Burns. 2025 Jul 14;51(8):107614. doi: 10.1016/j.burns.2025.107614. Online ahead of print.
ABSTRACT
BACKGROUND: Burn mortality prediction remains a critical aspect in burn medicine. Established scores, such as the ABSI or Baux score, experience continuous revision and improvement due to advances in critical care and surgical procedures. However, these scores often rely on predefined variables and limited statistical models. This study aimed to create a new prediction score that is based solely on machine learning techniques and to assess its performance against established traditional scoring systems.
METHODS: Using different advanced machine learning methods, data from the German burn registry, encompassing over 10,000 cases, were analyzed regarding the most relevant factors concerning mortality and a new prediction score was created. A new prediction model was constructed, employing algorithms such as random forests and gradient boosting. Internal validation was conducted using cross-validation to ensure robustness and reproducibility.
RESULTS: The Bochum Burn Survival (BoBS) score demonstrates strong predictive performance with an accuracy of 93.1 % and ROC AUC of 92.4 %, therefore surpassing traditional scores in predictive performance. Factors such as TBSA and age showed the strongest correlation with mortality, while comorbidities and treatment-specific variables contributed to model refinement. However, further adjustments and external validation beyond the German Burn Registry are crucial in the future.
DISCUSSION: The BoBS score represents a paradigm shift in burn mortality prediction, leveraging the potential of machine learning to analyze complex, high-dimensional datasets. Compared to traditional models, the BoBS score offers improved accuracy while providing insights into underexplored variables that might impact patient outcomes. But challenges remain in integrating such models into clinical workflows and validating them across diverse populations.
CONCLUSION: This score represents a significant advancement in burn mortality prediction by providing an interpretable, machine learning-based scoring system developed using multicenter data from the German Burn Registry. Its application has the potential to enhance decision-making in burn care, marking a significant step forward in personalized medicine for critically injured burn patients.
PMID:40700784 | DOI:10.1016/j.burns.2025.107614