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Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries

BACKGROUND: Injury is a leading cause of the global burden of disease (GBD). Estimates of non-fatal injury burden have been limited by a paucity of empirical outcomes data. This study aimed to (i) establish the 12-month disability associated with each GBD 2010 injury health state, and (ii) compare a...

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Autores principales: Gabbe, Belinda J., Harrison, James E., Lyons, Ronan A., Jolley, Damien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3184172/
https://www.ncbi.nlm.nih.gov/pubmed/21984951
http://dx.doi.org/10.1371/journal.pone.0025862
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author Gabbe, Belinda J.
Harrison, James E.
Lyons, Ronan A.
Jolley, Damien
author_facet Gabbe, Belinda J.
Harrison, James E.
Lyons, Ronan A.
Jolley, Damien
author_sort Gabbe, Belinda J.
collection PubMed
description BACKGROUND: Injury is a leading cause of the global burden of disease (GBD). Estimates of non-fatal injury burden have been limited by a paucity of empirical outcomes data. This study aimed to (i) establish the 12-month disability associated with each GBD 2010 injury health state, and (ii) compare approaches to modelling the impact of multiple injury health states on disability as measured by the Glasgow Outcome Scale – Extended (GOS-E). METHODS: 12-month functional outcomes for 11,337 survivors to hospital discharge were drawn from the Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry. ICD-10 diagnosis codes were mapped to the GBD 2010 injury health states. Cases with a GOS-E score >6 were defined as “recovered.” A split dataset approach was used. Cases were randomly assigned to development or test datasets. Probability of recovery for each health state was calculated using the development dataset. Three logistic regression models were evaluated: a) additive, multivariable; b) “worst injury;” and c) multiplicative. Models were adjusted for age and comorbidity and investigated for discrimination and calibration. FINDINGS: A single injury health state was recorded for 46% of cases (1–16 health states per case). The additive (C-statistic 0.70, 95% CI: 0.69, 0.71) and “worst injury” (C-statistic 0.70; 95% CI: 0.68, 0.71) models demonstrated higher discrimination than the multiplicative (C-statistic 0.68; 95% CI: 0.67, 0.70) model. The additive and “worst injury” models demonstrated acceptable calibration. CONCLUSIONS: The majority of patients survived with persisting disability at 12-months, highlighting the importance of improving estimates of non-fatal injury burden. Additive and “worst” injury models performed similarly. GBD 2010 injury states were moderately predictive of recovery 1-year post-injury. Further evaluation using additional measures of health status and functioning and comparison with the GBD 2010 disability weights will be needed to optimise injury states for future GBD studies.
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spelling pubmed-31841722011-10-07 Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries Gabbe, Belinda J. Harrison, James E. Lyons, Ronan A. Jolley, Damien PLoS One Research Article BACKGROUND: Injury is a leading cause of the global burden of disease (GBD). Estimates of non-fatal injury burden have been limited by a paucity of empirical outcomes data. This study aimed to (i) establish the 12-month disability associated with each GBD 2010 injury health state, and (ii) compare approaches to modelling the impact of multiple injury health states on disability as measured by the Glasgow Outcome Scale – Extended (GOS-E). METHODS: 12-month functional outcomes for 11,337 survivors to hospital discharge were drawn from the Victorian State Trauma Registry and the Victorian Orthopaedic Trauma Outcomes Registry. ICD-10 diagnosis codes were mapped to the GBD 2010 injury health states. Cases with a GOS-E score >6 were defined as “recovered.” A split dataset approach was used. Cases were randomly assigned to development or test datasets. Probability of recovery for each health state was calculated using the development dataset. Three logistic regression models were evaluated: a) additive, multivariable; b) “worst injury;” and c) multiplicative. Models were adjusted for age and comorbidity and investigated for discrimination and calibration. FINDINGS: A single injury health state was recorded for 46% of cases (1–16 health states per case). The additive (C-statistic 0.70, 95% CI: 0.69, 0.71) and “worst injury” (C-statistic 0.70; 95% CI: 0.68, 0.71) models demonstrated higher discrimination than the multiplicative (C-statistic 0.68; 95% CI: 0.67, 0.70) model. The additive and “worst injury” models demonstrated acceptable calibration. CONCLUSIONS: The majority of patients survived with persisting disability at 12-months, highlighting the importance of improving estimates of non-fatal injury burden. Additive and “worst” injury models performed similarly. GBD 2010 injury states were moderately predictive of recovery 1-year post-injury. Further evaluation using additional measures of health status and functioning and comparison with the GBD 2010 disability weights will be needed to optimise injury states for future GBD studies. Public Library of Science 2011-09-30 /pmc/articles/PMC3184172/ /pubmed/21984951 http://dx.doi.org/10.1371/journal.pone.0025862 Text en Gabbe 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gabbe, Belinda J.
Harrison, James E.
Lyons, Ronan A.
Jolley, Damien
Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries
title Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries
title_full Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries
title_fullStr Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries
title_full_unstemmed Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries
title_short Modelling Long Term Disability following Injury: Comparison of Three Approaches for Handling Multiple Injuries
title_sort modelling long term disability following injury: comparison of three approaches for handling multiple injuries
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3184172/
https://www.ncbi.nlm.nih.gov/pubmed/21984951
http://dx.doi.org/10.1371/journal.pone.0025862
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