George C. Kramer

Objective : To develop a high-fidelity mathematical model intended to replicate the cardiovascular (CV) responses of a critically ill patient to vasoplegic shock-induced hypotension and vasopressor therapy. Methods: The mathematical model consists of a lumped-parameter CV physiology model with baroreflex modulation feedback and a phenomenological dynamic dose-response model of a vasopressor. The adequacy of the proposed mathematical model was investigated using an experimental dataset acquired from 10 pigs receiving phenylephrine (PHP) therapy after vasoplegic shock induced via sodium nitroprusside (SNP). Results: Upon calibration, the mathematical model could (i) faithfully replicate the effects of PHP on dynamic changes in blood pressure (BP), cardiac output (CO), and systemic vascular resistance (SVR) (root-mean-squared errors between measured and calibrated mathematical responses: mean arterial BP 2.5+/-1.0 mmHg, CO 0.2+/-0.1 lpm, SVR 2.4+/-1.5 mmHg/lpm; r value: mean arterial BP 0.96+/-0.01, CO 0.65+/-0.45, TPR 0.92+/-0.10) and (ii) predict physiologically plausible behaviors of unmeasured internal CV variables as well as secondary baroreflex modulation effects. Conclusion: This mathematical model is perhaps the first of its kind that can comprehensively replicate both primary (i.e., direct) and secondary (i.e., baroreflex modulation) effects of a vasopressor drug on an array of CV variables, rendering it ideally suited to pre-clinical virtual evaluation of the safety and efficacy of closed-loop control algorithms for autonomous vasopressor administration once it is extensively validated. Significance: This mathematical model architecture incorporating both direct and baroreflex modulation effects may generalize to serve as part of an effective platform for high-fidelity in silico simulation of CV responses to vasopressors during vasoplegic shock.

Introduction: In patients with multi-organ system trauma, the diagnosis of coinciding traumatic brain injury can be difficult due to injuries from the hemorrhagic shock that confound clinical and radiographic signs of traumatic brain injury. In this study, a novel technique using heart rate variability was developed in a porcine model to detect traumatic brain injury early in the setting of hemorrhagic shock without the need for radiographic imaging or clinical exam. Methods: A porcine model of hemorrhagic shock was used with an arm of swine receiving hemorrhagic shock alone and hemorrhagic shock with traumatic brain injury. High-resolution heart rate frequencies were collected at different time intervals using waveforms based on voltage delivered from the heart rate monitor. Waveforms were analyzed to assess statistically significant differences between heart rate variability parameters in those with hemorrhagic shock and traumatic brain injury versus those with only hemorrhagic shock. Stochastic analysis was used to assess the validity of results and create a model by machine learning to better assess the presence of traumatic brain injury. Results: Significant differences were found in several heart rate variability parameters between the two groups. Additionally, significant differences in heart rate variability parameters were found in swine within 1 hour of inducing hemorrhage in those with traumatic brain injury versus those without. These results were confirmed with stochastic analysis and machine learning was used to generate a model which determined the presence of traumatic brain injury in the setting of hemorrhage shock with 91.6% accuracy. Conclusions: Heart rate variability represents a promising diagnostic tool to aid in the diagnosis of traumatic brain injury within 1 hour of injury.

Objective: Individual physiological experiments typically provide useful but incomplete information about a studied physiological process. As a result, inferring the unknown parameters of a physiological model from experimental data is often challenging. The objective of this paper is to propose and illustrate the efficacy of a collective variational inference (C-VI) method, intended to reconcile low-information and heterogeneous data from a collection of experiments to produce robust personalized and generative physiological models. Methods: To derive the C-VI method, we utilize a probabilistic graphical model to impose structure on the available physiological data, and algorithmically characterize the graphical model using variational Bayesian inference techniques. To illustrate the efficacy of the C-VI method, we apply it to a case study on the mathematical modeling of hemorrhage resuscitation. Results: In the context of hemorrhage resuscitation modeling, the C-VI method could reconcile heterogeneous combinations of hematocrit, cardiac output, and blood pressure data across multiple experiments to obtain (i) robust personalized models along with associated measures of uncertainty and signal quality, and (ii) a generative model capable of reproducing the physiological behavior of the population. Conclusion: The C-VI method facilitates the personalized and generative modeling of physiological processes in the presence of low-information and heterogeneous data. Significance: The resulting models provide a solid basis for the development and testing of interpretable physiological monitoring, decision-support, and closed-loop control algorithms.

