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Tight glycemic control (TGC) has emerged as a major research focus in critical care due to its potential to simultaneously reduce both mortality and costs. However, repeating initial successful TGC trials that reduced mortality and other outcomes has proven difficult with more failures than successes. Hence, there has been growing debate over the necessity of TGC, its goals, the risk of severe hypoglycemia, and target cohorts. This paper provides a review of TGC via new analyses of data from several clinical trials, including SPRINT, Glucontrol and a recent NICU study. It thus provides both a review of the problem and major background factors driving it, as well as a novel model-based analysis designed to examine these dynamics from a new perspective. Using these clinical results and analysis, the goal is to develop new insights that shed greater light on the leading factors that make TGC difficult and inconsistent, as well as the requirements they thus impose on the design and implementation of TGC protocols. A model-based analysis of insulin sensitivity using data from three different critical care units, comprising over 75,000h of clinical data, is used to analyse variability in metabolic dynamics using a clinically validated model-based insulin sensitivity metric (S(I)). Variation in S(I) provides a new interpretation and explanation for the variable results seen (across cohorts and studies) in applying TGC. In particular, significant intra- and inter-patient variability in insulin resistance (1/S(I)) is seen be a major confounder that makes TGC difficult over diverse cohorts, yielding variable results over many published studies and protocols. Further factors that exacerbate this variability in glycemic outcome are found to include measurement frequency and whether a protocol is blind to carbohydrate administration.

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 of1% 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.

Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood culture results may take up to 48 h. Insulin sensitivity (S(I)) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity S(I) values were calculated from glycemic control data of 36 patients with sepsis. The hourly S(I) is compared to the hourly sepsis score (ss) for these patients (ss=0-4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S(I) and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S(I) cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining S(I), temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.

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 (R^2=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.

Time-varying elastance of the ventricles is an important metric both clinically and as an input for a previously developed cardiovascular model. However, currently time-varying elastance is not normally available in an Intensive Care Unit (ICU) setting, as it is an invasive and ethically challenging metric to measure. A previous paper developed a method to map less invasive metrics to the driver function, enabling an estimate to be achieved without invasive measurements. This method requires reliable and accurate processing of the aortic and pulmonary artery pressure waveforms to locate the specific points that are required to estimate the driver function. This paper details the method by which these waveforms are processed, using a data set of five pigs induced with pulmonary embolism, and five pigs induced with septic shock (with haemofiltration), adding up to 88 waveforms (for each of aortic and pulmonary artery pressure), and 616 points in total to locate. 98.2% of all points were located to within 1% of their true value, 0.81% were between 1% and 5%, 0.65% were between 5% and 10%, the remaining 0.32% were below 20%.

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.

A model for the cardiovascular and circulatory systems has previously been validated in simulated cardiac and circulatory disease states. It has also been shown to accurately capture the main hemodynamic trends in porcine models of pulmonary embolism and PEEP (positive end-expiratory pressure) titrations at different volemic levels. In this research, the existing model and parameter identification process are used to study the effect of different adrenaline doses in healthy and critically ill patient populations, and to develop a means of predicting the hemodynamic response to adrenaline. The hemodynamic effects on arterial blood pressures and stroke volume (cardiac index) are simulated in the model and adrenaline-specific parameters are identified. The dose dependent changes in these parameters are then related to adrenaline dose using data from studies published in the literature. These relationships are then used to predict the future, patient-specific response to a change in dose or over time periods from 1-12 hours. The results are compared to data from 3 published adrenaline dosing studies comprising a total of 37 data sets. Absolute percentage errors for the identified model are within 10% when re-simulated and compared to clinical data for all cases. All identified parameter trends match clinically expected changes. Absolute percentage errors for the predicted hemodynamic responses (N=15) are also within 10% when re-simulated and compared to clinical data. Clinically accurate prediction of the effect of inotropic circulatory support drugs, such as adrenaline, offers significant potential for this type of model-based application. Overall, this work represents a further clinical, proof of concept, of the underlying fundamental mathematical model, methods and approach, as well as providing a template for using the model in clinical titration of adrenaline in a decision support role in critical care. They are thus a further justification in support of upcoming human clinical trials to validate this model.

