Integral-based identification of patient specific parameters for a minimal cardiac model

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 two disease states, Valvular Stenosis and Pulmonary Embolism, 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 minutes) to within a mean value of 4 – 8% 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 5% 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

Computationally efficient velocity profile solutions for cardiac haemodynamics

DOI: 10.1109/IEMBS.2004.1403316This paper reformulates the non-linear differential equations associated with time varying resistance in minimal cardio-vascular system models into a system of linear equations with an analytical solution. The importance of including time varying resistance is shown for a single chamber model where there is a 17.5% difference in cardiac output when compared with a constant resistance model. However, the increased complexity has significant extra computational cost. This new formulation provides a significant computational saving of 15x over the previous method. This improvement enables more physiological accuracy with minimal cost in computational time. As a result, the model can be used in clinical situations to aid diagnosis and therapy selection without compromising on physiological accuracy

Diagnosing cardiac disease states using a minimal cardiovascular model

Cardiovascular disease states are difficult to diagnose due to a variety of underlying dysfunctions combined with reflex mechanisms. To provide more consistent care a cardiovascular system model is combined with an efficient patient-specific parameter identification method. The goal is to identify the patient’s condition and to predict the future patient-specific reaction, making this approach a potential means for model-based guided therapy. The model and parameter-identification method are validated using clinical haemodynamic data measured during drug induced porcine pulmonary embolism experiments (N=6) and PEEP titration experiments (N=6). Identified model parameters are correlated to create predictive measures of haemodynamic changes to clinical therapy or patient condition. Prediction is tested for observed changes in arterial pressure (AP), pulmonary arterial pressure (PAP) and stroke volume (SV) as caused by a clinical change in PEEP. The parameter-identification method tracked pulmonary embolism in porcine data from an initial healthy to the disease state. The full range of haemodynamic responses was captured with mean errors of 4.1% in the pressures and 3.1% in the volumes. Pulmonary resistance increased significantly with the onset of embolism, as expected, with the percentage increase ranging from 89.98% to 261.44% of the initial state. Changes in AP, PAP and SV due to an increase in PEEP were predicted with a mean absolute percentage error less than 10% for 6 data sets. These results provide a first clinical validation of this model-based diagnostic therapeutic decision support approach to haemodynamic management

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

Invited. Available online 9 June 2010.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 cm H2O, 10% (IQR: 9.18) for PEEP = 7 cm H2O, 28% (IQR: 12.33) for PEEP = 10 cm H2O, 3% (IQR: 2.10) for PEEP = 12 cm H2O and 10% (IQR: 9.11) for PEEP = 15 cm H2O. 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

Glycemic Control Protocol Comparison using Virtual Trials

DTM2011 handbook/programme is given in files and also available as a hard copyBackground: Several accurate glycemic control (AGC) protocols for critical care patients exist but making comparisons is very hard. Objective: This study uses clinically validated virtual patient methods to compare safety and performance for several published AGC protocols. Method: Clinically validated virtual trials were run on 371 patients (39,481 hours, 26,646 measurements) created from the SPRINT AGC cohort. For protocols that do not modulate feed rates enteral nutrition was held at 100% of ACCP goal (25kcal/kg/day) when the patients were clinically fed, and parenteral nutrition rates were matched to clinical data. Performance was defined as %BG within glycemic bands and BG measurement frequency. Safety was defined as the incidence of severe (number patients with BG<40mg/dL) and moderate (%BG<72mg/dL) hypoglycemia. Clinical data from SPRINT is also compared. Results: Clinical SPRINT performance data matched re-simulated SPRINT with 86% vs. 86% BG in 80-145mg/dL, 2.00% vs. 2.07% BG above 180mg/dL and 7.83% vs. 7.29% BG below 72mg/dL, with 14 measurements (over 8 patients) of BG<40mg/dL. Yale results were 83.5%, 3.20%, 5.18%, with 6 severe hypoglycemic patients, using 37,961 measurements (23.0/day). Glucontrol had 75.2%, 3.70%, 9.45%, 52 cases and 26,199 measurements (15.8/day). Braithwaite had 84.2%, 3.00%, 4.22%, 19 cases and 24,396 measurements (14.8/day). The STAR (Stochastic TARgeted) model-based method had 90.6%, 1.67%, 1.33%, 5 cases and 20,591 measurements (12.3/day). Conclusions: Virtual trials provided an effective comparison across protocols with different target bands/values and different clinical cohorts. The model-based STAR protocol provided the best management of patient variability yielding the best performance and safety

