Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1782213074590105600 |
---|---|
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. |
format | Online Article Text |
id | pubmed-3184172 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT gabbebelindaj modellinglongtermdisabilityfollowinginjurycomparisonofthreeapproachesforhandlingmultipleinjuries AT harrisonjamese modellinglongtermdisabilityfollowinginjurycomparisonofthreeapproachesforhandlingmultipleinjuries AT lyonsronana modellinglongtermdisabilityfollowinginjurycomparisonofthreeapproachesforhandlingmultipleinjuries AT jolleydamien modellinglongtermdisabilityfollowinginjurycomparisonofthreeapproachesforhandlingmultipleinjuries |