Background:Premature neonates often experience hyperglycemia, which has been linked to worsened outcomes. Insulin therapy can assist in controlling blood glucose (BG) levels. However, a reliable, robust control protocol is required to avoid hypoglycemia and to ensure that clinically important nutrition goals are met.Methods:This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state of the neonate. Controller performance was tested and refined in virtual trials on a 25-patient retrospective cohort. The effects of measurement frequency and BG sensor error were evaluated. A stochastic model of insulin sensitivity was used in control to provide a guaranteed maximum 4% risk of BG < 72 mg/dl to protect against hypoglycemia as well as account for patient variability over 1–3 h intervals when determining the intervention. The resulting controller is demonstrated in two 24 h clinical neonatal pilot trials at Christchurch Women's Hospital.Results:Time in the 72–126 mg/dl BG band was increased by 103–161% compared to retrospective clinical control for virtual trials of the controller, with fewer hypoglycemic measurements. Controllers were robust to BG sensor errors. The model-based controller maintained glycemia to a tight target control range and accounted for interpatient variability in patient glycemic response despite using more insulin than the retrospective case, illustrating a further measure of controller robustness. Pilot clinical trials demonstrated initial safety and efficacy of the control method.Conclusions:A controller was developed that made optimum use of the very limited available BG measurements in the neonatal intensive care unit and provided robustness against BG sensor error and longer BG measurement intervals. It used more insulin than typical sliding scale approaches or retrospective hospital control. The potential advantages of a model-based approach demonstrated in simulation were applied to initial clinical trials.

Premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels. However a reliable, robust control protocol is required to avoid hypoglycaemia and to meet nutrition goals. This study presents an adaptive, model-based predictive controller designed to incorporate the unique metabolic state and control parameters of the neonate. Controller performance was tested in virtual trials on a 25 patient retrospective cohort and 24-hour pilot clinical trials. The effects of measurement frequency and BG sensor error were also evaluated. Time in the 4 – 7 mmol/L BG band was increased by 110%–145% compared to retrospective control for that cohort, with fewer hypoglycaemic measurements. Controllers were robust to BG sensor errors.

This paper focuses on clinical results of a model-based cardiac diagnosis and therapy methodology. The method allows for accurate patient specific identification and prediction of a broad range of hemodynamics typical in the critically ill. Extensive porcine experiments have been performed involving drug induced pulmonary embolism and septic shock including hemofiltration therapy. The model-based methods consistently capture all measured cardiac variables, within 5–10% error throughout the experiments. Importantly, all the physiologically expected responses to pulmonary embolism and septic shock are captured and compare well to more directly measured but highly invasive procedures. The cardiovascular system model only requires clinical data that is readily available in the Intensive Care Unit. Therefore, the results demonstrate the potential for longer term developing a cardiac diagnosis and therapeutic guidance system for use in critical care at the bedside.

Many critically ill patients are benefiting from extensive research done in tight glucose control (TGC) within the ICU. But moderate to high levels of hyperglycaemia are still tolerated within high dependency (HDU) and surgical units. The use and benefits of insulin protocols within these units have not yet been addressed in the literature. The management of tight glycaemic control still remains under the influence of ineffective standards characterized by tolerance for hyperglycaemia and a reluctance to use insulin intensively. A validated Glargine and intravenous insulin-glucose pharmacodynamic model are presented. Virtual trial results on 16 stable ICU patients showed that Glargine can provide effective blood glucose management for these long term recovering patients. An initial intravenous injection and higher Glargine dosing is required for the first day to quickly lower elevated blood glucose levels. However, once patient's blood glucose levels are within a desirable range, Glargine alone can provide effective glycaemic management, thus reducing nursing effort. Median blood glucose for the entire cohort when simulated with the combination of Glargine and an intravenous insulin injection is 6.5 with interquartile range of [5.6, 7.5]. The 90% confidence interval is [4.6, 9.7] with no occurrence of hypoglycaemia. This in silico study provides a first virtual trial analysis of the in-hospital transition between intravenous and subcutaneous insulin for TGC.

This paper compares three methods for estimating renal function, tested in rats. Acute Renal Failure (ARF) was induced via a 60-minute 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 renal function via a bolus injection of a radio-labelled inulin tracer, and was compared with the current gold standard plasma creatinine measurement, 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 excellent correlation between methods, and indicate good promise for fast, non-invasive determination of renal function via breath testing.

Tight glycaemic control (TGC) has emerged as a major focus in critical care. However, repeating the initial successful reductions in reducing mortality and other outcomes via TGC has proven very difficult. Hence, there has been growing debate over the necessity of TGC, its goals, safety from hypoglycemia, and target cohorts. This article reviews existing knowledge and results to provide a new interpretation and explanation for the variable results in applying TGC. It then uses a validated metabolic system model to show how the root cause is the intra- and inter- patient variability, which makes TGC difficult over diverse cohorts and thus yields such variable results over many protocols.

