Predicting length of stay in thermal burns: A systematic review of prognostic factors

• Published in: Burns 2013-11, Vol.39 (7), p.1331-1340
• Main Authors: Hussain, Amer, Dunn, Ken W
• Format: Article
• Language: English
• Subjects: Burns; Critical Care; Humans; Length of Stay; Outcome; Prediction; Prognosis; Risk Factors; Severity-adjustment
• ISSN: 0305-4179; 1879-1409
• DOI: 10.1016/j.burns.2013.04.026

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 ( R2 ) 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.