Citation

Onah CN, Allmendinger R, Handl J, Dunn KW. Int. J. Environ. Res. Public Health 2021; 18(2): e761.

Copyright

(Copyright © 2021, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph18020761

PMID

unavailable

Abstract

With a reduction in the mortality rate of burn patients, length of stay (LOS) has been increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient's LOS. However, few have investigated the association between LOS and a patient's mental and socioeconomic status. There is anecdotal evidence for links between these factors; uncovering these will aid in better addressing the specific physical and emotional needs of burn patients and facilitate the planning of scarce hospital resources. Here, we employ machine learning (clustering) and statistical models (regression) to investigate whether segmentation by socioeconomic/mental status can improve the performance and interpretability of an upstream predictive model, relative to a unitary model. Although we found no significant difference in the unitary model's performance and the segment-specific models, the interpretation of the segment-specific models reveals a reduced impact of burn severity in LOS prediction with increasing adverse socioeconomic and mental status. Furthermore, the socioeconomic segments' models highlight an increased influence of living circumstances and source of injury on LOS. These findings suggest that in addition to ensuring that patients' physical needs are met, management of their mental status is crucial for delivering an effective care plan.

Language: en

Keywords

socioeconomic status; clustering; length of stay; burn care; collaborative decision-making; mental state; predictive models; regression analysis