Background: Timely diagnosis and treatment of sepsis in critical care require significant clinical effort, experience, and resources. Insulin sensitivity is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to identify insulin sensitivity in real time. Methods: Receiver operating characteristic curves and cutoff insulin sensitivity values for diagnosing sepsis were calculated for model-based insulin sensitivity (SI) and a simpler metric (SSI) that was estimated from glycemic control data of 30 patients with sepsis and can be calculated in real time without use of a computer. Results were compared to the insulin sensitivity profiles of a general intensive care unit population of 113 patients without sepsis and 30 patients with sepsis, comprising a total of 26,453 patient hours. Patients with sepsis were identified as having sepsis based on a sepsis score ( ss) of 3 or higher (ss = 0–4 for increasing severity). Patients with type I or type II diabetes were excluded. Ethics approval for this study was granted by the South Island Regional Ethics Committee. Results: Receiver operating characteristic cutoff values of SI = 8 × 10-5 liter mU -1 min -1 and SSI = 2.8 × 10 -4 liter mU -1 min -1 were determined for ss ≥ 3. The model-based SI fell below this value in 15% of all patient hours. The SI test had a negative predictive value of 99.8%. The test sensitivity was 78% and specificity was 82%. However, the positive predictor value was 2.8%. Slightly lower sensitivity (68.8%) and specificity (81.7%), but equally good negative prediction (99.7%), were obtained for the estimated SSI. Conclusions: Insulin sensitivity provides a negative predictive diagnostic for sepsis. High insulin sensitivity rules out sepsis for the majority of patient hours and may be determined noninvasively in real time from glycemic control protocol data. Low insulin sensitivity is not an effective diagnostic, as it can equally mark the presence of sepsis or other conditions.

OBJECTIVES The goals of this study were to develop (1) a safe and effective protocol for the clinical control of type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements and multiple daily injections with insulin analogues, and (2) an in silico simulation tool of type 1 diabetes to predict long-term glycemic control outcomes of clinical interventions. METHODS The virtual patient method was used to develop a simulation tool for type 1 diabetes using data from a type 1 diabetes patient cohort (n = 40). The tool was used to test the adaptive protocol (AC) and a conventional intensive insulin therapy (CC) against results from a representative control cohort. Optimal and suboptimal basal insulin replacements were evaluated as a function of SMBG frequency in conjunction with the (AC and CC) prandial control protocols. RESULTS In long-term glycemic control, the AC protocol significantly decreased hemoglobin A1c in conditions of suboptimal basal insulin replacement for SMBG frequencies > or = 6/day, and reduced the occurrence of mild and severe hypoglycemia by 86-100% over controls, over all SMBG frequencies in conditions of optimal basal insulin. CONCLUSIONS A simulation tool to predict long-term glycemic control outcomes from clinical interventions has been developed to test a novel, adaptive control protocol for type 1 diabetes. The protocol is effective and safe compared to conventional intensive insulin therapy and controls. As fear of hypoglycemia is a large psychological barrier to glycemic control, the AC protocol may represent the next evolution of intensive insulin therapy to deliver increased glycemic control with increased safety. Further clinical or experimental validation is needed to fully prove the concept.

Glycaemic control can reduce mortality in intensive care by 45%. A model-based control algorithm utilising insulin and nutritional glucose inputs is presented. Simulated long-term gluco-regulatory trials with the virtual-patient method using retrospective ICU patient data (n=19) validate the approach. Two short-term proof-of-concept clinical trials test glycaemic predictive capability and the ability to adapt to patient condition. In simulation, a 312% increase in time spent in the 4–6 mmol/l euglycaemic band compared to retrospective patient data is recorded while feeding 39% greater nutrition. In the clinical trials, mean target error was 8.7% with hourly prediction horizon. About 61% of targets were achieved within ±5%, and only two targets had errors >20%, occurring during rapid deterioration in patient condition. Overall, the protocol demonstrated effective glycaemic control across the selected cohort in simulation and in highly dynamic patient conditions observed in initial proof-of-concept clinical trials.

Cardiovascular disturbances are difficult to diagnose and treat because of the large range of possible underlying dysfunctions combined with regulatory reflex mechanisms that can result in conflicting clinical data. A cardiovascular system (CVS) model and patient specific parameter identification method could better aggregate the clinical data into a more direct and simpler form for clinicians. A previously developed model and parameter identification method is improved to accurately capture physiological response to septic shock under continuous hemofiltration, further confirming the potential for using this model-based approach in critical care. Clinical data is matched with mean absolute errors less than 8% and the optimized parameters closely follow a previous study using significantly more invasive procedures and measurements.

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 modelbased, 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.

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.

A minimal cardiac model has been shown to accurately capture a wide range of cardiovascular system dynamics commonly seen in the intensive care unit (ICU). However, standard parameter identification methods for this model are highly non-linear and non-convex, hindering real-time clinical application. An integral-based identification method that transforms the problem into a linear, convex problem, has been previously developed, but was only applied on continuous simulated data with random noise. This paper extends the method to handle discrete sets of clinical data, unmodelled dynamics, a significantly reduced data set theta requires only the minimum and maximum values of the pressure in the aorta, pulmonary artery and the volumes in the ventricles. The importance of integrals in the formulation for noise reduction is illustrated by demonstrating instability in the identification using simple derivative-based approaches. The cardiovascular system (CVS) model and parameter identification method are then clinically validated on porcine data for pulmonary embolism. Errors for the identified model are within 10% when re-simulated and compared to clinical data. All identified parameter trends match clinically expected changes. This work represents the first clinical validation of these models, methods and approach to cardiovascular diagnosis in critical care.