Objective: Existing burn resuscitation protocols exhibit a large variability in treatment efficacy. Hence, they must be further optimized based on comprehensive knowledge of burn pathophysiology. A physics-based mathematical model that can replicate physiological responses in diverse burn patients can serve as an attractive basis to perform non-clinical testing of burn resuscitation protocols and to expand knowledge on burn pathophysiology. We intend to develop, optimize, validate, and analyze a mathematical model to replicate physiological responses in burn patients. Methods: Using clinical datasets collected from 233 burn patients receiving burn resuscitation, we developed and validated a mathematical model applicable to computer-aided in-human burn resuscitation trial and knowledge expansion. Using the validated mathematical model, we examined possible physiological mechanisms responsible for the cohort-dependent differences in burn pathophysiology between younger versus older patients, female versus male patients, and patients with versus without inhalational injury. Results: We demonstrated that the mathematical model can replicate physiological responses in burn patients associated with wide demographic characteristics and injury severity, and that an increased inflammatory response to injury may be a key contributing factor in increasing the mortality risk of older patients and patients with inhalation injury via an increase in the fluid retention. Conclusion: We developed and validated a physiologically plausible mathematical model of volume kinetic and kidney function after burn injury and resuscitation suited to in-human application. Significance: The mathematical model may provide an attractive platform to conduct non-clinical testing of burn resuscitation protocols and test new hypotheses on burn pathophysiology.

Physiological measurements are an integral part of many established and emerging engineering and biomedical applications that involve physiological modeling, physiological state estimation, and physiological closed loop control. In practice, such measurements exhibit a large degree of variability, which is apparent at multiple levels, including disturbances acting on measured signals and unexpected physiological behavior in certain individuals. In this short paper, we present an inference-based approach to estimating the atypicality of an individual's physiological data both at the level of measurement and physiological behavior. For this purpose, we use data from a cohort of subjects to infer, simultaneously, model representations for measurement disturbances and atypicality of physiological behavior. Using a case study on hematocrit (HCT), cardiac output (CO), and mean arterial pressure (MAP) measurements in response to hemorrhage and colloid infusions, we discuss the merits of the presented approach in deriving reliable subject atypicality and signal quality indicators for physiological data.

Pulmonary injury can be characterized by an increased need for fraction of inspired oxygen or inspired oxygen percentage (FiO2) to maintain arterial blood saturation of oxygenation (SaO2). We tested a smart oxygenation system (SOS) that uses the activity of a closed-loop control FiO2 algorithm (CLC-FiO2) to rapidly assess acute respiratory distress syndrome (ARDS) severity so that rescue ventilation (RscVent) can be initiated earlier. After baseline data, a pulse-oximeter (noninvasive saturation of peripheral oxygenation [SpO2]) was placed. Sheep were then subjected to burn and smoke inhalation injury and followed for 48 h. Initially, sheep were spontaneously ventilating and then randomized to standard of care (SOC) (n = 6), in which RscVent began when partial pressure of oxygen (PaO2) < 90 mmHg or FiO2 < 0.6, versus SOS (n = 7), software that incorporates and displays SpO2, CLC-FiO2, and SpO2/CLC-FiO2 ratio, at which RscVent was initiated when ratio threshold < 250. RscVent was achieved using a G5 Hamilton ventilator (Bonaduz, Switzerland) with adaptive pressure ventilation and adaptive support ventilation modes for SOC and SOS, respectively. Outcomes: the time difference from when SpO2/FiO2 < 250 to RscVent initiation was 4.7 ± 0.6 h and 0.2 ± 0.1 h, SOC and SOS, respectively (P < 0.001). Oxygen responsiveness after RscVent, defined as SpO2/FiO2 > 250 occurred in 4/7, SOS and 0/7, SOC. At 48 h the SpO2/FiO2 ratio was 104 ± 5 in SOC versus 228 ± 59 in SOS (P = 0.036). Ventilatory compliance and peak airway pressures were significantly improved with SOS versus SOC (P < 0.001). Data suggest that SOS software, e.g. SpO2/CLC-FiO2 ratio, after experimental ARDS can provide a novel continuous index of pulmonary function that is apparent before other clinical symptoms. Earlier initiation of RscVent translates into improved oxygenation (reduces ARDS severity) and ventilation.

