Algorithmic processing of pressure waveforms to facilitate estimation of cardiac elastance.

BACKGROUND: Cardiac elastances are highly invasive to measure directly, but are clinically useful due to the amount of information embedded in them. Information about the cardiac elastance, which can be used to estimate it, can be found in the downstream pressure waveforms of the aortic pressure (P(ao)) and the pulmonary artery (P(pa)). However these pressure waveforms are typically noisy and biased, and require processing in order to locate the specific information required for cardiac elastance estimations. This paper presents the method to algorithmically process the pressure waveforms. METHODS: A shear transform is developed in order to help locate information in the pressure waveforms. This transform turns difficult to locate corners into easy to locate maximum or minimum points as well as providing error correction. RESULTS: The method located all points on 87 out of 88 waveforms for Ppa, to within the sampling frequency. For Pao, out of 616 total points, 605 were found within 1%, 5 within 5%, 4 within 10% and 2 within 20%. CONCLUSIONS: The presented method provides a robust, accurate and dysfunction-independent way to locate points on the aortic and pulmonary artery pressure waveforms, allowing the non-invasive estimation of the left and right cardiac elastance.

Beat-to-beat estimation of the continuous left and right cardiac elastance from metrics commonly available in clinical settings.

INTRODUCTION: Functional time-varying cardiac elastances (FTVE) contain a rich amount of information about the specific cardiac state of a patient. However, a FTVE waveform is very invasive to directly measure, and is thus currently not used in clinical practice. This paper presents a method for the estimation of a patient specific FTVE, using only metrics that are currently available in a clinical setting. METHOD: Correlations are defined between invasively measured FTVE waveforms and the aortic and pulmonary artery pressures from 2 cohorts of porcine subjects, 1 induced with pulmonary embolism, the other with septic shock. These correlations are then used to estimate the FTVE waveform based on the individual aortic and pulmonary artery pressure waveforms, using the "other" dysfunction's correlations as a cross validation. RESULTS: The cross validation resulted in 1.26% and 2.51% median errors for the left and right FTVE respectively on pulmonary embolism, while the septic shock cohort had 2.54% and 2.90% median errors. CONCLUSIONS: The presented method accurately and reliably estimated a patient specific FTVE, with no added risk to the patient. The cross validation shows that the method is not dependent on dysfunction and thus has the potential for generalisation beyond pulmonary embolism and septic shock.

Cardiac output estimation using pulmonary mechanics in mechanically ventilated patients.

The application of positive end expiratory pressure (PEEP) in mechanically ventilated (MV) patients with acute respiratory distress syndrome (ARDS) decreases cardiac output (CO). Accurate measurement of CO is highly invasive and is not ideal for all MV critically ill patients. However, the link between the PEEP used in MV, and CO provides an opportunity to assess CO via MV therapy and other existing measurements, creating a CO measure without further invasiveness.This paper examines combining models of diffusion resistance and lung mechanics, to help predict CO changes due to PEEP. The CO estimator uses an initial measurement of pulmonary shunt, and estimations of shunt changes due to PEEP to predict CO at different levels of PEEP. Inputs to the cardiac model are the PV loops from the ventilator, as well as the oxygen saturation values using known respiratory inspired oxygen content. The outputs are estimates of pulmonary shunt and CO changes due to changes in applied PEEP. Data from two published studies are used to assess and initially validate this model.The model shows the effect on oxygenation due to decreased CO and decreased shunt, resulting from increased PEEP. It concludes that there is a trade off on oxygenation parameters. More clinically importantly, the model also examines how the rate of CO drop with increased PEEP can be used as a method to determine optimal PEEP, which may be used to optimise MV therapy with respect to the gas exchange achieved, as well as accounting for the impact on the cardiovascular system and its management.

Monte Carlo analysis of a new model-based method for insulin sensitivity testing.

Insulin resistance (IR), or low insulin sensitivity, is a major risk factor in the pathogenesis of type 2 diabetes and cardiovascular disease. A simple, high resolution assessment of IR would enable earlier diagnosis and more accurate monitoring of intervention effects. Current assessments are either too intensive for clinical settings (Euglycaemic Clamp, IVGTT) or have too low resolution (HOMA, fasting glucose/insulin). Based on high correlation of a model-based measure of insulin sensitivity and the clamp, a novel, clinically useful test protocol is designed with: physiological dosing, short duration (<1 h), simple protocol, low cost and high repeatability. Accuracy and repeatability are assessed with Monte Carlo analysis on a virtual clamp cohort (N=146). Insulin sensitivity as measured by this test has a coefficient of variation (CV) of CV(SI)=4.5% (90% CI: 3.8-5.7%), slightly higher than clamp ISI (CV(ISI)=3.3% (90% CI: 3.0-4.0%)) and significantly lower than HOMA (CV(HOMA)=10.0% (90% CI: 9.1-10.8%)). Correlation to glucose and unit normalised ISI is r=0.98 (90% CI: 0.97-0.98). The proposed protocol is simple, cost effective, repeatable and highly correlated to the gold-standard clamp.

Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care.

Targeted, tight model-based glycemic control in critical care patients that can reduce mortality 18-45% is enabled by prediction of insulin sensitivity, S(I). However, this parameter can vary significantly over a given hour in the critically ill as their condition evolves. A stochastic model of S(I) variability is constructed using data from 165 critical care patients. Given S(I) for an hour, the stochastic model returns the probability density function of S(I) for the next hour. Consequently, the glycemic distribution following a known intervention can be derived, enabling pre-determined likelihoods of the result and more accurate control. Cross validation of the S(I) variability model shows that 86.6% of the blood glucose measurements are within the 0.90 probability interval, and 54.0% are within the interquartile interval. "Virtual Patients" with S(I) behaving to the overall S(I) variability model achieved similar predictive performance in simulated trials (86.8% and 45.7%). Finally, adaptive control method incorporating S(I) variability is shown to produce improved glycemic control in simulated trials compared to current clinical results. The validated stochastic model and methods provide a platform for developing advanced glycemic control methods addressing critical care variability.

Model-based insulin and nutrition administration for tight glycaemic control in critical care.

OBJECTIVE: Present a new model-based tight glycaemic control approach using variable insulin and nutrition administration. BACKGROUND: Hyperglycaemia is prevalent in critical care. Current published protocols use insulin alone to reduce blood glucose levels, require significant added clinical effort, and provide highly variable results. None directly address both the practical clinical difficulties and significant patient variation seen in general critical care, while also providing tight control. METHODS: The approach presented manages both nutritional inputs and exogenous insulin infusions using tables simplified from a model-based, computerised protocol. Unique delivery aspects include bolus insulin delivery for safety and variable enteral nutrition rates. Unique development aspects include the use of simulated virtual patient trials created from retrospective data. The model, protocol development, and first 50 clinical case results are presented. RESULTS: High qualitative correlation to within +/-10% between simulated virtual trials and published clinical results validates the overall approach. Pilot tests covering 7358 patient hours produced an average glucose of 5.9 +/- 1.1 mmol/L. Time in the 4-6.1 mmol/L band was 59%, with 84% in 4.0-7.0 mmol/L, and 92% in 4.0-7.75 mmol/L. The average feed rate was 63% of patient specific goal feed and the average insulin dose was 2.6U/hour. There was one hypoglycaemic measurement of 2.1 mmol/L. No departures from protocol or clinical interventions were required at any time. SUMMARY: Modulating both low dose insulin boluses and nutrition input rates challenges the current practice of using only insulin in larger doses to reduce hyperglycaemic levels. Clinical results show very tight control in safe glycaemic bands. The approach could be readily adopted in any typical ICU.

Measuring facial grimacing for quantifying patient agitation in critical care.

The effective delivery of sedation in critical care relies primarily on an accurate and consistent measure of a patient's agitation level. However, current methods for assessing agitation are subjective and prone to error, often leading to over sedation or cycles between agitation and oversedation. This paper builds on previous work developing agitation sensors based on heart rate and blood pressure variability, and overall whole body motion. In this research, the focus is on real-time measurement of high-resolution facial changes that are observed to occur in agitation. An algorithm is developed that measures the degree of facial grimacing from a single digital camera. The method is demonstrated on simulated patient facial motion to prove the concept. A consistent measure is obtained that is robust to significant random head movement and compares well against visual observation of different levels of grimacing. The method provides a basis for clinical validation.

Transient and steady-state euglycemic clamp validation of a model for glycemic control and insulin sensitivity testing.