Background: Hyperglycemia is prevalent in critical care and tight control can save lives. Current ad-hoc clinical protocols require significant clinical effort and produce highly variable results. Model-based methods can provide tight, patient specific control, while addressing practical clinical difficulties and dynamic patient evolution. However, tight control remains elusive as there is not enough understanding of the relationship between control performance and clinical outcome. Methods: The general problem and performance criteria are defined. The clinical studies performed to date using both ad-hoc titration and model-based methods are reviewed. Studies reporting mortality outcome are analysed in terms of standardized mortality ratio (SMR) and a 95 th percentile (±2s) standard error (SE95%) to enable better comparison across cohorts. Results: Model-based control trials lower blood glucose into a 72-110 mg/dL band within 10 hours, have target accuracy over 90%, produce fewer hypoglycemic episodes, and require no additional clinical intervention. Plotting SMR versus SE95% shows potentially high correlation (r=0.84) between ICU mortality and tightness of control. Summary: Model-based methods provide tighter, more adaptable one method fits all solutions, using methods that enable patientspecific modeling and control. Correlation between tightness of control and clinical outcome suggests that performance metrics, such as time in a relevant glycemic band, may provide better guidelines. Overall, compared to the current one size fits all sliding scale and ad-hoc regimens, patient-specific pharmacodynamic and pharmacokinetic model-based, or one method fits all control, utilizing computational and emerging sensor technologies, offers improved treatment and better potential outcomes when treating hyperglycemia in the highly dynamic critically ill patient.

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.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.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.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.

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 generalisable 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.

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.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.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.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.

A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). However, identifying patient specific parameters with the limited measurements often available, hinders the clinical application of the model for diagnosis and therapy selection. This paper presents an integral-based parameter identification method for fast, accurate identification of patient specific parameters using limited measured data. The integral method turns a previously non-linear and non-convex optimization problem into a linear and convex identification problem. The model includes ventricular interaction and physiological valve dynamics. A healthy human state and four disease states, valvular stenosis, pulmonary embolism, cardiogenic shock and septic shock are used to test the method. Parameters for the healthy and disease states are accurately identified using only discretized flows into and out of the two cardiac chambers, the minimum and maximum volumes of the left and right ventricles, and the pressure waveforms through the aorta and pulmonary artery. These input values can be readily obtained non-invasively using echo-cardiography and ultra-sound, or invasively via catheters that are often used in Intensive Care. The method enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 min) to within a mean value of 4-10% in the presence of 5-15% uniformly distributed measurement noise. The specific changes made to simulate each disease state are correctly identified in each case to within 10% without false identification of any other patient specific parameters. Clinically, the resulting patient specific model can then be used to assist medical staff in understanding, diagnosis and treatment selection.

Hyperglycaemia is prevalent in critical care, as patients experience stress-induced hyperglycaemia, even with no history of diabetes. Hyperglycaemia in critical care is not largely benign, as once thought, and it has a deleterious effect on outcome. Recent studies have shown that tight glucose regulation to average levels from 6.1–7.75 mmol/L can reduce mortality 25–43%, while also significantly reducing other negative clinical outcomes. However, clinical results are highly variable and there is little agreement on what levels of performance can be achieved and how to achieve them. A typical clinical solution is to use ad-hoc protocols based primarily on experience, where large amounts of insulin, up to 50 U/h, are titrated against glucose measurements variably taken every 1–4 h. When combined with the unpredictable and sudden metabolic changes that characterise this aspect of critical illness and/or clinical changes in nutritional support, this approach results in highly variable blood glucose levels. The overall result is sustained periods of hyper- or hypo- glycaemia, characterised by oscillations between these states, which can adversely affect clinical outcomes and mortality. The situation is exacerbated by exogenous nutritional support regimes with high dextrose content. Hence, there is an emerging, strong need for the more rigorous analysis and methods that model-based control methods bring to this type of problem. This paper reviews the state of the clinical and model-based control systems approach to the problem of managing hyperglycaemia in critical care, emphasising emerging methods and results. The overall goal is to present the fundamental problem and associated science and technologies involved. Thus, it is less a discussion of specific advantageous approaches than a presentation of the different factors that impact the problem and the different approaches taken to address them in the limited clinical engineering research done to date. These discussions are presented in the context of current and emerging clinical studies, both model-based and empirical protocol driven. Analogies to the Type 1 diabetes mellitus control problem are also noted where relevant and significant. Hence, it is more overview than specific analysis, where the overall conclusion is that there are many opportunities and unanswered questions remaining on which model-based control research can have significant clinical impact.