A Fast and Accurate Diagnostic Test for Severe Sepsis Using Kernel Classifiers

Severe 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 gold standard blood culture test results may return in up to 48 hours. Insulin sensitivity (SI) is known to decrease with worsening condition and inflammatory response, and could thus be used to aid clinical treatment decisions. Some glycemic control protocols are able to accurately identify SI in real-time. A biomarker for severe sepsis was developed from retrospective SI and concurrent temperature, heart rate, respiratory rate, blood pressure, and SIRS score from 36 adult patients with sepsis. Patients were identified as having sepsis based on a clinically validated sepsis score (ss) of 2 or higher (ss = 0–4 for increasing severity). Kernel density estimates were used for the development of joint probability density profiles for ss = 2 and ss < 2 data hours (213 and 5858 respectively of 6071 total hours) and for classification. From the receiver operator characteristic (ROC) curve, the optimal probability cutoff values for classification were determined for in-sample and out-of-sample estimates. A biomarker including concurrent insulin sensitivity and clinical data for the diagnosis of severe sepsis (ss = 2) achieves 69–94% sensitivity, 75–94% specificity, 0.78–0.99 AUC, 3–17 LHR+, 0.06–0.4 LHR-, 9–38% PPV, 99–100% NPV, and a diagnostic odds ratio of 7–260 for optimal probability cutoff values of 0.32 and 0.27 for in-sample and out-of-sample data, respectively. The overall result lies between these minimum and maximum error bounds. Thus, the clinical biomarker shows good to high accuracy and may provide useful information as a real-time diagnostic test for severe sepsis

Diabetic Retinopathy Screening Using Computer Vision

6-pagesDiabetic Retinopathy (DR) is one of the main causes of blindness and visual impairment in developed countries, stemming solely from diabetes mellitus. Current screening methods using fundus images rely on the experience of the operator as they are manually examined. Automated methods based on neural networks and other approaches have not provided sensitivity or specificity above 85%. This work presents a computer vision based method that directly identifies hard exudates and dot haemorrhages (DH) from 100 digital fundus images from a graded database of images using standard computer vision techniques, and clinical observation and knowledge. Sensitivity and specificity in diagnosis are 95-100% in both cases. Positive and negative prediction values (PPV, NPV) were 95-100% for both cases. The overall method is general, computationally efficient and suitable for further clinical trials to test both accuracy and the ability to the track DR status over time

Prediction Validation of Two Glycaemic Control Models in Critical Care

Invited paperMetabolic models can substantially improve control of hyperglycaemia in critically ill patients. Control efficacy depends on how accurately a model-based system is able to predict future blood glucose (BG) concentrations after a glycaemic control intervention. This research compares two metabolic models in terms of their predictive power. 30 minutes to 10 hour forward predictions are made using the Glucosafe model (GS) and a clinically tested model (CC) from Christchurch in a retrospective study of 11 hyperglycemic patients, 6 from New Zealand and 5 from Denmark. Median and ranges of prediction errors are similar for predictions up to 360 minutes. Both models make better predictions on the Danish patients. At long prediction times of more than 5 hours, GS predictions tend to be more accurate in the cohort from New Zealand whereas the CC model tends to predict better in the cohort from Denmark. However, differences in root mean square (RMS) of prediction errors are not greater than 4–5% in both cohorts. For both models, outlying prediction errors are dominated by single patients, particularly type 1 diabetic patients. GS predicted BG values are generally higher compared to CC predicted values. As expected, the RMS prediction error increases with prediction interval for both models and cohorts. Results show the potential of both models for use in prospective clinical trials with longer than 120 min sampling intervals, though predictive power is probably related to the type of cohort in terms of admission type, degree of illness and glycaemic stability

Glucose control positively influences patient outcome: a retrospective study

The goal of this research is to demonstrate that well-regulated glycemia is beneficial to patient outcome, regardless of how it is achieved

Long term verification of glucose-insulin regulatory system model dynamics

doi: 10.1109/IEMBS.2004.1403269Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. A long-term verification of a model that captures the essential glucose- and insulin-kinetics is presented, using retrospective data gathered in an Intensive Care Unit (ICU). The model uses only two patient specific parameters, for glucose clearance and insulin sensitivity. The optimization of these parameters is accomplished through a novel integration-based fitting approach, and a piecewise linearization of the parameters. This approach reduces the non-linear, non-convex optimization problem to a simple linear equation system. The method was tested on long-term blood glucose recordings from 17 ICU-patients, resulting in an average error of 7%, which is in the range of the sensor error. One-hour predictions of blood glucose data proved acceptable with an error range between 7- 11%. These results verify the model’s ability to capture longterm observed glucose-insulin dynamics in hyperglycaemic ICU patients