A model to simulate cardio-respiratory responses to fentanyl analgesia after traumatic injury

Prehospital pain management is challenging and is expected to become even more complex in future large-scale combat operations, where mass-casualty events and evacuation delay may be inevitable. An improved understanding of how analgesic drugs affect the physiological response of trauma patients can enhance treatment efficacy of combat casualties. We previously developed and validated a cardio-respiratory (CR) model for humans that accounts for vital-sign responses to hemorrhagic injuries, resuscitation with six fluid types, airway obstruction, and ketamine analgesia. Here, we extended the model to include the effect of fentanyl on vital signs by integrating existing fentanyl pharmacokinetic-pharmacodynamic models with the neuronal controller of the model. We calibrated and validated the extended model using experimental data from eight studies involving intravenous fentanyl administration (0.71-50.00 μg/kg) to healthy humans and swine with hemorrhagic injury. The model predictions reasonably captured the trend of the experimental data, with root mean square errors (RMSEs) between model predictions and measured data of 0.83 L/min for minute ventilation (MV), 1.20 mmHg for end-tidal carbon dioxide, 0.09 L for tidal volume, and 1.61 mmHg for mean arterial pressure, all of which were within 3-12% of their baseline values. For plasma fentanyl concentration, we obtained RMSEs of 0.79 μg/L in humans and 25.83 μg/L in swine. In simulations, we observed that as hemorrhage increased from 0 to 40% of blood volume, the fentanyl-induced decrease in MV increased from 19 to 34% of its baseline value prior to administration due to reduced fentanyl clearance. Similarly, in simulations of airway obstruction, the fentanyl-induced decrease in MV was 26% of its baseline value after a 100% obstruction compared to only 19% for a no-obstruction condition. Given that most combat casualties receive either fentanyl or ketamine for pain management, the ability to predict and quantify the physiological effects of these drugs will allow us to generate relevant synthetic datasets of diverse battlefield scenarios.