Citation

Hussain A, Dunn KW. Burns 2013; 39(7): 1331-1340.

Affiliation

University Hospital South Manchester, Southmoor Road, Manchester M23 9LT, United Kingdom. Electronic address: amh@doctors.net.uk.

Copyright

(Copyright © 2013, Elsevier Publishing)

DOI

10.1016/j.burns.2013.04.026

PMID

23768707

Abstract

BACKGROUND: Continued improvement in all aspects of the management of thermal injury has resulted in marked improvements in the traditionally reported outcome of mortality. This has resulted in the search for alternative parameters that can be monitored to indicate the performance of burn services. Length of stay (LOS) in hospitalised burn patients has long been considered reflective of injury-associated morbidity, cost and the quality of care, which can be monitored consistently across services. AIM: We undertook a systematic review of published literature pertaining to LOS prognostication in thermal burns to identify the relevant factors, quantify the risk associated with these factors and identify predictive prognostic models. METHODS: Electronic searches were performed on MEDLINE, CINHAL, EMBASE, Web of Science(®), the Cochrane collection and a general web search was performed using Google(®). The searches were complemented by a manual search of the contents of leading burns journals. Quality of the studies included in the review was evaluated against published standards for prognostic studies. RESULTS: Fourteen studies were included in the review after meeting the inclusion/exclusion criteria. Age and %TBSA were the strongest predictors of LOS in these studies. Other significant predictors included % full thickness burn, female gender, inhalation injury, surgery including escharotomy and the depth of burn. Nine studies reported multivariate models for predicting LOS in patients sustaining thermal injury. None of these models were validated and the goodness-of-fit statistic (R(2)) ranged from 0.15 to 0.75. CONCLUSION: This review has demonstrated that %TBSA and age are the best predictors of LOS in published literature. Current prognostic models do not explain a significant proportion of variation in LOS.

Language: en