The depth of burn has been an important factor often overlooked when estimating the total resuscitation fluid needed for early burn care. The goal of this study was to determine the degree to which full-thickness (FT) involvement affected overall 24-hour burn resuscitation volumes.We performed a retrospective review of patients admitted to our burn intensive care unit from December 2007 to April 2013, with significant burns that required resuscitation using our computerized decision support system for burn fluid resuscitation. We defined the degree of FT involvement as FT Index (FTI; percentage of FT injury/percentage of total body surface area (TBSA) burned [%FT / %TBSA]) and compared variables on actual 24-hour fluid resuscitation volumes overall as well as for any given burn size.A total of 203 patients admitted to our burn center during the study period were included in the analysis. Mean age and weight were 47 ± 19 years and 87 ± 18 kg, respectively. Mean %TBSA was 41 ± 20 with a mean %FT of 18 ± 24. As %TBSA, %FT, and FTI increased, so did actual 24-hour fluid resuscitation volumes (mL/kg). However, increase in FTI did not result in increased volume indexed to burn size (mL/kg per %TBSA). This was true even when patients with inhalation injury were excluded. Further investigation revealed that as %TBSA increased, %FT increased nonlinearly (quadratic polynomial) (R = 0.994).Total burn size and FT burn size were both highly correlated with increased 24-hour fluid resuscitation volumes. However, FTI did not correlate with a corresponding increase in resuscitation volumes for any given burn size, even when patients with inhalation injury were excluded. Thus, there are insufficient data to presume that those who receive more volume at any given burn size are likely to be mostly full thickness or vice versa. This was influenced by a relatively low sample size at each 10%TBSA increment and larger burn sizes disproportionately having more FT burns. A more robust sample size may elucidate this relationship better.Therapeutic/care management study, level IV.

Objective. Exposure to nerve agents requires prompt treatment. We hypothesized that intraosseous (IO) injections of drug antidotes into the vascularized bone marrow will provide a more rapid and effective means to treat exposure to nerve agents than standard intramuscular (IM) injections. We compared the pharmacokinetics of IM and IO administration of pralidoxime chloride (2-PAM Cl) during normovolemia and hypovolemia, as well as their combined administration during normovolemia in swine. Methods. Ten normovolemic swine were randomly administered 2 mL, 660 mg 2-PAM Cl via the IM or IO route and monitored for 180 minutes. IM versus IO also was compared in 8 hypovolemic swine bled to a mean arterial pressure of 50 mmHg. In a combined group, an IO injection was administered followed by an IM injection 60 minutes later. Blood samples were collected at times over a 180-minute period to calculate standard pharmacokinetic variables to compare the 2 routes of administration. Results. In the normovolemic swine, IM injection achieved therapeutic levels (4 μg/mL) in 2 minutes, whereas IO infusion achieved these levels in less than 15 seconds. 2-PAM-Cl concentrations fell below these levels at 60 minutes post-injection in both groups. In the hypovolemic swine, IM injection achieved therapeutic levels in 4 minutes compared to less than 15 seconds in the IO group. 2-PAM-Cl concentrations fell below therapeutic levels at 12 and 90 minutes post-injection in the IM and IO groups, respectively. In the combined IO-IM treatment, plasma levels remained above therapeutic levels for the entire experiment and had two concentration peaks that corresponded to IO and IM injections. Conclusions. The IO route for the delivery of 2-PAM Cl provides a significant time and high initial blood concentrations advantage compared to the IM route for the prehospital treatment of nerve agent exposure even under hypovolemic conditions. The initial concentration peak associated with IO, but not IM, may provide greater initial therapy at the most critical time.

Assessing respiratory and cardiovascular system coupling can provide new insights into disease progression, but requires accurate analysis of each signal. Respiratory waveform data collected during spontaneous breathing are noisy and respiration rates from long term physiological experiments can vary over a wide range across time. There is a need for automatic and robust algorithms to detect breathing peaks in respiration signals for assessment of the coupling between the respiratory and cardiovascular systems. We developed an automatic algorithm to detect breathing peaks from a respiration signal. The algorithm was tested on respiration signals collected during hemorrhage in a conscious ovine model (N=9, total length = 11.0h). The breathing rate varied from 15 to as high as 160 breaths/min for some animals during the hemorrhage protocol. The sensitivity of the algorithm to detect respiration peaks was 93.7% with a precision of 94.5%. The developed algorithm presents a promising approach to detect breathing peaks in respiration signals from spontaneously breathing subjects. The algorithm was able to consistently identify breathing peaks while the breathing rate varied from 15 to 160 breaths/min.