Predictors of transfusion in trauma and their utility in the prehospital environment: a scoping review

Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept

PURPOSE

Early administration and protocolization of massive hemorrhage protocols (MHP) has been associated with decreases in mortality, multiorgan system failure, and number of blood products used. Various prediction tools have been developed for the initiation of MHP, but no single tool has demonstrated strong prediction with early clinical data. We sought to develop a massive transfusion prediction model using machine learning and early clinical data.

METHODS

Using the National Trauma Data Bank from 2013 to 2018, we included severely injured trauma patients and extracted clinical features available from the pre-hospital and emergency department. We subsequently balanced our dataset and used the Boruta algorithm to determine feature selection. Massive transfusion was defined as five units at 4 h and ten units at 24 h. Six machine learning models were trained on the balanced dataset and tested on the original.

RESULTS

A total of 326,758 patients met our inclusion with 18,871 (5.8%) requiring massive transfusion. Emergency department models demonstrated strong performance characteristics with mean areas under the receiver-operating characteristic curve of 0.83. Extreme gradient boost modeling slightly outperformed and demonstrated adequate predictive performance with pre-hospital data only, as well as 4-h transfusion thresholds.

CONCLUSIONS

We demonstrate the use of machine learning in developing an accurate prediction model for massive transfusion in trauma patients using early clinical data. This research demonstrates the potential utility of artificial intelligence as a clinical decision support tool.

A comparative analysis of current out-of-hospital transfusion protocols to expert recommendations

Aim

This study aimed to compare current out-of-hospital transfusion (OHT) protocols in Canadian civilian critical care transport organizations (CCTO) to expert recommendations and explore the variability and potential benefits of standardizing OHT practices across Canada.

Methods

A comprehensive cross-sectional study was conducted, encompassing all seven Canadian CCTOs that provide OHT. The study assessed adherence to expert recommendations and examined specific aspects of the transfusion process, such as indications for transfusion and cessation criteria.

Results

The study found an 89% adherence to expert recommendations for OHT among Canadian CCTOs. It highlighted a strong alignment between current practices and recommendations, possibly attributed to collaborative frameworks like the CAN-PATT network. However, notable variability and ambiguity were observed in transfusion indications and cessation criteria. The study also emphasized the potential benefits of standardizing OHT practices, such as improved policy formulation, better interpretation of emerging literature, and evaluation of OHT efficacy.

Conclusion

This cross-sectional study assessed how Canadian CCTOs implement OHT practices compared to expert-recommended practices. The findings underscore the importance of structured protocols in trauma management. Given the consistency in OHT protocol adoption and the comprehensive approach across CCTOs, there's a solid foundation for managing trauma patients in prehospital and transport settings across Canada. As OHT practices continue to evolve, sustained efforts are vital to refine, adapt, and elevate patient care standards in trauma management.