Literature DB >> 18301208

Consensus or data-derived anatomic injury severity scoring?

Lynne Moore1, André Lavoie, Natalie Le Sage, Eric Bergeron, Marcel Emond, Belkacem Abdous.   

Abstract

BACKGROUND: Anatomic injury severity scores can be grouped into two classes; consensus-derived and data-derived. The former, including the Injury Severity Score (ISS), the New Injury Severity Score (NISS), and the Anatomic Profile Score (APS), are based on the severity score of the Abbreviated Injury Scale (AIS), assigned by clinical experts. The latter, including the International Classification of Disease Injury Severity Score (ICISS) and the Trauma Registry Abbreviated Injury Scale Score (TRAIS) are based on survival probabilities calculated in large trauma databases. We aimed to compare the predictive accuracy of consensus-derived and data-derived severity scores when considered alone and in combination with age and physiologic status.
METHODS: Analyses were based on 25,111 patients from the trauma registries of the four Level I trauma centers in the province of Quebec, Canada, abstracted between April 1998 and March 2005. The predictive validity of each severity score was evaluated in logistic regression models predicting hospital mortality using measures of discrimination (Area Under the Receiver Operating Characteristics curve [AUC]) and calibration (Hosmer-Lemeshow statistic [HL]).
RESULTS: Data-derived scores had consistently better predictive accuracy than consensus-derived scores in univariate models (p < 0.0001) but very little difference between scores was observed in models including information on age and physiologic status. The difference in AUC between the least accurate severity score (ISS) and the most accurate severity score (TRAIS) was 15% in anatomic-only models but fell to 2% in models including age and physiologic status.
CONCLUSIONS: Data-derived scores provide more accurate mortality prediction than consensus-derived scores do when only anatomic injury severity is considered but offer little advantage if age and physiologic status are taken into account. This may be because of the fact that data-derived scores are not an independent measure of anatomic injury severity.

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Year:  2008        PMID: 18301208     DOI: 10.1097/01.ta.0000241201.34082.d4

Source DB:  PubMed          Journal:  J Trauma        ISSN: 0022-5282


  8 in total

1.  Overcoming barriers to population-based injury research: development and validation of an ICD10-to-AIS algorithm.

Authors:  Barbara Haas; Wei Xiong; Maureen Brennan-Barnes; David Gomez; Avery B Nathens
Journal:  Can J Surg       Date:  2012-02       Impact factor: 2.089

2.  Simplified alternative to the TRISS method for resource-constrained settings.

Authors:  Shinji Nakahara; Masao Ichikawa; Akio Kimura
Journal:  World J Surg       Date:  2011-03       Impact factor: 3.352

3.  Evaluating the validity of multiple imputation for missing physiological data in the national trauma data bank.

Authors:  Lynne Moore; James A Hanley; André Lavoie; Alexis Turgeon
Journal:  J Emerg Trauma Shock       Date:  2009-05

4.  Perioperative and acute care outcomes in morbidly obese patients with acetabular fractures at a Level 1 trauma center.

Authors:  Heather K Vincent; Edward Haupt; Sonya Tang; Adaeze Egwuatu; Richard Vlasak; MaryBeth Horodyski; Donna Carden; Kalia K Sadisivan
Journal:  J Orthop       Date:  2014-05-10

5.  The counterintuitive effect of multiple injuries in severity scoring: a simple variable improves the predictive ability of NISS.

Authors:  Stefano Di Bartolomeo; Chiara Ventura; Massimiliano Marino; Francesca Valent; Susanna Trombetti; Rossana De Palma
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2011-04-19       Impact factor: 2.953

Review 6.  Comparison of the Ability to Predict Mortality between the Injury Severity Score and the New Injury Severity Score: A Meta-Analysis.

Authors:  Qiangyu Deng; Bihan Tang; Chen Xue; Yuan Liu; Xu Liu; Yipeng Lv; Lulu Zhang
Journal:  Int J Environ Res Public Health       Date:  2016-08-16       Impact factor: 3.390

Review 7.  Systematic review of predictive performance of injury severity scoring tools.

Authors:  Hideo Tohira; Ian Jacobs; David Mountain; Nick Gibson; Allen Yeo
Journal:  Scand J Trauma Resusc Emerg Med       Date:  2012-09-10       Impact factor: 2.953

8.  Artificial intelligence to predict in-hospital mortality using novel anatomical injury score.

Authors:  Wu Seong Kang; Heewon Chung; Hoon Ko; Nan Yeol Kim; Do Wan Kim; Jayun Cho; Hongjin Shim; Jin Goo Kim; Ji Young Jang; Kyung Won Kim; Jinseok Lee
Journal:  Sci Rep       Date:  2021-12-07       Impact factor: 4.379

  8 in total

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