Modeling injury outcomes using time-to-event methods

Clark, DE; Ryan, LM

Modeling injury outcomes using time-to-event methods

Clark, DE Ryan, LM

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Publication Type:: Journal Article
Citation:: Journal of Trauma - Injury, Infection and Critical Care, 1997, 42 (6), pp. 1129 - 1134
Issue Date:: 1997-06-01

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Field	Value	Language
dc.contributor.author	Clark, DE	en_US
dc.contributor.author	Ryan, LM https://orcid.org/0000-0001-5957-2490	en_US
dc.date.issued	1997-06-01	en_US
dc.identifier.citation	Journal of Trauma - Injury, Infection and Critical Care, 1997, 42 (6), pp. 1129 - 1134	en_US
dc.identifier.issn	1079-6061	en_US
dc.identifier.uri	http://hdl.handle.net/10453/26232
dc.description.abstract	Background: Mortality is an important measurement of injury outcomes, but measurements reflecting disability or cost are also important. Hospital length of stay (LOS) has been used as an outcome variable, but reduced LOS could be achieved either by improved care or by increased mortality. A solution to this statistical problem of 'competing risks' would enable injury outcomes based on LOS to be modeled using time-to-event methods. Methods: Time-to-event methodology was applied to 2,106 cases with complete data from the 1991-1994 registry of a regional trauma center. LOS was used as the outcome variable, modified by assigning an arbitrarily long LOS to any fatal case. A combination of proportional hazards and logistic regression models was used to explore the effects of potential predictive variables, including Trauma Score (TS), Injury Severity Score (ISS), components of TS or ISS, age, sex, alcohol use, and whether a patient was transferred. Results: The 'TRISS' combination of TS, ISS, and age previously shown to predict mortality also predicted 'modified LOS' (Wald p value less than 0.001 for each variable). Models using only age and certain components of ISS or TS fit our data even better, with fewer parameters. Other variables were not predictive. Modified Kaplan-Meier plots provided easily interpreted graphical results, combining both mortality and LOS information. Conclusions: With a simple modification to allow for competing risks, time-to-event methods enable more informative modeling of injury outcomes than binary (lived/died) methods alone. Such models may be useful for describing and comparing groups of hospitalized trauma patients.	en_US
dc.relation.ispartof	Journal of Trauma - Injury, Infection and Critical Care	en_US
dc.relation.isbasedon	10.1097/00005373-199706000-00025	en_US
dc.subject.classification	Emergency & Critical Care Medicine	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Wounds and Injuries	en_US
dc.subject.mesh	Length of Stay	en_US
dc.subject.mesh	Trauma Severity Indices	en_US
dc.subject.mesh	Logistic Models	en_US
dc.subject.mesh	Survival Analysis	en_US
dc.subject.mesh	Outcome Assessment (Health Care)	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Outcome Assessment, Health Care	en_US
dc.title	Modeling injury outcomes using time-to-event methods	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	42	en_US
utslib.for	010402 Biostatistics	en_US
utslib.for	1102 Cardiorespiratory Medicine and Haematology	en_US
utslib.for	1103 Clinical Sciences	en_US
utslib.for	1110 Nursing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	42	en_US

Abstract:

Background: Mortality is an important measurement of injury outcomes, but measurements reflecting disability or cost are also important. Hospital length of stay (LOS) has been used as an outcome variable, but reduced LOS could be achieved either by improved care or by increased mortality. A solution to this statistical problem of 'competing risks' would enable injury outcomes based on LOS to be modeled using time-to-event methods. Methods: Time-to-event methodology was applied to 2,106 cases with complete data from the 1991-1994 registry of a regional trauma center. LOS was used as the outcome variable, modified by assigning an arbitrarily long LOS to any fatal case. A combination of proportional hazards and logistic regression models was used to explore the effects of potential predictive variables, including Trauma Score (TS), Injury Severity Score (ISS), components of TS or ISS, age, sex, alcohol use, and whether a patient was transferred. Results: The 'TRISS' combination of TS, ISS, and age previously shown to predict mortality also predicted 'modified LOS' (Wald p value less than 0.001 for each variable). Models using only age and certain components of ISS or TS fit our data even better, with fewer parameters. Other variables were not predictive. Modified Kaplan-Meier plots provided easily interpreted graphical results, combining both mortality and LOS information. Conclusions: With a simple modification to allow for competing risks, time-to-event methods enable more informative modeling of injury outcomes than binary (lived/died) methods alone. Such models may be useful for describing and comparing groups of hospitalized trauma patients.

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http://hdl.handle.net/10453/26232