How well do principal diagnosis classifications predict disability 12 months postinjury?

Belinda Jane Gabbe, Pamela May Simpson, Ronan Anthony Lyons, Suzanne Polinder, Frederick P Rivara, Shanthi Ameratunga, Sarah Derrett, Juanita A Haagsma, James E Harrison

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Background: The application of disability weights by nature of injury is central to the calculation of disability-adjusted life years (DALYs). Such weights should represent injury diagnosis groups that demonstrate homogeneity in disability outcomes. Existing classifications have not used empirical data in their development to inform groups that are homogeneous for disability outcomes, limiting the capacity to make informed recommendations for best practice in measuring injury burden. Methods: The Validating and Improving injury Burden Estimates (Injury-VIBES) Study includes pooled data from over 30 000 injured participants recruited to six cohort studies. The International Classification of Disease 10th Revision (ICD-10) diagnosis codes were mapped to existing injury burden study groupings and prediction models were developed to measure the capacity of the injury groupings and ICD-10 diagnoses to predict disability outcomes at 12 months. Models were adjusted for age, gender and data source and investigated for discrimination using area under the receiver operating characteristic curve (AUC) and calibration using Hosmer-Lemeshow statistics and calibration curves. Results: Discrimination and calibration of models varied depending on the outcome measured. Models using full four-character ICD-10 diagnosis codes, rather than groupings of codes, demonstrated the highest discrimination ranging from an AUC (95 CI) of 0.627 (0.618 to 0.635) for the pain or discomfort item of the EQ-5D to 0.847 (0.841 to 0.853) for the extended Glasgow Outcome Scale independent living outcome. However, gain over other groupings was marginal. Conclusions: Prediction performance was best for measures of function such as independent living, mobility and self-care. The classifications were poorer predictors of anxiety/depression and pain/discomfort.
Original languageEnglish
Pages (from-to)120 - 126
Number of pages7
JournalInjury Prevention
Volume21
Issue numbere1
DOIs
Publication statusPublished - 2015

Cite this

Gabbe, Belinda Jane ; Simpson, Pamela May ; Lyons, Ronan Anthony ; Polinder, Suzanne ; Rivara, Frederick P ; Ameratunga, Shanthi ; Derrett, Sarah ; Haagsma, Juanita A ; Harrison, James E. / How well do principal diagnosis classifications predict disability 12 months postinjury?. In: Injury Prevention. 2015 ; Vol. 21, No. e1. pp. 120 - 126.
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title = "How well do principal diagnosis classifications predict disability 12 months postinjury?",
abstract = "Background: The application of disability weights by nature of injury is central to the calculation of disability-adjusted life years (DALYs). Such weights should represent injury diagnosis groups that demonstrate homogeneity in disability outcomes. Existing classifications have not used empirical data in their development to inform groups that are homogeneous for disability outcomes, limiting the capacity to make informed recommendations for best practice in measuring injury burden. Methods: The Validating and Improving injury Burden Estimates (Injury-VIBES) Study includes pooled data from over 30 000 injured participants recruited to six cohort studies. The International Classification of Disease 10th Revision (ICD-10) diagnosis codes were mapped to existing injury burden study groupings and prediction models were developed to measure the capacity of the injury groupings and ICD-10 diagnoses to predict disability outcomes at 12 months. Models were adjusted for age, gender and data source and investigated for discrimination using area under the receiver operating characteristic curve (AUC) and calibration using Hosmer-Lemeshow statistics and calibration curves. Results: Discrimination and calibration of models varied depending on the outcome measured. Models using full four-character ICD-10 diagnosis codes, rather than groupings of codes, demonstrated the highest discrimination ranging from an AUC (95 CI) of 0.627 (0.618 to 0.635) for the pain or discomfort item of the EQ-5D to 0.847 (0.841 to 0.853) for the extended Glasgow Outcome Scale independent living outcome. However, gain over other groupings was marginal. Conclusions: Prediction performance was best for measures of function such as independent living, mobility and self-care. The classifications were poorer predictors of anxiety/depression and pain/discomfort.",
author = "Gabbe, {Belinda Jane} and Simpson, {Pamela May} and Lyons, {Ronan Anthony} and Suzanne Polinder and Rivara, {Frederick P} and Shanthi Ameratunga and Sarah Derrett and Haagsma, {Juanita A} and Harrison, {James E}",
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doi = "10.1136/injuryprev-2013-041037",
language = "English",
volume = "21",
pages = "120 -- 126",
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Gabbe, BJ, Simpson, PM, Lyons, RA, Polinder, S, Rivara, FP, Ameratunga, S, Derrett, S, Haagsma, JA & Harrison, JE 2015, 'How well do principal diagnosis classifications predict disability 12 months postinjury?', Injury Prevention, vol. 21, no. e1, pp. 120 - 126. https://doi.org/10.1136/injuryprev-2013-041037

How well do principal diagnosis classifications predict disability 12 months postinjury? / Gabbe, Belinda Jane; Simpson, Pamela May; Lyons, Ronan Anthony; Polinder, Suzanne; Rivara, Frederick P; Ameratunga, Shanthi; Derrett, Sarah; Haagsma, Juanita A; Harrison, James E.

