A Lumped-Parameter Model of the Cardiovascular System Response for Evaluating Automated Fluid Resuscitation Systems.

Physiological closed-loop controlled (PCLC) medical devices, such as those designed for blood pressure regulation, can be tested for safety and efficacy in real-world clinical settings. However, relying solely on limited animal and clinical studies may not capture the diverse range of physiological conditions. Credible mathematical models can complement these studies by allowing the testing of the device against simulated patient scenarios. This research involves the development and validation of a low-order lumped-parameter mathematical model of the cardiovascular system's response to fluid perturbation. The model takes rates of hemorrhage and fluid infusion as inputs and provides hematocrit and blood volume, heart rate, stroke volume, cardiac output and mean arterial blood pressure as outputs. The model was calibrated using data from 27 sheep subjects, and its predictive capability was evaluated through a leave-one-out cross-validation procedure, followed by independent validation using 12 swine subjects. Our findings showed small model calibration error against the training dataset, with the normalized root-mean-square error (NRMSE) less than 10% across all variables. The mathematical model and virtual patient cohort generation tool demonstrated a high level of predictive capability and successfully generated a sufficient number of subjects that closely resembled the test dataset. The average NRMSE for the best virtual subject, across two distinct samples of virtual subjects, was below 12.7% and 11.9% for the leave-one-out cross-validation and independent validation dataset. These findings suggest that the model and virtual cohort generator are suitable for simulating patient populations under fluid perturbation, indicating their potential value in PCLC medical device evaluation.

A Mathematical Model for Simulation of Vasoplegic Shock and Vasopressor Therapy.

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.

Development and validation of a mathematical model of heart rate response to fluid perturbation.

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient's condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain pre-clinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of [Formula: see text]. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which [Formula: see text] of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave-one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of [Formula: see text] and [Formula: see text] NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

Nonlinear functional muscle network based on information theory tracks sensorimotor integration post stroke.

Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with impairment due to injury to the central nervous system. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information on motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task, while their muscle activity from 8 muscles in each arm was measured using surface electromyography. Subjects performed the task with their affected hand before and after sensory exposure to the task performed with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies changes in functional muscle connectivity in the affected hand after exposure to sensory information from the less-affected side. > 90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting its potential use in precision rehabilitation interventions.

Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation.

Subject-specific mathematical models for prediction of physiological parameters such as blood volume, cardiac output, and blood pressure in response to hemorrhage have been developed. In silico studies using these models may provide an effective tool to generate pre-clinical safety evidence for medical devices and help reduce the size and scope of animal studies that are performed prior to initiation of human trials. To achieve such a goal, the credibility of the mathematical model must be established for the purpose of pre-clinical in silico testing. In this work, the credibility of a subject-specific mathematical model of blood volume kinetics intended to predict blood volume response to hemorrhage and fluid resuscitation during fluid therapy was evaluated. A workflow was used in which: (i) the foundational properties of the mathematical model such as structural identifiability were evaluated; (ii) practical identifiability was evaluated both pre- and post-calibration, with the pre-calibration results used to determine an optimal splitting of experimental data into calibration and validation datasets; (iii) uncertainty in model parameters and the experimental uncertainty were quantified for each subject; and (iv) the uncertainty was propagated through the blood volume kinetics model and its predictive capability was evaluated via validation tests. The mathematical model was found to be structurally identifiable. Pre-calibration identifiability analysis led to splitting the 180 min of time series data per subject into 50 and 130 min calibration and validation windows, respectively. The average root mean squared error of the mathematical model was 12.6% using the calibration window of (0 min, 50 min). Practical identifiability was established post-calibration after fixing one of the parameters to a nominal value. In the validation tests, 82 and 75% of the subject-specific mathematical models were able to correctly predict blood volume response when predictive capability was evaluated at 180 min and at the time when amount of infused fluid equals fluid loss.

Accuracy assessment methods for physiological model selection toward evaluation of closed-loop controlled medical devices.

