Abstract:
Introduction: Although there are numerous methods available for monitoring the patient/parturient's condition during anesthesia, the options for monitoring the fetus's condition are significantly more restricted. Of major importance in this context is the value of oxygen delivery to the fetal tissues (DfetusO2), which, in turn, depends on several factors, including the method of anesthesia used. Material and Methods: Based on the current knowledge of the physiology of the maternal and fetal body, as well as the interaction between them, it is possible to create a system dynamics model that would evolve in real time depending on several factors, including anesthetic ones, which would allow the estimation of DfetusO2 continuously. System dynamics modeling tools (e.g., NetLogo SDM and others) can be used for this. Results: Using the NetLogo programming environment, a system dynamics model was created, which consists of three subsystems: (a) the maternal system (primarily, elements that determine oxygen transport), (b) the fetoplacental system, and (c) the fetal system (with emphasis on the elements that determine DfetusO2). The DfetusO2 value is influenced by the dynamics of the physiological parameters, which are the foundation of the three subsystems and can be monitored using traditional methods. Modifying specific parameters within each subsystem directly impacts DfetusO2, the central element of the model's graphical interface. In this way, DfetusO2 can continuously monitor oxygen delivery to fetal tissues. The demo version of the created model includes several scenarios: (a) state of anesthesia, (b) maternal pathology (e.g., anemia, heart failure, etc.), and (c) fetoplacental pathology (e.g., abruptio placentae). Conclusions: The created model allows the modeling of physiological events in the described framework with the continuous estimation of DfetusO2, which, in turn, could fill the gap in monitoring the state of the fetus during the perianesthetic period. The next step in the current research would be to evaluate the model's accuracy under clinical conditions.