In: Injury Prevention, Vol. 21, No. e1, 2015, p. 120 - 126.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - How well do principal diagnosis classifications predict disability 12 months postinjury?

AU - Gabbe, Belinda Jane

AU - Simpson, Pamela May

AU - Lyons, Ronan Anthony

AU - Polinder, Suzanne

AU - Rivara, Frederick P

AU - Ameratunga, Shanthi

AU - Derrett, Sarah

AU - Haagsma, Juanita A

AU - Harrison, James E

PY - 2015

Y1 - 2015

N2 - Background: The application of disability weights by nature of injury is central to the calculation of disability-adjusted life years (DALYs). Such weights should represent injury diagnosis groups that demonstrate homogeneity in disability outcomes. Existing classifications have not used empirical data in their development to inform groups that are homogeneous for disability outcomes, limiting the capacity to make informed recommendations for best practice in measuring injury burden. Methods: The Validating and Improving injury Burden Estimates (Injury-VIBES) Study includes pooled data from over 30 000 injured participants recruited to six cohort studies. The International Classification of Disease 10th Revision (ICD-10) diagnosis codes were mapped to existing injury burden study groupings and prediction models were developed to measure the capacity of the injury groupings and ICD-10 diagnoses to predict disability outcomes at 12 months. Models were adjusted for age, gender and data source and investigated for discrimination using area under the receiver operating characteristic curve (AUC) and calibration using Hosmer-Lemeshow statistics and calibration curves. Results: Discrimination and calibration of models varied depending on the outcome measured. Models using full four-character ICD-10 diagnosis codes, rather than groupings of codes, demonstrated the highest discrimination ranging from an AUC (95 CI) of 0.627 (0.618 to 0.635) for the pain or discomfort item of the EQ-5D to 0.847 (0.841 to 0.853) for the extended Glasgow Outcome Scale independent living outcome. However, gain over other groupings was marginal. Conclusions: Prediction performance was best for measures of function such as independent living, mobility and self-care. The classifications were poorer predictors of anxiety/depression and pain/discomfort.

AB - Background: The application of disability weights by nature of injury is central to the calculation of disability-adjusted life years (DALYs). Such weights should represent injury diagnosis groups that demonstrate homogeneity in disability outcomes. Existing classifications have not used empirical data in their development to inform groups that are homogeneous for disability outcomes, limiting the capacity to make informed recommendations for best practice in measuring injury burden. Methods: The Validating and Improving injury Burden Estimates (Injury-VIBES) Study includes pooled data from over 30 000 injured participants recruited to six cohort studies. The International Classification of Disease 10th Revision (ICD-10) diagnosis codes were mapped to existing injury burden study groupings and prediction models were developed to measure the capacity of the injury groupings and ICD-10 diagnoses to predict disability outcomes at 12 months. Models were adjusted for age, gender and data source and investigated for discrimination using area under the receiver operating characteristic curve (AUC) and calibration using Hosmer-Lemeshow statistics and calibration curves. Results: Discrimination and calibration of models varied depending on the outcome measured. Models using full four-character ICD-10 diagnosis codes, rather than groupings of codes, demonstrated the highest discrimination ranging from an AUC (95 CI) of 0.627 (0.618 to 0.635) for the pain or discomfort item of the EQ-5D to 0.847 (0.841 to 0.853) for the extended Glasgow Outcome Scale independent living outcome. However, gain over other groupings was marginal. Conclusions: Prediction performance was best for measures of function such as independent living, mobility and self-care. The classifications were poorer predictors of anxiety/depression and pain/discomfort.

UR - http://injuryprevention.bmj.com/content/21/e1/e120.full.pdf

U2 - 10.1136/injuryprev-2013-041037

DO - 10.1136/injuryprev-2013-041037

M3 - Article

VL - 21

SP - 120

EP - 126

JO - Injury Prevention

JF - Injury Prevention

SN - 1353-8047

IS - e1

ER -