Effective treatment of critically ill patients requires adequate administration of drugs to resuscitate and stabilize the patient by maintaining the volume of blood against bleeding and preserving the blood circulation to the body tissues. In today’s clinical practice, drug dose is adjusted by human clinicians. Therefore, treatment is often subjective and ad-hoc depending on the style and experience of the clinician. Thus, in theory, it is anticipated that well-designed automated critical care systems can help clinicians make superior adjustments to drug doses while they are always vigilant and never distracted by other obligations. Yet, automated critical care systems developed by researchers are ad-hoc, because they determine the control law, i.e., drug infusion rate, using input-output observations rather than the insights on the patient’s physiological states gained from rigorous data-based analysis of mathematical models. Thus, it is worth developing model-based automated systems relating the fluid and vasopressor dose input to the underlying physiological states. This necessitates dose-response mathematical models capable of reproducing realistic physiological and dose-mediated states with reasonable computational load. However, most of existing models are too simplistic to reflect physiological reality, while others are too complicated with thousands of parameters to tune. To address these challenges, we believe that a hybrid physiologic-phenomenological modeling paradigm is effective in developing mathematical models for automated systems: low-order phenomenological models with adaptive personalization capability are suited to develop control algorithms, while physiological models can provide high-fidelity patterns with physiological transparency suited to interpret the underlying physiological states.

In this study, hybrid physiologic-phenomenological models of blood volume and cardiovascular responses to fluid and vasopressor infusion are successfully developed and validated using experimental data. It is shown that the models can adequately reproduce the underlying physiological states and endpoints to fluid and vasopressor infusion. The main contributions of this research are lined in the following three folds. First, the models are robust against inter-individual variability, in which they can be adapted to each patient with a small number of tunable parameters. Second, they are physiologically transparent where the underlying physiological states not measured in the standard clinical setting can be interpreted and streamlined during an intervention. And eventually the interpreted underlying states can be employed as direct endpoints to monitor the patient and guide the treatment in a closed-loop or decision-support platform.