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Predicting prolonged sick leave among trauma survivors
Many survivors after trauma suffer from long-term morbidity. The aim of this observational cohort study was to develop a prognostic prediction tool for early assessment of full-time sick leave one year after trauma. Potential predictors were assessed combining individuals from a trauma register with...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329751/ https://www.ncbi.nlm.nih.gov/pubmed/30635611 http://dx.doi.org/10.1038/s41598-018-37289-w |
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author | von Oelreich, Erik Eriksson, Mikael Brattström, Olof Discacciati, Andrea Strömmer, Lovisa Oldner, Anders Larsson, Emma |
author_facet | von Oelreich, Erik Eriksson, Mikael Brattström, Olof Discacciati, Andrea Strömmer, Lovisa Oldner, Anders Larsson, Emma |
author_sort | von Oelreich, Erik |
collection | PubMed |
description | Many survivors after trauma suffer from long-term morbidity. The aim of this observational cohort study was to develop a prognostic prediction tool for early assessment of full-time sick leave one year after trauma. Potential predictors were assessed combining individuals from a trauma register with national health registers. Two models were developed using logistic regression and stepwise backward elimination. 4458 individuals were included out of which 488 were on sick leave full-time 12 months after the trauma. One comprehensive and one simplified model were developed including nine and seven predictors respectively. Both models showed excellent discrimination (AUC 0.81). The comprehensive model had very good calibration, and the simplified model good calibration. Prediction models can be used to assess post-trauma sick leave using injury-related variables as well as factors not related to the trauma per se. Among included variables, pre-injury sick leave was the single most important predictor for full-time sick leave one year after trauma. These models could facilitate a more efficient use of resources, targeting groups for follow-up interventions to improve outcome. External validation is necessary in order to evaluate generalizability. |
format | Online Article Text |
id | pubmed-6329751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63297512019-01-14 Predicting prolonged sick leave among trauma survivors von Oelreich, Erik Eriksson, Mikael Brattström, Olof Discacciati, Andrea Strömmer, Lovisa Oldner, Anders Larsson, Emma Sci Rep Article Many survivors after trauma suffer from long-term morbidity. The aim of this observational cohort study was to develop a prognostic prediction tool for early assessment of full-time sick leave one year after trauma. Potential predictors were assessed combining individuals from a trauma register with national health registers. Two models were developed using logistic regression and stepwise backward elimination. 4458 individuals were included out of which 488 were on sick leave full-time 12 months after the trauma. One comprehensive and one simplified model were developed including nine and seven predictors respectively. Both models showed excellent discrimination (AUC 0.81). The comprehensive model had very good calibration, and the simplified model good calibration. Prediction models can be used to assess post-trauma sick leave using injury-related variables as well as factors not related to the trauma per se. Among included variables, pre-injury sick leave was the single most important predictor for full-time sick leave one year after trauma. These models could facilitate a more efficient use of resources, targeting groups for follow-up interventions to improve outcome. External validation is necessary in order to evaluate generalizability. Nature Publishing Group UK 2019-01-11 /pmc/articles/PMC6329751/ /pubmed/30635611 http://dx.doi.org/10.1038/s41598-018-37289-w Text en © The Author(s) 2019 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article von Oelreich, Erik Eriksson, Mikael Brattström, Olof Discacciati, Andrea Strömmer, Lovisa Oldner, Anders Larsson, Emma Predicting prolonged sick leave among trauma survivors |
title | Predicting prolonged sick leave among trauma survivors |
title_full | Predicting prolonged sick leave among trauma survivors |
title_fullStr | Predicting prolonged sick leave among trauma survivors |
title_full_unstemmed | Predicting prolonged sick leave among trauma survivors |
title_short | Predicting prolonged sick leave among trauma survivors |
title_sort | predicting prolonged sick leave among trauma survivors |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6329751/ https://www.ncbi.nlm.nih.gov/pubmed/30635611 http://dx.doi.org/10.1038/s41598-018-37289-w |
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