BACKGROUND: There is an urgent need for a simple and accurate measure of insulin sensitivity to diagnose insulin resistance in the general population and quantify changes due to clinical intervention. A new physiological control model of glucose and insulin metabolism is validated with the euglycemic-hyperinsulinemic clamp during steady and transient states. METHODS: The data consist of n = 60 (15 lean, 15 overweight, 15 obese, and 15 morbidly obese) euglycemic-hyperinsulinemic clamp trials performed on normoglycemic insulin-resistant individuals. The glucose and insulin model is fitted using an integral-based method. Correlations between clamp-derived insulin sensitivity index (ISI) and the model's insulin sensitivity parameter (SI) are obtained during steady and transient states. Results are compared with log-homeostasis model assessment (HOMA), a widely used fasting surrogate for insulin sensitivity. RESULTS: Correlation between model-based insulin sensitivity, SI, and ISIG (ISI normalized by steady-state glucose) is r = 0.99 (n = 60) at steady state and r = 0.97 at transient state, respectively. Correlations did not significantly change across subgroups, with narrow 95% confidence intervals. Log-HOMA correlations are r=-0.72 to SI and r=-0.71 to ISIG for the overall population but are significantly lower in the subgroups, with wide 95% confidence intervals. CONCLUSIONS: The model-based insulin sensitivity parameter, SI, highly correlates to ISIG in all subgroups, even when only considering a transient state. The high correlation of SI offers the potential for a short, simple yet highly correlated, model-based assessment of insulin sensitivity that is not currently available.

A pilot study of the SPRINT protocol for tight glycemic control in critically Ill patients.

BACKGROUND: Stress-induced hyperglycemia is prevalent in critical care, even in patients with no history of diabetes. Increased counter-regulatory hormone response increases gluconeogenesis and effective insulin resistance, which can be exacerbated by drug therapy. Control of blood glucose levels to the 4.0-6.1 mmol/L range has been shown to reduce mortality and improve clinical outcomes. The Specialized Relative Insulin and Nutrition Tables (SPRINT) protocol is a simple alternative intensive care unit protocol for modulating insulin and nutritional input to gain tight blood glucose control in the 4.0-6.1 mmol/L target band. The look-up tables, implemented in a wheel-based format, are used by nurses to determine glycemic control actions based on hourly or 2-hourly blood glucose measurements and nutrition and insulin administration rates. METHODS: An 11 patient pilot study was conducted comprising 2,152 hours of blood glucose level control using the SPRINT protocol. The patient cohort average Acute Physiology and Chronic Health Evaluation II score was 22, which was higher than previous intensive insulin clinical studies. RESULTS: Overall, 64% of measurements were in the 4.0-6.1 mmol/L band, 89% in the 4.0-7.0 mmol/L band, and 96% of all measurements in the 4.0-7.75 mmol/L band. The average value was 5.8 +/- 0.9 mmol/L. Only 1.4% of all measurements were below 4 mmol/L, with a minimum of 3.2 mmol/L. The maximum value recorded was 11.8 mmol/L. CONCLUSIONS: Control of blood glucose level was achieved using a protocol implemented by the nursing staff without the need for physician intervention or interpretation, where control is defined as maximizing time within a desired band. The results led to a high level of support for the SPRINT protocol among clinical staff and acceptance of the frequent measurement requirement for effective control. The ease-of-use of the protocol resulted in minimal noncompliance by clinical staff.

A simple insulin-nutrition protocol for tight glycemic control in critical illness: development and protocol comparison.

BACKGROUND: Hyperglycemia is prevalent in critical care, and tight control can significantly reduce mortality. However, current protocols have been considered taxing to administer and may require extra staff. In addition, increased insulin resistance and saturation effects limit the level of control possible using insulin alone. Thus, regulating both insulin and exogenous nutritional inputs is required to control blood glucose. METHODS: A robust, easy-to-use protocol ["SPRINT" (Specialized Relative Insulin Nutrition Tables)] that employs both insulin and feed modulation is developed and analyzed using retrospective data from 19 patients with average Acute Physiology and Chronic Health Evaluation II score of 21.8. Results are compared with several published protocols in simulation, and verified in a proof-of-concept trial. RESULTS: In simulation, 61.7% of measurements were in the 75-110 mg/dL band and 83.5% in the 75-140 mg/dL band. Results from the simulation of published protocols agreed with published results. Clinically, for two patients, 64% and 85% of measurements were between 75 and 110 mg/dL during the two proof-of-concept trials. Total enteral feeding was similar to, or exceeded, retrospective data. CONCLUSIONS: Tight control was achieved in simulation using a protocol that is easy to implement in an intensive care unit. Similarly tight control was also maintained during the two proof-of-concept clinical trials. Measurement frequency of 1-2 h is seen to be critical to achieving and maintaining tight control. The overall SPRINT protocol is easy to use for clinical staff and effective in achieving and maintaining normoglycemia in critical illness.