Modelling can enhance the diagnosis and control of metabolic disorders. Clinical effectiveness demands physiological accuracy, patient specificity and identification with limited data. A two-compartment insulin kinetics model and associated insulin-glucose pharmacodynamics are presented. Similarities with C-peptide kinetics are used to simplify parameter identification. Critical patient specific parameters are identified using a novel convex, integral-based method. The model and methods are validated within physiological ranges using euglycaemic clamp (N=146) and IVGTT data. The mean absolute errors in the resulting glucose and insulin profiles are eG = 5.9% ± 6.6% SD and eI = 6.2% ± 6.4% SD for the clamps and area under glucose and insulin profiles deviated and during IVGTT.

Critically ill patients are often hyperglycemic and extremely diverse in their dynamics. Consequently, fixed protocols and sliding scales can result in error and poor control. Tight glucose control has been shown to significantly reduce mortality in critical care. An improved physiological system model of the glucose-insulin dynamics of a critical care patient is used to develop an adaptive tight glucose control protocol that accounts for variable patient dynamics, and is verified in limited clinical trials.A physiologically based two-compartment system model that accounts for time-varying insulin sensitivity, time-varying endogenous glucose removal, and two saturation kinetics mechanisms is developed. A bolus-based adaptive control protocol is developed that monitors the physiological status of a critically ill patient, enabling tight glycemic regulation to preset glycemic targets. The model and protocol are verified in three, 5-h preliminary proof-of-concept clinical trials. Ethics approval was granted by the Canterbury Ethics Committee (Christchurch, New Zealand).Preset glycemic targets are achieved with an average absolute error of 9%, with 75% of all targets achieved within the 7% measurement error. Absolute errors greater than 7% ranged from 17% to 21%.Tight stepwise control was exhibited in all cases, and the adaptive system was able to match the model and observed patient dynamics. Most errors are associated with external perturbations such as drug therapies, or mismodeled parameters that can be easily adjusted with longer trials and/or more data per hour. The overall result is targeted stepwise tight glycemic regulation using insulin boluses.

Close control of blood glucose levels significantly reduces vascular complications in Type 1 and Type 2 diabetic individuals. Heavy derivative controllers using the data density available from emerging biosensors are developed to provide tight, optimal control of elevated blood glucose levels, while robustly handling variation in patient response. A two-compartment glucose regulatory system model is developed for intravenous infusion from physiologically verified subcutaneous infusion models enabling a proof-of-concept clinical trial at the Christchurch Hospital Department of Intensive Care Medicine. This clinical trial is the first of its kind to test a high sample rate feedback control algorithm for tight glucose regulation. The clinical trial results show tight control with reductions of 79-89% in blood glucose excursions for an oral glucose tolerance test. Experimental performance is very similar to modelled behaviour. Results include a clear need for an additional accumulator dynamic for insulin behaviour in transport to the blood and strong correlation of 10% or less between modelled insulin infused and the amounts used in clinical trials. Finally, the heavy derivative PD control approach is seen to be able to bring blood glucose levels below the (elevated) basal level, showing the potential for truly tight control.

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.

Located between the left atrium and the left ventricle, the mitral valve controls flow between these two cardiac chambers. Mitral valve dysfunction is a major cause of cardiac dysfunction and its dynamics are little known. A simple non-linear rotational spring model is developed and implemented to capture the dynamics of the mitral valve. A measured pressure difference curve was used as the input into the model, which represents an applied torque to the anatomical valve chords. A range of mechanical model hysteresis states were investigated to find a model that best matches reported animal data of chord movement during a heartbeat. The study is limited by the use of one dataset found in the literature due to the highly invasive nature of getting this data. However, results clearly highlight fundamental physiological issues, such as the damping and chord stiffness changing within one cardiac cycle, that would be directly represented in any mitral valve model and affect behaviour in dysfunction. Very good correlation was achieved between modeled and experimental valve angle with 1-10% absolute error in the best case, indicating good promise for future simulation of cardiac valvular dysfunction, such as mitral regurgitation or stenosis. In particular, the model provides a pathway to capturing these dysfunctions in terms of modeled stiffness or elastance that can be directly related to anatomical, structural defects and dysfunction.

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 errors0.5%. 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.

