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Journal Article

Citation

Boissin C, Wallis LA, Kleintjes W, Laflamme L. Burns 2019; ePub(ePub): ePub.

Affiliation

Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden; University of South Africa, Pretoria, South Africa. Electronic address: lucie.laflamme@ki.se.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.burns.2019.03.005

PMID

30928024

Abstract

OBJECTIVE: Little is known concerning the factors associated with in-hospital mortality of trauma patients in resource-constrained settings, not least in burns centres. We investigated this question in the adult burns centre at Tygerberg Hospital in Cape Town. We further assessed whether the Abbreviated Burn Severity Index (ABSI) is an accurate predictive score of mortality in this setting.

METHODS: Medical records of all patients admitted with fresh burns over a two-year period (2015 and 2016) were scrutinized to obtain data on patient, injury and admission-related characteristics. Association with in-hospital mortality was investigated for flame burns using logistic regressions and expressed as odds ratios (ORs). The mortality prediction of the ABSI score was assessed using sensitivity and specificity analyses.

RESULTS: Overall the in-hospital mortality was 20.4%. For the 263 flame burns, while crude ORs suggested gender, burn depth, burn size, inhalation injury, and referral status were all individually significantly associated with mortality, only the association with female gender, not being referred and burn size remained significant after adjustments (adjusted ORs = 3.79, 2.86 and 1.11 (per percentage increase in size) respectively). For the ABSI score, sensitivity and specificity were 84% and 86% respectively.

CONCLUSION: In this specialised centre, mortality occurs in one in five patients. It is associated with a few clinical parameters, and can be predicted using the ABSI score.

Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.


Language: en

Keywords

Burns; Injury; Mortality prediction; Referral; Resource-constrained setting

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