Predicting the Need to Perform Life-Saving Interventions in Trauma Patients by Using New Vital Signs and Artificial Neural Networks
Previous work in risk stratification of critically injured patients involved artificial neural networks (ANNs) of various configurations tuned to process traditional vital signs and demographical, clinical, and laboratory data obtained via direct contact with the patient. We now report “new vital signs” (NVSs) that are superior in distinguishing the injured and can be derived without hands-on patient contact. Data from 262 trauma patients are presented, in whom NVSs derived from electrocardiogram (EKG) analysis (heart-rate complexity and variability) were input into a commercially available ANN. The endpoint was performance of life-saving interventions (LSIs) such as intubation, cardiopulmonary resuscitation, chest-tube placement, needle chest decompression, and blood transfusion. We conclude that based on EKG-derived NVS alone, it is possible to accurately identify trauma patients who undergo LSIs. Our approach may permit development of a next-generation decision support system.
Keywordselectrocardiography heart rate complexity heart rate variability ANN prediction of life-saving interventions
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