Digital Image Elasto Tomography (DIET) is a non-invasive breast cancer screening technology that images the surface motion of a breast under harmonic mechanical actuation. A new approach capturing the dynamics and characteristics of tumor behavior is presented. A simple mechanical model of the breast is used to identify a transfer function relating the input harmonic actuation to the output surface displacements using imaging data of a silicone phantom. Areas of higher stiffness cause significant changes of damping and resonant frequencies as seen in the resulting Bode plots. A case study on a healthy and tumor silicone breast phantom shows the potential for this model-based method to clearly distinguish cancerous and healthy tissue as well as correctly predicting the tumor position.

The aim of this research is to test the ability of a model-based method to track disease-dependent hemodynamic changes in sepsis. Thus, subject-specific models of the cardiovascular system (CVS) are identified using measurements from a porcine model of septic shock with hemofiltration [1].

Background:Insulin resistance is a significant risk factor in the pathogenesis of type 2 diabetes. This article presents pilot study results of the dynamic insulin sensitivity and secretion test (DISST), a high-resolution, low-intensity test to diagnose insulin sensitivity (IS) and characterize pancreatic insulin secretion in response to a (small) glucose challenge. This pilot study examines the effect of glucose and insulin dose on the DISST, and tests its repeatability.Methods:DISST tests were performed on 16 subjects randomly allocated to low (5 g glucose, 0.5 U insulin), medium (10 g glucose, 1 U insulin) and high dose (20 g glucose, 2 U insulin) protocols. Two or three tests were performed on each subject a few days apart.Results:Average variability in IS between low and medium dose was 10.3% ( p = .50) and between medium and high dose 6.0% ( p = .87). Geometric mean variability between tests was 6.0% (multiplicative standard deviation (MSD) 4.9%). Geometric mean variability in first phase endogenous insulin response was 6.8% (MSD 2.2%). Results were most consistent in subjects with low IS.Conclusions:These findings suggest that DISST may be an easily performed dynamic test to quantify IS with high resolution, especially among those with reduced IS.

Digital Image-based Elasto Tomography (DIET) is a non-invasive breast cancer screening modality that induces vibrations into a breast and images its surface motion with digital cameras. Disturbances in the motion are caused by areas of higher stiffness within the breast, potentially cancerous tumors. A concept is presented to detect the angular location of a tumor by analyzing the phase delay of the vibrations on the surface. The approach is verified experimentally on silicone phantom breasts with stiffer inclusions ranging from 0–32 mm. A strong signal differentiating healthy and cancerous phantoms can be seen at the second modal frequency of the breast, clearly detecting a 10 mm tumor. This approach offers great potential for this low cost and accessible breast cancer screening, as an adjunct to existing modalities.

Aims and Background: Model-based insulin sensitivity testing via the intravenous glucose tolerance test (IVGTT) or similar is clinically very intensive due to the need for frequent sampling to accurately capture the dynamics of insulin secretion and clearance. The goal of this study was to significantly reduce the number of samples required in intravenous glucose tolerance test protocols to accurately identify C-peptide and insulin secretion characteristics. Methods: Frequently sampled IVGTT data from 12 subjects [5 normal glucose-tolerant (NGT) and 7 type 2 diabetes mellitus (T2DM)] were analyzed to calculate insulin and C-peptide secretion using a well-accepted C-peptide model. Samples were reduced in a series of steps based on the critical IVGTT profile points required for the accurate estimation of C-peptide secretion. The full data set of 23 measurements was reduced to sets with six or four measurements. The peak secretion rate and total secreted C-peptide during 10 and 20 minutes postglucose input and during the total test time were calculated. Results were compared to those from the full data set using the Wilcoxon rank sum to assess any differences. Results: In each case, the calculated secretion metrics were largely unchanged, within expected assay variation, and not significantly different from results obtained using the full 23 measurement data set ( P < 0.05). Conclusions: Peak and total C-peptide and insulin secretory characteristics can be estimated accurately in an IVGTT from as few as four systematically chosen samples, providing an opportunity to minimize sampling, cost, and burden.

Estimation of ventricular contractility is clinically important in diagnosing cardiac dysfunction in the critically ill. However, experimental assessment of indexes of ventricular contractility, such as the end-systolic pressure-volume relationship requires a highly invasive maneuver. This research describes the use of a previously validated cardiovascular system (CVS) model and identification process to evaluate the right-ventricle contractility as well as the right ventricular arterial coupling in septic shock. The results show good agreement with the gold-standard experimental assessment (conductance catheter method), and offer the potential to develop a model-based sensor to monitor the coupling in clinical realtime.