Parameter identification and sedative sensitivity analysis of an agitation-sedation model.

Sedation administration and agitation management are fundamental activities in any intensive care unit. A lack of objective measures of agitation and sedation, as well as poor understanding of the underlying dynamics, contribute to inefficient outcomes and expensive healthcare. Recent models of agitation-sedation dynamics have enhanced understanding of the underlying dynamics and enable development of advanced protocols for semi-automated sedation administration. In this research, the agitation-sedation model parameters are identified using an integral-based fitting method developed in this work. Parameter variance is then analysed over 37 intensive care unit patients. The parameter identification method is shown to be effective and computationally inexpensive, making it suited to real-time clinical control applications. Sedative sensitivity, an important model parameter, is found to be both patient-specific and time-varying. However, while the variation between patients is observed to be as large as a factor 10, the observed variation in time is smaller, and varies slowly over a period of days rather than hours. The high fitted model performance across all patients show that the agitation-sedation model presented captures the fundamental dynamics of the agitation-sedation system. Overall, these results provide additional insight into the system and clinical dynamics of sedation management.

Integral-based filtering of continuous glucose sensor measurements for glycaemic control in critical care.

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the continuous glucose monitoring system (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute 1-h glucose prediction error of 6.20% (range: 4.97-8.06%) with an average standard deviation per patient of 5.22% (range: 3.26-8.55%). Note that all the methods and results are generalizable to similar applications outside of critical care, such as less acute wards and eventually ambulatory individuals. Clinically, the results show one possible computational method for managing the larger errors encountered in emerging continuous blood glucose sensors, thus enabling their more effective use in clinical glucose regulation studies.

Integral-based parameter identification for long-term dynamic verification of a glucose-insulin system model.

Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. This paper introduces a model capable of capturing the essential glucose and insulin kinetics in patients from retrospective data gathered in an intensive care unit (ICU). The model uses two time-varying patient specific parameters for glucose effectiveness and insulin sensitivity. The model is mathematically reformulated in terms of integrals to enable a novel method for identification of patient specific parameters. The method was tested on long-term blood glucose recordings from 17 ICU patients, producing 4% average error, which is within the sensor error. One-hour forward predictions of blood glucose data proved acceptable with an error of 2-11%. All identified parameter values were within reported physiological ranges. The parameter identification method is more accurate and significantly faster computationally than commonly used non-linear, non-convex methods. These results verify the model's ability to capture long-term observed glucose-insulin dynamics in hyperglycemic ICU patients, as well as the fitting method developed. Applications of the model and parameter identification method for automated control of blood glucose and medical decision support are discussed.

Validation of subject-specific cardiovascular system models from porcine measurements.

A previously validated mathematical model of the cardiovascular system (CVS) is made subject-specific using an iterative, proportional gain-based identification method. Prior works utilised a complete set of experimentally measured data that is not clinically typical or applicable. In this paper, parameters are identified using proportional gain-based control and a minimal, clinically available set of measurements. The new method makes use of several intermediary steps through identification of smaller compartmental models of CVS to reduce the number of parameters identified simultaneously and increase the convergence stability of the method. This new, clinically relevant, minimal measurement approach is validated using a porcine model of acute pulmonary embolism (APE). Trials were performed on five pigs, each inserted with three autologous blood clots of decreasing size over a period of four to five hours. All experiments were reviewed and approved by the Ethics Committee of the Medical Faculty at the University of Liege, Belgium. Continuous aortic and pulmonary artery pressures (P(ao), P(pa)) were measured along with left and right ventricle pressure and volume waveforms. Subject-specific CVS models were identified from global end diastolic volume (GEDV), stroke volume (SV), P(ao), and P(pa) measurements, with the mean volumes and maximum pressures of the left and right ventricles used to verify the accuracy of the fitted models. The inputs (GEDV, SV, P(ao), P(pa)) used in the identification process were matched by the CVS model to errors <0.5%. Prediction of the mean ventricular volumes and maximum ventricular pressures not used to fit the model compared experimental measurements to median absolute errors of 4.3% and 4.4%, which are equivalent to the measurement errors of currently used monitoring devices in the ICU (∼5-10%). These results validate the potential for implementing this approach in the intensive care unit.

Dynamic functional residual capacity can be estimated using a stress-strain approach.

