SYSTEMS-LEVEL MODELING AND VALIDATION OF CARDIOVASCULAR SYSTEM RESPONSES TO FLUID AND VASOPRESSOR INFUSION FOR AUTOMATED CRITICAL CARE SYSTEMS

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

Closed-Loop Fluid Resuscitation Control Via Blood Volume Estimation

This paper presents a closed-loop control of fluid resuscitation to overcome hypovolemia based on model-based estimation of relative changes in blood volume (BV). In this approach, the control system consists of a model-based relative BV (RBV) estimator and a feedback controller. The former predicts relative changes in the BV response to augmented fluid by analyzing an arterial blood pressure (BP) waveform and the electrocardiogram (ECG). Then, the latter determines the amount of fluid to be augmented by comparing target versus predicted relative changes in BV. In this way, unlike many previous methods for fluid resuscitation based on controlled variable(s) nonlinearly correlated with the changes in BV, fluid resuscitation can be guided by a controlled variable linearly correlated with the changes in BV. This paper reports initial design of the closed-loop fluid resuscitation system and its in silico evaluation in a wide range of hypovolemic scenarios. The results suggest that closed-loop fluid resuscitation guided by a controlled variable linearly correlated with the changes in BV can be effective in overcoming hypovolemia: across 100 randomly produced hypovolemia cases, it resulted in the BV regulation error of 7.98 6 171.6 ml, amounting to 0.18 6 3.04% of the underlying BV. When guided by pulse pressure (PP), a classical controlled variable nonlinearly correlated with the changes in BV; the same closed-loop fluid resuscitation system resulted in persistent under-resuscitation with the BV regulation error of À779.1 6 147.4 ml, amounting to À13.9 6 2.65% of the underlying BV

Hypotension in ICU Patients Receiving Vasopressor Therapy

Vasopressor infusion (VPI) is used to treat hypotension in an ICU. We studied compliance with blood pressure (BP) goals during VPI and whether a statistical model might be efficacious for advance warning of impending hypotension, compared with a basic hypotension threshold alert. Retrospective data were obtained from a public database. Studying adult ICU patients receiving VPI at submaximal dosages, we analyzed characteristics of sustained hypotension episodes (>15 min) and then developed a logistic regression model to predict hypotension episodes using input features related to BP trends. The model was then validated with prospective data. In the retrospective dataset, 102-of-215 ICU stays experienced >1 hypotension episode (median of 2.5 episodes per day in this subgroup). When trained with 75% of retrospective dataset, testing with the remaining 25% of the dataset showed that the model and the threshold alert detected 99.6% and 100% of the episodes, respectively, with median advance forecast times (AFT) of 12 and 0 min. In a second, prospective dataset, the model detected 100% of 26 episodes with a median AFT of 22 min. In conclusion, episodes of hypotension were common during VPI in the ICU. A logistic regression model using BP temporal trend features predicted the episodes before their onset

Damage Identification in Collocated Structural Systems Using Structural Markov Parameters

This paper presents a novel approach to damage identification in a class of collocated multi-input multi-output structural systems. In the proposed approach, damage is identified via the structural Markov parameters obtained from a system identification procedure, which is in turn exploited to localize and quantify damage by evaluating relative changes occurring in the mass and stiffness matrices associated with the structural system. To this aim, an explicit relationship between structural Markov parameters versus mass and stiffness matrices is developed. The main strengths of the proposed approach are that it is capable of quantitatively identifying the occurrence of multiple damages associated with both mass and stiffness characteristics in the structural system, and it is computationally efficient in that it is solely based on the structural Markov parameters but does not necessitate costly calculations related to natural frequencies and mode shapes, making it highly attractive for structural damage detection and health monitoring applications. Numerical examples are provided to demonstrate the validity and effectiveness of the proposed approach

A Lumped-Parameter Subject-Specific Model of Blood Volume Response to Fluid Infusion

This paper presents a lumped-parameter model that can reproduce blood volume response to fluid infusion. The model represents the fluid shift between the intravascular and interstitial compartments as the output of a hypothetical feedback controller that regulates the ratio between the volume changes in the intravascular and interstitial fluid at a target value (called target volume ratio). The model is characterized by only three parameters: the target volume ratio, feedback gain (specifying the speed of fluid shift), and initial blood volume. This model can obviate the need to incorporate complex mechanisms involved in the fluid shift in reproducing blood volume response to fluid infusion. The ability of the model to reproduce real-world blood volume response to fluid infusion was evaluated by fitting it to a series of data reported in the literature. The model reproduced the data accurately with average error and root-mean-squared error (RMSE) of 0.6 % and 9.5 % across crystalloid and colloid fluids when normalized by the underlying responses. Further, the parameters derived for the model showed physiologically plausible behaviors. It was concluded that this simple model may accurately reproduce a variety of blood volume responses to fluid infusion throughout different physiological states by fitting three parameters to a given dataset. This offers a tool that can quantify the fluid shift in a dataset given the measured fractional blood volumes