An accurate test for insulin resistance can delay or prevent the development of Type 2 diabetes and its complications. The current gold standard test, CLAMP, is too labor intensive to be used in general practice. A recently developed dynamic insulin sensitivity test, DIST, uses a glucose-insulin-C-peptide model to calculate model-based insulin sensitivity, SI. Preliminary results show good correlation to CLAMP. However both CLAMP and DIST ignore saturation in insulin-mediated glucose removal. This study uses the data from 17 patients who underwent multiple DISTs to investigate interstitial insulin action and its influence on modeled insulin sensitivity. The critical parameters influencing interstitial insulin action are saturation in insulin receptor binding, αG, and plasma-interstitial diffusion rate, nI. Very low values of αG and very low values of nI produced the most intra-patient variability in SI. Repeatability in SI is enhanced with modeled insulin receptor saturation. Future parameter study on subjects with varying degree of insulin resistance may provide a better understanding of different contributing factors of insulin resistance.

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiologically accurate "open on pressure, close on flow" law. Thus, it does not consider the real time scale of the valve aperture dynamics, and therefore does not fully capture valve dysfunction, particularly where the dysfunction involves partial closure. This research describes a new closed-loop CVS model, including a description of the progressive aperture of the mitral valve, and is valid over the full cardiac cycle. This new model is solved for a healthy and diseased mitral valve.

The time varying elastance of the left and right ventricles, also called the driver functions, are the inputs to a previously developed cardiovascular model. However these functions cannot normally be measured directly in an Intensive Care Unit (ICU), as they require both ventricle volume and pressure waveforms. This paper develops a method to construct both driver functions from measurements which are more commonly available in an ICU. Specifically the aortic pressure and pulmonary artery pressure waveforms. The information used from the pulmonary artery pressure can be found from the central venous pressure and ECG, however these were not available for this study. This paper also details a method to locate specific points of interest in the aortic and pulmonary artery pressures, such as the dicrotic notch. The driver functions that were constructed with a median error of 1.32% and 2.54% for the left and right respectively, with a 90th percentile of 3.67% and 9.43% respectively, and tracked all the physiologically meaningful points on the driver function, such as the start and end of ejection and the maximum ascending gradient.

A model of the cardiovascular system (CVS) has previously been validated in simulated cardiac and circulatory disease states. In these studies, a lumped parameter model representing the hemodynamics of the body is personalised to each individual. In this research, the effects of pulmonary embolism (PE) in porcine trials are investigated using the existing model and an improved parameter identification method. The new method requires only a minimal set of measurements that are available in a typical intensive care unit (ICU) including blood pressure, cardiac output and electrocardiogram. In this paper, the CVS model is used to track temporal changes in disease dependent parameters such as pulmonary resistance to follow the progression of PE during the trials. The patient-specific models correctly identified all the major physiological trends associated with PE and the model outputs closely matched the measured data. In particular, matching of the modeled to measured left and right ventricle pressures and volumes, which were not used in the parameter identification process, provide verification of the models accuracy. These results suggest the potential for implementation in an ICU setting.

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 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 non-invasive CO measure. This paper examines combining models of diffusion resistance with 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 output is 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 looks at how the rate of CO drop with increased PEEP may also be used as a method to determine optimal PEEP, and optimise MV therapy with respect to the gas exchange achieved, while also accounting for its impact on cardiovascular system management.

The cardiac muscle activation or driver function, is a major determinant of cardiovascular dynamics, and is often approximated by the ratio of the left ventricle pressure to the left ventricle volume. In an intensive care unit, the left ventricle pressure is usually never measured, and the left ventricle volume is only measured occasionally by echocardiography, so is not available real-time. This paper develops a method for identifying the driver function based on correlates with geometrical features in the aortic pressure waveform. The method is included in an overall cardiovascular modelling approach, and is clinically validated on a porcine model of pulmonary embolism. For validation a comparison is done between the optimized parameters for a baseline model, which uses the direct measurements of the left ventricle pressure and volume, and the optimized parameters from the approximated driver function. The parameters do not significantly change between the two approaches thus showing that the patient specific approach to identifying the driver function is valid, and has potential clinically.

Extremely premature neonates often experience hyperglycaemia, which has been linked to increased mortality and worsened outcomes. Insulin therapy can assist in controlling blood glucose levels and promoting needed growth. This study presents the development of a model-based stochastic targeted controller designed to adapt insulin infusion rates to match the unique and changing metabolic state and control parameters of the neonate. Long-term usage of targeted BG control requires successfully forecasting variations in neonatal metabolic state, accounting for differences in clinical practices between units, and demonstrating robustness to errors that can occur in everyday clinical usage. Simulation studies were used to evaluate controller ability to target several common BG ranges and evaluate controller sensitivity to missed BG measurements and delays in control interventions on a virtual patient cohort of 25 infants developed from retrospective data. Initial clinical pilot trials indicated model performance matched expected performance from simulations. Stochastic targeted glucose control developed using validated patient-specific virtual trials can yield effective protocols for this cohort. Long-term trials show fundamental success, however clinical interface design appears as a critical factor to ensuring good compliance and thus good control.

