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Burn Injury Severity in Adults: Proposed Definitions Based on the National Burn Research Dataset

Previous iterations of burn severity (mild, moderate, and severe) were not data-driven and were outdated. Clustering analyses have gained popularity for identifying homogenous subgroups from heterogeneous medical conditions, such as asthma, sepsis, and lung disease. There is no consensus in burn lit...

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Bibliographic Details
Published in:Journal of burn care & research 2024-09
Main Authors: Heard, Jason, Ren, Yuni, Taylor, Sandra L, Sen, S, Palmieri, T, Romanowski, K, Greenhalgh, D
Format: Article
Language:English
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Summary:Previous iterations of burn severity (mild, moderate, and severe) were not data-driven and were outdated. Clustering analyses have gained popularity for identifying homogenous subgroups from heterogeneous medical conditions, such as asthma, sepsis, and lung disease. There is no consensus in burn literature regarding what constitutes massive burns. The current classification includes a 20% total body surface area (TBSA) burn and a 95% TBSA burn as severe. Latent class and hierarchical clustering analyses were applied to the American Burn Association National Burn Research Dataset. Cluster variables included length of stay, length of stay, intensive care unit length of, number and type of procedures, and number and type of complications. Non-clustering variables were evaluated after clustering, including burned TBSA, inhalation injury, mortality, discharge disposition, age, sex, and race. Latent class analysis suggested three clusters. Hierarchical clustering analysis was applied to the most severe latent class, creating four total burn severity groups. In total, 112,297 patients were included in the final analysis. The mean TBSA burned for each class is 4.26±4.91 for minor, 8.07±8.39 for moderate, 22.76±17.31 for severe and 36.72±21.61 for massive. The age and sex proportions were similar among all clusters. The clustering variables steadily increased for each severity cluster. Mortality was the highest in the massive cluster (18.2%). Data informed categories of burn severity were formed using clustering analyses, which will be helpful for triage, data-benchmarking, and class-specific research.
ISSN:1559-047X
1559-0488
1559-0488
DOI:10.1093/jbcr/irae186