-Christensen, Berit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842554/
https://www.ncbi.nlm.nih.gov/pubmed/31706350
http://dx.doi.org/10.1186/s12891-019-2906-4
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author Moll, Line Thorndal
Schmidt, Anne Mette
Stapelfeldt, Christina Malmose
Labriola, Merete
Jensen, Ole Kudsk
Kindt, Morten Wasmod
Jensen, Tue Secher
Schiøttz-Christensen, Berit
author_facet Moll, Line Thorndal
Schmidt, Anne Mette
Stapelfeldt, Christina Malmose
Labriola, Merete
Jensen, Ole Kudsk
Kindt, Morten Wasmod
Jensen, Tue Secher
Schiøttz-Christensen, Berit
author_sort Moll, Line Thorndal
collection PubMed
description BACKGROUND: In cases of neck and shoulder pain, the responsibility for assessing work prognosis is held by clinicians with access to different domains of information. One of these domains is magnetic resonance imaging (MRI), and although MRI is increasingly used, it is unknown which domains of information contribute the most to the prediction of work prognosis. This retrospective cohort study explored the contribution of demographic, patient-reported, clinical, and MRI information to the prediction of work participation in sickness absentees with neck or shoulder pain. METHODS: From a secondary care setting, 168 sickness absentees with neck or shoulder pain were included. Based on registry data, a successful work outcome was defined as ≥50% work participation score (WPS) from Weeks 1 to 104 after enrolment. Prognostic variables were categorized into four domains (demographic, patient-reported, clinical, and MRI) resembling the order of information obtained in a clinical setting. Crude logistic regression analyses were used to identify prognostic variables for each domain (p < 0.2). This was followed by multivariable analyses including the identified variables in a domain-wise order. For each added domain, the probability of successful WPS was dichotomized leaving two possible classifications: ≥ 50% chance of successful WPS or not. In cross-tabulations of chance and the actual WPS outcome, positive and negative predictive values (PPV and NPV), sensitivity, specificity and area under the curve (AUC) were calculated. RESULTS: The combination of demographic and patient-reported variables yielded an NPV of 0.72 and a PPV of 0.67, while specificity was 0.82, sensitivity 0.54 and AUC 0.77. None of these values improved notably by adding clinical and MRI variables as predictors of successful WPS. CONCLUSIONS: These results suggest that - among sickness absentees with neck or shoulder pain – clinical and MRI variables provide no additional information for the prediction of work participation compared with only demographic and patient-reported information.
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spelling pubmed-68425542019-11-14 Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information Moll, Line Thorndal Schmidt, Anne Mette Stapelfeldt, Christina Malmose Labriola, Merete Jensen, Ole Kudsk Kindt, Morten Wasmod Jensen, Tue Secher Schiøttz-Christensen, Berit BMC Musculoskelet Disord Research Article BACKGROUND: In cases of neck and shoulder pain, the responsibility for assessing work prognosis is held by clinicians with access to different domains of information. One of these domains is magnetic resonance imaging (MRI), and although MRI is increasingly used, it is unknown which domains of information contribute the most to the prediction of work prognosis. This retrospective cohort study explored the contribution of demographic, patient-reported, clinical, and MRI information to the prediction of work participation in sickness absentees with neck or shoulder pain. METHODS: From a secondary care setting, 168 sickness absentees with neck or shoulder pain were included. Based on registry data, a successful work outcome was defined as ≥50% work participation score (WPS) from Weeks 1 to 104 after enrolment. Prognostic variables were categorized into four domains (demographic, patient-reported, clinical, and MRI) resembling the order of information obtained in a clinical setting. Crude logistic regression analyses were used to identify prognostic variables for each domain (p < 0.2). This was followed by multivariable analyses including the identified variables in a domain-wise order. For each added domain, the probability of successful WPS was dichotomized leaving two possible classifications: ≥ 50% chance of successful WPS or not. In cross-tabulations of chance and the actual WPS outcome, positive and negative predictive values (PPV and NPV), sensitivity, specificity and area under the curve (AUC) were calculated. RESULTS: The combination of demographic and patient-reported variables yielded an NPV of 0.72 and a PPV of 0.67, while specificity was 0.82, sensitivity 0.54 and AUC 0.77. None of these values improved notably by adding clinical and MRI variables as predictors of successful WPS. CONCLUSIONS: These results suggest that - among sickness absentees with neck or shoulder pain – clinical and MRI variables provide no additional information for the prediction of work participation compared with only demographic and patient-reported information. BioMed Central 2019-11-09 /pmc/articles/PMC6842554/ /pubmed/31706350 http://dx.doi.org/10.1186/s12891-019-2906-4 Text en © The Author(s). 2019 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 Research Article
Moll, Line Thorndal
Schmidt, Anne Mette
Stapelfeldt, Christina Malmose
Labriola, Merete
Jensen, Ole Kudsk
Kindt, Morten Wasmod
Jensen, Tue Secher
Schiøttz-Christensen, Berit
Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
title Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
title_full Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
title_fullStr Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
title_full_unstemmed Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
title_short Prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
title_sort prediction of 2-year work participation in sickness absentees with neck or shoulder pain: the contribution of demographic, patient-reported, clinical and imaging information
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842554/
https://www.ncbi.nlm.nih.gov/pubmed/31706350
http://dx.doi.org/10.1186/s12891-019-2906-4
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