Predicting Burn Mortality Using a Simple Novel Prediction Model

Abstract

Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

Comparative Analysis of Composite Mortality Prediction Scores in Intensive Care Burn Patients

“…We also found full-thickness burns to be significantly associated with mortality in the multivariate analysis. This is in agreement with multiple recently published studies [20][21][22]; however, there are also conflicting data in the literature [23,24]. It is worth noting that estimates of burn depth on admission remain at best subjective and susceptible to interobserver variability.…”

External Validation of Three Burn-Specific Mortality Prediction Models in Adult Burn Patients at a Tertiary Care Hospital in India

“…We also recommend that more studies be performed to develop and validate indigenous burn specific mortality prediction models, such as the AT score. 3 This score was developed by the authors while studying variables, which affect mortality in adult Indian burn patients. The AT score has been validated in the training population but awaits external validation.…”

“…India being a developing country differs from these nations with respect to the incidence and severity of burns as well as the availability of resources and infrastructure. 3 These disparities preclude the use of these models in our population without extensive validation.…”

“…Keeping this in mind, the authors studied predictors of burn mortality among adult Indian burn patients and formulated the AT score. 3 This score awaits external validation. External validation of a model assesses if the predictions made by these models are generalizable to the native population.…”