BACKGROUND: Acute Respiratory Distress Syndrome (ARDS) results in collapse of alveolar units and loss of lung volume at the end of expiration. Mechanical ventilation is used to treat patients with ARDS or Acute Lung Injury (ALI), with the end objective being to increase the dynamic functional residual capacity (dFRC), and thus increasing overall functional residual capacity (FRC). Simple methods to estimate dFRC at a given positive end expiratory pressure (PEEP) level in patients with ARDS/ALI currently does not exist. Current viable methods are time-consuming and relatively invasive. METHODS: Previous studies have found a constant linear relationship between the global stress and strain in the lung independent of lung condition. This study utilizes the constant stress-strain ratio and an individual patient's volume responsiveness to PEEP to estimate dFRC at any level of PEEP. The estimation model identifies two global parameters to estimate a patient specific dFRC, ß and mß. The parameter ß captures physiological parameters of FRC, lung and respiratory elastance and varies depending on the PEEP level used, and mß is the gradient of ß vs. PEEP. RESULTS: dFRC was estimated at different PEEP values and compared to the measured dFRC using retrospective data from 12 different patients with different levels of lung injury. The median percentage error is 18% (IQR: 6.49) for PEEP=5 cmH2O, 10% (IQR: 9.18) for PEEP=7 cmH2O, 28% (IQR: 12.33) for PEEP=10 cmH2O, 3% (IQR: 2.10) for PEEP=12 cmH2O and 10% (IQR: 9.11) for PEEP=15 cmH2O. The results were further validated using a cross-correlation (N=100,000). Linear regression between the estimated and measured dFRC with a median R² of 0.948 (IQR: 0.915, 0.968; 90% CI: 0.814, 0.984) over the N=100,000 cross-validation tests. CONCLUSIONS: The results suggest that a model based approach to estimating dFRC may be viable in a clinical scenario without any interruption to ventilation and can thus provide an alternative to measuring dFRC by disconnecting the patient from the ventilator or by using advanced ventilators. The overall results provide a means of estimating dFRC at any PEEP levels. Although reasonable clinical accuracy is limited to the linear region of the static PV curve, the model can evaluate the impact of changes in PEEP or other mechanical ventilation settings.

Modelling acute renal failure using blood and breath biomarkers in rats.

This paper compares three methods for estimating renal function, as tested in rats. Acute renal failure (ARF) was induced via a 60-min bilateral renal artery clamp in 8 Sprague-Dawley rats and renal function was monitored for 1 week post-surgery. A two-compartment model was developed for estimating glomerular filtration via a bolus injection of a radio-labelled inulin tracer, and was compared with an estimated creatinine clearance method, modified using the Cockcroft-Gault equation for rats. These two methods were compared with selected ion flow tube-mass spectrometry (SIFT-MS) monitoring of breath analytes. Determination of renal function via SIFT-MS is desirable since results are available non-invasively and in real time. Relative decreases in renal function show very good correlation between all 3 methods (R²=0.84, 0.91 and 0.72 for breath-inulin, inulin-creatinine, and breath-creatinine correlations, respectively), and indicate good promise for fast, non-invasive determination of renal function via breath testing.

Independent cohort cross-validation of the real-time DISTq estimation of insulin sensitivity.

Insulin sensitivity (SI) is useful in the diagnosis, screening and treatment of diabetes. However, most current tests cannot provide an accurate, immediate or real-time estimate. The DISTq method does not require insulin or C-peptide assays like most SI tests, thus enabling real-time, low-cost SI estimation. The method uses a posteriori parameter estimations in the absence of insulin or C-peptide assays to simulate accurate, patient-specific, insulin concentrations that enable SI identification. Mathematical functions for the a posteriori parameter estimates were generated using data from 46 fully sampled DIST tests (glucose, insulin and C-peptide). SI values found using the DISTq from the 46 test pilot cohort and a second independent 218 test cohort correlated R=0.890 and R=0.825, respectively, to the fully sampled (including insulin and C-peptide assays) DIST SI metrics. When the a posteriori insulin estimation functions were derived using the second cohort, correlations for the pilot and second cohorts reduced to 0.765 and 0.818, respectively. These results show accurate SI estimation is possible in the absence of insulin or C-peptide assays using the proposed method. Such estimates may only need to be generated once and then used repeatedly in the future for isolated cohorts. The reduced correlation using the second cohort was due to this cohort's bias towards low SI insulin resistant subjects, limiting the data set's ability to generalise over a wider range. All the correlations remain high enough for the DISTq to be a useful test for a number of clinical applications. The unique real-time results can be generated within minutes of testing as no insulin and C-peptide assays are required and may enable new clinical applications.

