Prediction of morbidity and mortality on admission to a burn unit

Sagit Meshulam-Derazon*, Shira Nachumovsky, Dean Ad-El, Jaqueline Sulkes, Daniel J. Hauben

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Improvements in burn care during the last two decades call for new prediction models of morbidity and mortality. The aim of the study was to identify parameters that are predictive of major morbidity factors and risk of mortality in patients with burn injury. METHODS: The charts of 249 patients (236 survivors) aged 1 to 94 years who were treated for second- and third-degree burns from 1995 to 2002 were reviewed. A multivariate linear stepwise regression model was fitted to the data to predict length of hospitalization, length of operations, and mortality rate. RESULTS: Survivors' mean burn size was 14 ± 15 percent of the total body surface area (range, 5 to 90 percent), with a mean hospitalization time of 22.9 ± 17.1 days and a mean operative time of 127.5 ± 166.8 minutes. The prognostic factors in each of the regression models predicted 40 percent and 55 percent of the variance in length of hospital stay and operative time, respectively. Total body surface area alone explained most of the variance (29 percent and 44 percent, respectively). As a result, the authors created shorter formulas: Length of hospitalization (days) = 18 + [total body surface area]/3; Operative time (minutes) = 55 + 4[total body surface area]. Total body surface area and smoke inhalation were the only statistically significant predictors of death. Every 1 percent increase in total body surface area was associated with a 6 percent increase in mortality risk. The presence of smoke inhalation increased mortality risk by nine-fold. CONCLUSIONS: Using objective measurements in burn treatment is of great importance. The formulas presented by the authors explain a considerable percentage of the probability of morbidity in burn victims. The authors suggest that other burn units develop their own statistically supported prediction models.

Original languageEnglish
Pages (from-to)116-120
Number of pages5
JournalPlastic and Reconstructive Surgery
Issue number1
StatePublished - Jul 2006


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