Physiological closed-loop controlled (PCLC) medical devices are complex systems integrating one or more medical devices with a patient's physiology through closed-loop control algorithms; introducing many failure modes and parameters that impact performance. These control algorithms should be tested through safety and efficacy trials to compare their performance to the standard of care and determine whether there is sufficient evidence of safety for their use in real care setting. With this aim, credible mathematical models have been constructed and used throughout the development and evaluation phases of a PCLC medical device to support the engineering design and improve safety aspects. Uncertainties about the fidelity of these models and ambiguities about the choice of measures for modeling performance need to be addressed before a reliable PCLC evaluation can be achieved. This research develops tools for evaluating the accuracy of physiological models and establishes fundamental measures for predictive capability assessment across different physiological models. As a case study, we built a refined physiological model of blood volume (BV) response by expanding an original model we developed in our prior work. Using experimental data collected from 16 sheep undergoing hemorrhage and fluid resuscitation, first, we compared the calibration performance of the two candidate physiological models, i.e., original and refined, using root-mean-squared error (RMSE), Akiake information criterion (AIC), and a new multi-dimensional approach utilizing normalized features extracted from the fitting error. Compared to the original model, the refined model demonstrated a significant improvement in calibration performance in terms of RMSE (9%, P = 0.03) and multi-dimensional measure (48%, P = 0.02), while a comparable AIC between the two models verified that the enhanced calibration performance in the refined model is not due to data over-fitting. Second, we compared the physiological predictive capability of the two models under three different scenarios: prediction of subject-specific steady-state BV response, subject-specific transient BV response to hemorrhage perturbation, and leave-one-out inter-subject BV response. Results indicated enhanced accuracy and predictive capability for the refined physiological model with significantly larger proportion of measurements that were within the prediction envelope in the transient and leave-one-out prediction scenarios (P < 0.02). All together, this study helps to identify and merge new methods for credibility assessment and physiological model selection, leading to a more efficient process for PCLC medical device evaluation.

Mathematical model of volume kinetics and renal function after burn injury and resuscitation.

This paper presents a mathematical model of blood volume kinetics and renal function in response to burn injury and resuscitation, which is applicable to the development and non-clinical testing of burn resuscitation protocols and algorithms. Prior mathematical models of burn injury and resuscitation are not ideally suited to such applications due to their limited credibility in predicting blood volume and urinary output observed in wide-ranging burn patients as well as in incorporating contemporary knowledge of burn pathophysiology. Our mathematical model consists of an established multi-compartmental model of blood volume kinetics, a hybrid mechanistic-phenomenological model of renal function, and novel lumped-parameter models of burn-induced perturbations in volume kinetics and renal function equipped with contemporary knowledge on burn-related physiology and pathophysiology. Using the dataset collected from 16 sheep, we showed that our mathematical model can be characterized with physiologically plausible parameter values to accurately predict blood volume kinetic and renal function responses to burn injury and resuscitation on an individual basis against a wide range of pathophysiological variability. Pending validation in humans, our mathematical model may serve as an effective basis for in-depth understanding of complex burn-induced volume kinetic and renal function responses as well as development and non-clinical testing of burn resuscitation protocols and algorithms.

Sparse model-based estimation of functional dependence in high-dimensional field and spike multiscale networks.

OBJECTIVE: Behavior is encoded across multiple scales of brain activity, from binary neuronal spikes to continuous fields including local field potentials (LFP). Multiscale models need to describe both the encoding of behavior and the conditional dependencies in simultaneously recorded spike and field signals, which form a high-dimensional multiscale network. However, learning spike-field dependencies in high-dimensional recordings is challenging due to the prohibitively large number of spike-field signal pairs, which makes standard learning techniques subject to overfitting. APPROACH: We present a sparse model-based estimation algorithm to learn these multiscale network dependencies. We develop a multiscale encoding model consisting of a point process model of binary spikes for each neuron whose firing rate is a function of the LFP network features and behavioral states. Doing so, spike-field dependencies constitute the model parameters to be learned. We resolve the parameter learning challenge by forming a constrained optimization problem to maximize the likelihood with an L1 penalty term that eases the detection of significant spike-LFP dependencies. We then apply the Akaike information criterion (AIC) to force a sparse number of nonzero dependency parameters in the model. MAIN RESULTS: We validate the algorithm using simulations and spike-field data from two non-human primates (NHP) in a 3D motor task with motor cortical recordings and a pro-saccade visual task with prefrontal recordings. We find that by identifying a model with a sparse set of dependency parameters, the algorithm improves spike prediction compared with models without dependencies. Further, the algorithm identifies significantly fewer dependency parameters compared with standard methods while improving their spike prediction likely due to detecting fewer spurious dependencies. Also, spike prediction on any electrode improves by including LFP features from all electrodes compared with using only those on the same electrode. Finally, unlike standard methods, the algorithm uncovers patterns of spike-field network dependencies as a function of distance, brain region, and frequency band. SIGNIFICANCE: This algorithm can help study functional dependencies in high-dimensional spike-field networks and leads to more accurate multiscale encoding models.