A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients.

Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose-insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S(I), the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S(I) only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients (N=42,941 h in total) who received insulin while in the ICU and stayed for ≥ 72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% [IQR 1.18, 6.41%]. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7-12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose-insulin models render this article a mini-review of the state of model-based TGC in critical care.

Clinical detection and monitoring of acute pulmonary embolism: proof of concept of a computer-based method.

BACKGROUND: The diagnostic ability of computer-based methods for cardiovascular system (CVS) monitoring offers significant clinical potential. This research tests the clinical applicability of a newly improved computer-based method for the proof of concept case of tracking changes in important hemodynamic indices due to the influence acute pulmonary embolism (APE). METHODS: Hemodynamic measurements from a porcine model of APE were used to validate the method. Of these measurements, only those that are clinically available or inferable were used in to identify pig-specific computer models of the CVS, including the aortic and pulmonary artery pressure, stroke volume, heart rate, global end diastolic volume, and mitral and tricuspid valve closure times. Changes in the computer-derived parameters were analyzed and compared with experimental metrics and clinical indices to assess the clinical applicability of the technique and its ability to track the disease state. RESULTS: The subject-specific computer models accurately captured the increase in pulmonary resistance (Rpul), the main cardiovascular consequence of APE, in all five pigs trials, which related well (R2 = 0.81) with the experimentally derived pulmonary vascular resistance. An increase in right ventricular contractility was identified, as expected, consistent with known reflex responses to APE. Furthermore, the modeled right ventricular expansion index (the ratio of right to left ventricular end diastolic volumes) closely followed the trends seen in the measured data (R2 = 0.92) used for validation, with sharp increases seen in the metric for the two pigs in a near-death state. These results show that the pig-specific models are capable of tracking disease-dependent changes in pulmonary resistance (afterload), right ventricular contractility (inotropy), and ventricular loading (preload) during induced APE. Continuous, accurate estimation of these fundamental metrics of cardiovascular status can help to assist clinicians with diagnosis, monitoring, and therapy-based decisions in an intensive care environment. Furthermore, because the method only uses measurements already available in the ICU, it can be implemented with no added risk to the patient and little extra cost. CONCLUSIONS: This computer-based monitoring method shows potential for real-time, continuous diagnosis and monitoring of acute CVS dysfunction in critically ill patients.

Subject-specific cardiovascular system model-based identification and diagnosis of septic shock with a minimally invasive data set: animal experiments and proof of concept.

A cardiovascular system (CVS) model and parameter identification method have previously been validated for identifying different cardiac and circulatory dysfunctions in simulation and using porcine models of pulmonary embolism, hypovolemia with PEEP titrations and induced endotoxic shock. However, these studies required both left and right heart catheters to collect the data required for subject-specific monitoring and diagnosis-a maximally invasive data set in a critical care setting although it does occur in practice. Hence, use of this model-based diagnostic would require significant additional invasive sensors for some subjects, which is unacceptable in some, if not all, cases. The main goal of this study is to prove the concept of using only measurements from one side of the heart (right) in a 'minimal' data set to identify an effective patient-specific model that can capture key clinical trends in endotoxic shock. This research extends existing methods to a reduced and minimal data set requiring only a single catheter and reducing the risk of infection and other complications-a very common, typical situation in critical care patients, particularly after cardiac surgery. The extended methods and assumptions that found it are developed and presented in a case study for the patient-specific parameter identification of pig-specific parameters in an animal model of induced endotoxic shock. This case study is used to define the impact of this minimal data set on the quality and accuracy of the model application for monitoring, detecting and diagnosing septic shock. Six anesthetized healthy pigs weighing 20-30 kg received a 0.5 mg kg(-1) endotoxin infusion over a period of 30 min from T0 to T30. For this research, only right heart measurements were obtained. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the experimentally measured data, including measurements not used in the identification process for validation. Importantly, all identified parameter trends match physiologically and clinically and experimentally expected changes, indicating that no diagnostic power is lost. This work represents a further with human subjects validation for this model-based approach to cardiovascular diagnosis and therapy guidance in monitoring endotoxic disease states. The results and methods obtained can be readily extended from this case study to the other animal model results presented previously. Overall, these results provide further support for prospective, proof of concept clinical testing with humans.