Machine learning for the prediction of preoxygenation technique in trauma
Description
Background Preoxygenation can be achieved best by non-invasive ventilation techniques (NIV). Objective With the help of machine learning, the decision-making process against or in favour of NIV for preoxygenation in severely injured preclinical patients shall be evaluated. Methods A registry-based, retrospective analysis in preclinical adult trauma patients in south-western Germany between 2018 to 2020 was conducted. Attributes considered were the initial vital signs, Glasgow Coma Scale, airway devices, administered medication, description of difficult airway, emergency interventions, shock index, age and pre emergency status. A decision tree model (REPTree) and two Bayesian network (BN) were created, one with all and the other with the attributes occurring in the decision tree. Results 992 datasets with 333 cases of NIV (33%) were identified. Main splitting points in the decision tree model were the attributes rhonchus and bronchial spasm, videolaryngoscopy, respiratory rate, heart rate, age, oxygen saturation and head injury. The area under the receiver operating characteristics was between 0.97 (original BN; 95% CI, 0.96-0.97) and 0.93 (REPTree, 95% CI, 0.92-0.93). For the prediction, the precision-recall area was 0.96 (BN, 95% CI, 0.96-0.97) and 0.88 (REPTree, 95% CI, 0.87-0.89) and for exclusion 0.96 (BN, 95% CI, 0.96-0.97) and 0.94 (REPTree, 65% CI, 0.93-0.94). The simplified BN performed equally to the original BN. Conclusion The presented models demonstrated a feasibility for modeling decision making as well as an excellent performance. An expended model should contain internal and neurological patients as well as the effectiveness of the chosen method and could therefore support emergency medical crews. Files: Supplement Bayesian Network max 3 nodes XML BIF.xml • XML data file with all nodes and probabilities of the final Bayesian network with a maximum of 3 parental nodes that can be implemented in WEKA Supplement Simplified Bayesian Network max 3 nodes XML BIF.xml • XML data file with all nodes and probabilities of the simplified Bayesian network with a maximum of 3 parental nodes that can be implemented in WEKA