Forecasting Hypotension during Vasopressor Infusion via Time Series Analysis.

For optimal management of hypotension during continuous vasopressor infusion, this study investigated two forecasting models, logistic regression (LR) and auto-regressive (AR) models, to predict sustained hypotension episodes (SHEs) in the ICU, before the SHE occurred. Two investigational models were compared to a simple threshold detector, which alerts whenever the BP is less than the specific hypotension threshold. Datasets were collected from 207 patients treated for a variety of clinical indications in two different hospitals (Hospital 1 & 2). For the 60 mmHg hypotension threshold, LR model predicted SHEs an average of 7.0 min before (Hospital 1) and 2.5 min before (Hospital 2), and the AR model predicted SHEs 10.5 min and 2.0 min before (Hospital 1 and 2 respectively). Both were significantly better than the threshold method and without higher false alarm rates. The AR model offered the flexibility to predict for different hypotension thresholds.

Development and In Silico Evaluation of a Model-Based Closed-Loop Fluid Resuscitation Control Algorithm.

OBJECTIVE: To develop and evaluate in silico a model-based closed-loop fluid resuscitation control algorithm via blood volume feedback. METHODS: Model-based adaptive control algorithm for fluid resuscitation was developed by leveraging a low-order lumped-parameter blood volume dynamics model, and then in silico evaluated based on a detailed mechanistic model of circulatory physiology. The algorithm operates in two steps: (1) the blood volume dynamics model is individualized based on the patient's fractional blood volume response to an initial fluid bolus via system identification; and (2) an adaptive control law built on the individualized blood volume dynamics model regulates the blood volume of the patient. RESULTS: The algorithm was able to track the blood volume set point as well as accurately estimate and monitor the patient's absolute blood volume level. The algorithm significantly outperformed a population-based proportional-integral-derivative control. CONCLUSION: Model-based development of closed-loop fluid resuscitation control algorithm may enable regulation of blood volume and monitoring of absolute blood volume level. SIGNIFICANCE: Model-based closed-loop fluid resuscitation algorithm may offer opportunities for standardized and patient-tailored therapy and reduction of clinician workload.

Estimation of Functional Dependence in High-Dimensional Spike-Field Activity.

Behavior is encoded across spatiotemoral scales of brain activity, from small-scale spikes to large-scale local field potentials (LFP). Identifying the functional dependence between spikes and LFP networks during behavior can help understand neural encoding and improve future neurotechnologies, but is difficult to achieve. First, spikes and LFP have different statistical characteristics (binary spikes vs. continuous LFPs) and time-scales. Second, given the prohibitively large number of spike channels and LFP features recorded in today's experiments, learning dependencies between all recorded signals is challenging and prone to overfitting. To solve this challenge, we present a model-based approach to estimate the functional dependence between high-dimensional field features and neuronal spikes. We model the binary time-series of spikes for each neuron as a point process dependent on the behavioral states and LFP features across the network. Given the prohibitively large number of possible spike-LFP dependency parameters, we first employ an Ll-regularization technique to learn the point process model during both supervised and unsupervised learning to ease detection of significant dependency parameters. We then use the Akaike information criterion (AIC) to enforce model sparsity by incorporating only a minimum number of non-zero dependency parameters into the point process model based on a trade-off between model complexity and its prediction power. Using extensive numerical simulations, we show that our method (i) can correctly identify the functional dependencies and thus improve the prediction of spiking activity and (ii) can improve the prediction of spiking activity with significantly fewer number of parameters compared to when regularization is not enforced. Our approach may serve as a tool to investigate brain connectivity patterns across spatiotemporal scales.

