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Deriving a prediction rule for short stay admission in trauma patients admitted at a major trauma centre in Australia

Emergency medicine journal : EMJ, 2014-04, Vol.31 (4), p.263-267 [Peer Reviewed Journal]

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions ;Copyright: 2014 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions ;ISSN: 1472-0205 ;EISSN: 1472-0213 ;DOI: 10.1136/emermed-2012-202222 ;PMID: 23407379

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Title:
Deriving a prediction rule for short stay admission in trauma patients admitted at a major trauma centre in Australia
Author: Dinh, Michael M ; Bein, Kendall J ; Byrne, Chris M ; Gabbe, Belinda ; Ivers, Rebecca
Subjects: Abdomen ; Adolescent ; Adult ; Age Factors ; Aged ; Area Under Curve ; Central business districts ; Comorbidity ; Datasets ; Disease ; Emergency communications systems ; Emergency services ; Female ; Heart attacks ; Hospitals ; Humans ; Injuries ; Length of Stay - statistics & numerical data ; Logistic Models ; Male ; Medical records ; Mental disorders ; Mental health ; Middle Aged ; Mortality ; New South Wales ; Patients ; Population ; Predictive Value of Tests ; Retrospective Studies ; Schizophrenia ; Sensitivity and Specificity ; Trauma Centers - statistics & numerical data ; Trauma Severity Indices ; Young Adult
Is Part Of: Emergency medicine journal : EMJ, 2014-04, Vol.31 (4), p.263-267
Description: Introduction The aim of this study was to derive and internally validate a prediction rule for short stay admissions (SSAs) in trauma patients admitted to a major trauma centre. Methods A retrospective study of all trauma activation patients requiring inpatient admission at a single inner city major trauma centre in Australia between 2007 and 2011 was conducted. Logistic regression was used to derive a multivariable model for the outcome of SSA (length of stay ≤2 days excluding deaths or intensive care unit admission). Model discrimination was tested using area under receiver operator characteristic curve analyses and calibration was tested using the Hosmer-Lemeshow test statistic. Validation was performed by splitting the dataset into derivation and validation datasets and further tested using bootstrap cross validation. Results A total of 2593 patients were studied and 30% were classified as SSAs. Important independent predictors of SSA were injury severity score ≤8 (OR 7.8; 95% CI 5.0 to 11.9), Glasgow coma score 14–15 (OR 3.2; 95% CI 1.8 to 5.4), no need for operative intervention (OR 2.2; 95% CI 1.6 to 3.2) and age < 65 years. (OR 1.7; 95% CI 1.2 to 2.6). The overall model had an area under receiver operator characteristic curve of 0.84 (95% CI 0.82 to 0.87) for the derivation dataset. After bootstrap cross validation the area under the curve of the final model was 0.83 (95% CI 0.81 to 0.84). Conclusions We report a prediction rule that could be used to establish admission criteria for a trauma short stay unit. Further studies are required to prospectively validate the prediction rule.
Publisher: England: BMJ Publishing Group LTD
Language: English
Identifier: ISSN: 1472-0205
EISSN: 1472-0213
DOI: 10.1136/emermed-2012-202222
PMID: 23407379
Source: AUTh Library subscriptions: ProQuest Central
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Deriving a prediction rule for short stay admission in trauma patients admitted at a major trauma centre in Australia

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