Applicability of new supervised statistical models to assess burn injury patterns, outcomes, and their interrelationship.

Abstract

The aim of the study was to investigate the possibility of using supervised statistical models to assess burn injury patterns, outcomes and their interrelationship. Using burn study data, a preliminary principal component analysis was carried out and two separate clusters were observed. Observations were split into two classes and analysed by partial least squares (PLS) regression discriminant analysis to assess possible predictors of each class. To assess predictors of total body surface area burned (TBSA), the orthogonal projections to latent structures (OPLS) model was used after PLS regression. The identified classes were later designated as high-risk burn victims and low-risk burn victims. Female gender fell into the high-risk class. Many possible predictors were found to be associated with burn injury extent, after modelling the natural logarithm of TBSA by OPLS. The fitted model explained 76% of variation in Y. It excluded up to 9% of orthogonal variation captured in two orthogonal components. This seems to be the first application of the OPLS model in public health epidemiology. The results of this study were promising regarding the use of supervised models in injury pattern analysis.