Control-oriented physiological modeling of hemodynamic responses to blood volume perturbation.

This paper presents a physiological model to reproduce hemodynamic responses to blood volume perturbation. The model consists of three sub-models: a control-theoretic model relating blood volume response to blood volume perturbation; a simple physics-based model relating blood volume to stroke volume and cardiac output; and a phenomenological model relating cardiac output to blood pressure. A unique characteristic of this model is its balance for simplicity and physiological transparency. Initial validity of the model was examined using experimental data collected from 11 animals. The model may serve as a viable basis for the design and evaluation of closed-loop fluid resuscitation controllers.

In-human subject-specific evaluation of a control-theoretic plasma volume regulation model.

The goal of this study was to conduct a subject-specific evaluation of a control-theoretic plasma volume regulation model in humans. We employed a set of clinical data collected from nine human subjects receiving fluid bolus with and without co-administration of an inotrope agent, including fluid infusion rate, plasma volume, and urine output. Once fitted to the data associated with each subject, the model accurately reproduced the fractional plasma volume change responses in all subjects: the error between actual versus model-reproduced fractional plasma volume change responses was only 1.4 ± 1.6% and 1.2 ± 0.3% of the average fractional plasma volume change responses in the absence and presence of inotrope co-administration. In addition, the model parameters determined by the subject-specific fitting assumed physiologically plausible values: (i) initial plasma volume was estimated to be 36 ± 11 mL/kg and 37 ± 10 mL/kg in the absence and presence of inotrope infusion, respectively, which was comparable to its actual counterpart of 37 ± 4 mL/kg and 43 ± 6 mL/kg; (ii) volume distribution ratio, specifying the ratio with which the inputted fluid is distributed in the intra- and extra-vascular spaces, was estimated to be 3.5 ± 2.4 and 1.9 ± 0.5 in the absence and presence of inotrope infusion, respectively, which accorded with the experimental observation that inotrope could enhance plasma volume expansion in response to fluid infusion. We concluded that the model was equipped with the ability to reproduce plasma volume response to fluid infusion in humans with physiologically plausible model parameters, and its validity may persist even under co-administration of inotropic agents.

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.

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.

Prediction of Hemodynamic Response to Epinephrine via Model-Based System Identification.

In this study, we present a system identification approach to the mathematical modeling of hemodynamic responses to vasopressor-inotrope agents. We developed a hybrid model called the latency-dose-response-cardiovascular (LDC) model that incorporated 1) a low-order lumped latency model to reproduce the delay associated with the transport of vasopressor-inotrope agent and the onset of physiological effect, 2) phenomenological dose-response models to dictate the steady-state inotropic, chronotropic, and vasoactive responses as a function of vasopressor-inotrope dose, and 3) a physiological cardiovascular model to translate the agent's actions into the ultimate response of blood pressure. We assessed the validity of the LDC model to fit vasopressor-inotrope dose-response data using data collected from five piglet subjects during variable epinephrine infusion rates. The results suggested that the LDC model was viable in modeling the subjects' dynamic responses: After tuning the model to each subject, the r (2) values for measured versus model-predicted mean arterial pressure were consistently higher than 0.73. The results also suggested that intersubject variability in the dose-response models, rather than the latency models, had significantly more impact on the model's predictive capability: Fixing the latency model to population-averaged parameter values resulted in r(2) values higher than 0.57 between measured versus model-predicted mean arterial pressure, while fixing the dose-response model to population-averaged parameter values yielded nonphysiological predictions of mean arterial pressure. We conclude that the dose-response relationship must be individualized, whereas a population-averaged latency-model may be acceptable with minimal loss of model fidelity.

Relationship between stroke volume and pulse pressure during blood volume perturbation: a mathematical analysis.

