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Ann Burns Fire Disasters. 2018 Jun 30;31(2):89-93.

A comparison of injury scoring systems in predicting burn mortality.

Author information

1
William Beaumont Army Medical Center, El Paso, Texas, USA.
2
Maricopa Integrated Health System, Phoenix, Arizona, USA.
3
Arizona Burn Center, Maricopa Integrated Health System, Phoenix, Arizona, USA.

Abstract

in English, French

The models most widely used to predict burn patient mortality are the revised Baux score, Ryan, Smith, McGwin, Abbreviated Burn Severity Index (ABSI), Belgian Outcome of Burn Injury (BOBI), and the Fatality by Longevity, APACHE II score, Measured Extent of burn, and Sex (FLAMES). Improvements in critical care have reduced mortality resulting from severe burns, which may affect the predictive strength of older models. We conducted a cross-validation study on all burn patients (n = 114) with TBSA greater than 20%, admitted to the Arizona Burn Center between 2014 and 2016. The study compared the accuracy of seven previously validated burn-specific models and one new model derived for our cohort. Data were collected on age, ethnicity, gender, total body surface area burned (TBSA), inhalational injury, associated trauma, and injury severity (ISS, APACHE II). The accuracy of each model was tested using logistic regression, preserving the published regression coefficients. Predictive performance of the models was assessed by Receiving Operator Curve (ROC) curve analyses and Hosmer-Lemeshow (H-L) goodness of fit tests. Age, TBSA and APACHE II score were found to be significant, independent risk factors for patient mortality. The FLAMES model performed best (AUC 0.96) and was comparable to our native model (AUC 0.96). The revised Baux score was both accurate and easy to calculate, making it clinically useful. The older models demonstrated adequate predictive performance compared with the newer models. Even without key burn parameters, the APACHE II score performed well in critically ill patients with moderate to severe burn injuries.

KEYWORDS:

injury severity; model; mortality; prediction

PMID:
30374258
PMCID:
PMC6199008

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