Is the Target of 1 Day of Stay per 1% Total Body Surface Area Burned Achieved in Chemical Burns?

Association of length of hospital stay, total body surface area burned, and Carbapenem-Resistant Klebsiella pneumoniae infection in burn patients: A retrospective study

BACKGROUND

The specific relationships between total body surface area burned (TBSA), length of stay (LOS), and carbapenem-resistant Klebsiella pneumoniae (CRKP) infection remain unclear. This study aimed to explore the relationship between TBSA and CRKP infection and to examine whether LOS mediates the association between TBSA and the risk of CRKP infection.

METHODS

We retrospectively collected the clinical data of adult burn patients admitted to Nanyang Nanshi Hospital between 2021 and 2023. We used multivariate logistic regression analysis to study the risk factors for CRKP infection; restricted cubic spline analysis to investigate the associations between TBSA, LOS, and CRKP infection; and mediation analysis to examine whether LOS mediated the association between TBSA and CRKP infection.

RESULTS

A total of 178 subjects were included in the study; 104 (58.4 %) were male, and the median age was 47 (33-59) years. Thirty-one (17.4 %) subjects developed CRKP infection. Both TBSA and LOS showed a linear positive correlation with the risk of CRKP infection. The LOS mediated the association between TBSA and incident CRKP infection, with a proportion of 17.6 %.

CONCLUSION

The risk of CRKP infection linearly increased as TBSA or LOS increased, and LOS mediated approximately 20 % of the total association between TBSA and CRKP infection.

Using Artificial Intelligence to Obtain More Evidence? Prediction of Length of Hospitalization in Pediatric Burn Patients

Background: It is not only important for counseling purposes and for healthcare management. This study investigates the prediction accuracy of an artificial intelligence (AI)-based approach and a linear model. The heuristic expecting 1 day of stay per percentage of total body surface area (TBSA) serves as the performance benchmark. Methods: The study is based on pediatric burn patient's data sets from an international burn registry (N = 8,542). Mean absolute error and standard error are calculated for each prediction model (rule of thumb, linear regression, and random forest). Factors contributing to a prolonged stay and the relationship between TBSA and the residual error are analyzed. Results: The random forest-based approach and the linear model are statistically superior to the rule of thumb (p < 0.001, resp. p = 0.009). The residual error rises as TBSA increases for all methods. Factors associated with a prolonged LOS are particularly TBSA, depth of burn, and inhalation trauma. Conclusion: Applying AI-based algorithms to data from large international registries constitutes a promising tool for the purpose of prediction in medicine in the future; however, certain prerequisites concerning the underlying data sets and certain shortcomings must be considered.