Arterial pulse pressure has been widely used as surrogate of stroke volume, for example, in the guidance of fluid therapy. However, recent experimental investigations suggest that arterial pulse pressure is not linearly proportional to stroke volume. However, mechanisms underlying the relation between the two have not been clearly understood. The goal of this study was to elucidate how arterial pulse pressure and stroke volume respond to a perturbation in the left ventricular blood volume based on a systematic mathematical analysis. Both our mathematical analysis and experimental data showed that the relative change in arterial pulse pressure due to a left ventricular blood volume perturbation was consistently smaller than the corresponding relative change in stroke volume, due to the nonlinear left ventricular pressure-volume relation during diastole that reduces the sensitivity of arterial pulse pressure to perturbations in the left ventricular blood volume. Therefore, arterial pulse pressure must be used with care when used as surrogate of stroke volume in guiding fluid therapy.

An analytic tool for prediction of hemodynamic responses to vasopressors.

This paper presents a new analytic tool for automated control of vasopressor infusion, which uses measured changes in blood pressure to infer changes in the underlying cardiovascular system and then estimate dose-response relationships for the underlying cardinal cardiovascular parameters, i.e., those related to cardiac output (CO) and total peripheral resistance (TPR). Ultimately, blood pressure as a function of vasopressor dose is predicted based on the estimated underlying cardiovascular state by extrapolating the dose-response relationship. As well, this tool adapts to individual subjects with a minimum of individualized training data. In this report, proof-of-principle is provided using experimental epinephrine dose-response data from four different sets of subjects. Given two observations from different infusion rates, the analytic tool was able to accurately predict the groups' blood pressure, heart rate, TPR, stroke volume, and CO as a function of vasopressor dose levels: the root-mean-squared prediction error for the mean arterial pressure (MAP) was consistently smaller than 5% of the underlying MAP; the r(2) values for the TPR, stroke volume, and CO were consistently higher than 0.96; and the limits of agreement between actual versus predicted blood pressure (BP), TPR, stroke volume, and CO were consistently smaller than 8% of the respective underlying values. The proposed analytic tool may provide a meaningful step towards automated control of vasopressor therapy.

Is there opportunity for automated decision-support and closed-loop control in ICU patients receiving vasopressor infusion?

Vasopressors are administered to critically ill patients suffering from a body-wide reduction in blood circulation. In theory, if the vasopressor infusion is either too high or too low, it could be harmful to the patient. In a retrospective analysis, we investigated the degree to which today's intensive care unit (ICU) patients receive appropriate vasopressor therapy, in terms of how often the mean arterial pressure (MAP) was kept within a normative range. Using the MIMIC II database, we studied patients with minute-by-minute MAP data, sourced from the bedside monitor, who were receiving vasopressor therapy. For each record, we identified MAP samples that were out-of-range, i.e., MAP <; 60 mmHg or MAP > 100 mmHg, and grouped these into out-of-range episodes. Each out-of-range episode was categorized as either transient (<; 15 min) or sustained (≥ 15 min). Out of the 224 ICU stays, we identified 152 ICU stays (68% of ICU stays) with at least one sustained MAP out-of-range episode. In that subset, MAP was frequently out-of-range (out-of-range 18.4% of the time) due to a combination of sustained episodes of hypotension and hypertension. Compared with all ICU stays, those stays with sustained out-of-range events did not demonstrate an increased MAP variability per hour. It is possible that the out-of-range events resulted from insufficient dose-adjustment. Technologies that might continuously optimize vasopressor dosing throughout the patient's stay and thereby minimize these abnormal cardiovascular states may be worthy of further study.

Model-based estimation of blood pressure response to epinephrine.

In this study, we present a model-based approach to estimation of blood pressure (BP) response to epinephrine. The proposed approach estimates systolic (SBP), mean (MAP) and diastolic (DBP) BP based on a 2-parameter windkessel (WK) model with dose-dependent total peripheral resistance (TPR), arterial compliance (AC) and stroke volume (SV) indices that is driven by the epinephrine dose, heart rate (HR). Using the epinephrine dose and hemodynamic response data collected for young/old normotensive and hypertensive subject groups, four group-specific models as well as a generalized model were developed and then were evaluated for BP estimation performance. The results indicated that the group-specific model is superior to its generalized counterpart; on average, the root-mean-squared SBP, MAP and DBP estimation errors associated with the group-specific model were only 34%, 52% and 69%, respectively, compared with the generalized model.