Literature DB >> 3203503

Progress toward a new injury severity characterization: severity profiles.

W J Sacco1, J W Jameson, W S Copes, M M Lawnick, S L Keast, H R Champion.   

Abstract

Presented is a new seven-dimensional injury severity profile. The profile includes three physiologic assessments and four variables which express the number, location, and severity of a patient's injuries in terms of 'Abbreviated injury scale' values. The physiologic assessments are coded values for the 'Glasgow coma scale', systolic blood pressure, and respiratory rate. Also presented are survival-death predictive values of a cluster model based on survival rates of clusters of profiles of 2569 blunt-injured and penetrating-injured patients. The cluster model has a relative information gain (R) of 0.90. R is a measure of predictive value relative to an infallible predictor. It varies from 0 to 1, the higher the value the better the predictive value. The model had 26 false negatives (deaths predicted to survive) and 35 false positives (survivors predicted to die) giving rise to a false negative rate of 9.3%, a false positive rate of 1.4% and a misclassification rate of 2.4%. The R value and false negative rate are particularly noteworthy, the R value being higher than, and the false negative rate much lower than typical values of 30-40% achieved by TRISS (a combination index based on trauma score, injury severity score and patient age). Also noteworthy is that the clustering was independent of survival/death outcome information and that the good results were achieved even though patient age has not yet been incorporated into the model.

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Year:  1988        PMID: 3203503     DOI: 10.1016/0010-4825(88)90059-5

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  3 in total

1.  Evaluations of hospital and/or trauma care systems.

Authors:  W S Copes; W J Sacco; H R Champion
Journal:  Arch Emerg Med       Date:  1989-09

Review 2.  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

3.  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

  3 in total

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