Development and Validation of a Bayesian Network Predicting Intubation Following Hospital Arrival Among Injured Children.

BACKGROUND

Inadequate airway management can contribute to preventable trauma deaths. Current machine learning tools for predicting intubation in trauma are limited to adult populations and include predictors not readily available at the time of patient arrival. We developed a Bayesian network to predict intubation in injured children and adolescents using observable data available upon or immediately after patient arrival.

METHODS

We obtained patient demographic, injury, resuscitation, and transportation characteristics from trauma registries from four American College of Surgeons-verified level 1 pediatric trauma centers from January 2010 through December 2021. We trained and validated a Bayesian network to predict emergent intubation after pediatric injury. We evaluated model performance using the area under the receiver operating and calibration curves.

RESULTS

The final model, TITAN (Timing of Intubation in Trauma Analysis Network), incorporated five factors: Glasgow Coma Scale, mechanism of injury, injury type (e.g., penetrating, blunt), systolic blood pressure, and age. The model achieved an area under the receiver operating characteristic curve of 0.83 (95% CI 0.80, 0.85) and had a calibration curve slope of 0.98 (95% CI 0.67, 1.29). TITAN had high specificity (98%), negative predictive value (97%), and accuracy (96%) at a binary probability threshold of 22.6%.

CONCLUSION

The TITAN Bayesian network predicts the risk of intubation in pediatric trauma patients using five factors that are observable early in trauma resuscitation. Prospective validation of the model performance with patient outcomes is needed to assess real-life application benefits and risks.

LEVEL OF EVIDENCE: Prognostic and Epidemiological, Level III.