Objective: The goal of this study was to develop a unified physiological subcutaneous (SC) insulin absorption model for computer simulation in a clinical diabetes decision support role. The model must model the plasma insulin appearance of a wide range of current insulins, especially monomer insulin and insulin glargine, utilizing common chemical states and transport rates, where appropriate. Methods: A compartmental model was developed with 13 patient-specific model parameters covering six diverse insulin types [rapid-acting, regular, neutral protamine Hagedorn (NPH), lente, ultralente, and glargine insulin]. Model parameters were identified using 37 sets of mean plasma insulin time-course data from an extensive literature review via nonlinear optimization methods. Results: All fitted parameters have a coefficient of variation Conclusion: A model is presented to describe SC injected insulin appearance in plasma in a diabetes decision support role. Clinically current insulin types (monomeric insulin, regular insulin, NPH, insulin, and glargine) and older insulin types (lente and ultralente) are included in a unified framework that accounts for nonlinear concentration and dose dependency. Future work requires clinical validation using published pharmacokinetic studies.

The goal of this study was to develop a system model of type 1 diabetes for the purpose of in silico simulation for the prediction of long-term glycemic control outcomes.The system model was created and identified on a physiological cohort of virtual type 1 diabetes patients (n = 40). Integral-based identification was used to develop (n = 40) insulin sensitivity profiles.The n = 40 insulin sensitivity profiles provide a driving input for virtual patient trials using the models developed. The identified models have a median (90% range) absolute percentage error of 1.33% (0.08-7.20%). The median (90% range) absolute error was 0.12 mmol/liter (0.01-0.56 mmol/liter). The model and integral-based identification of SI captured all patient dynamics with low error, which would lead to more physiological behavior simulation.A simulation tool incorporating n = 40 virtual patient data sets to predict long-term glycemic control outcomes from clinical interventions was developed based on a physiological type 1 diabetes metabolic system model. The overall goal is to utilize this model and insulin sensitivity profiles to develop and optimize self-monitoring blood glucose and multiple daily injection therapy.

We use a previously validated cardiovascular system (CVS) model and parameter identification method to identify the pig-specific parameters during induced endotoxic shock. Six anesthetized healthy pigs weighing 20-30 kg received a 0.5- mg/kg endotoxin infusion over a period of 30 mins from T0 to T30. Only right heart measurements were obtained and thus significantly less data was available for the model parameter identification compared to previous studies. Errors for the identified model are within 8% when the model is identified from data, re-simulated and then compared to the clinically measured data. All identified parameter trends match physiologically expected changes. This work represents a further clinical validation for this model-based approach to cardiovascular diagnosis and therapy guidance in monitoring endotoxic disease states.

A method for improving the model-based diagnostic capability of an existing and clinically validated cardiovascular system model is developed. The improvement is based on the addition of an ECG to obtain diastolic and systolic timing. This extra data constrains the model generated left and right ventricular volume waveforms, and is shown to remove the need for invasive maximum and minimum volume measurements. The model and methods are clinically validated on a porcine model of pulmonary embolism. All errors in the matching of the clinical data are within 8% including the left ventricular pressure which was not used in the parameter identification procedure. When the maximum and minimum volumes were removed from the porcine data set, the identified parameters changed by at most 17% with the majority of errors less than 10%. These results show the clinical potential of adding an ECG to reduce the data set and improve model-based diagnosis in an Intensive Care Unit.

A majority of patients admitted to the Intensive Care Unit (ICU) require some form of respiratory support. In the case of Acute Respiratory Distress Syndrome (ARDS), the patient often requires full intervention from a mechanical ventilator. ARDS is also associated with mortality rate as high as 70%. Despite many recent studies on ventilator treatment of the disease, there are no well established methods to determine the optimal Positive End-Expiratory Pressure (PEEP) or other critical ventilator settings for individual patients. A model of fundamental lung mechanics is developed based on capturing the recruitment status of lung units. The main objective of this research is to develop a minimal model that is clinically effective in determining PEEP. The model was identified for a variety of different ventilator settings using clinical data. The fitting error was between 0.1% and 4% over the inflation limb and between 0.3% and 13% over the deflation limb at different PEEP settings. The model produces good correlation with clinical data, and is clinically applicable due to the minimal number of patient specific parameters to identify. The ability to use this identified patient specific model to optimize ventilator management is demonstrated by its ability to predict the patient specific response of PEEP changes before clinically applying them. Predictions of recruited lung volume change with change in PEEP have a median absolute error of 1.87% (IQR: 0.93-4.80%; 90% CI: 0.16-11.98%) for inflation and a median of 5.76% (IQR: 2.71-10.50%; 90% CI: 0.43-17.04%) for deflation, across all data sets and PEEP values (N=34predictions). This minimal model thus provides a clinically useful and relatively simple platform for continuous patient specific monitoring of lung unit recruitment for a patient.