Is there more to glycaemic control than meets the eye?

Tight glycaemic control has emerged as a major focus in critical care. However, the struggle to repeat, improve and standardize the results of the initial landmark studies is ongoing. The prospective computerized glycaemic control study by Shulman et al. highlights two emerging and often overlooked aspects of intensive insulin therapy protocols beyond simple glycaemic performance. First, the clinical ergonomics and ability to integrate into the critical care unit workflow must be considered as they may impact results and definitely affect uptake. Second, the real lessons of any protocol's performance are likely to be best realized by comparison with other results, a task that is very difficult without a consensus method of reporting that allows such comparisons across studies. Embracing these issues will take the field closer to accepted, repeatable approaches to tight glycaemic control

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

A novel mechanical lung model of pulmonary diseases to assist with teaching and training

BACKGROUND: A design concept of low-cost, simple, fully mechanical model of a mechanically ventilated, passively breathing lung is developed. An example model is built to simulate a patient under mechanical ventilation with accurate volumes and compliances, while connected directly to a ventilator. METHODS: The lung is modelled with multiple units, represented by rubber bellows, with adjustable weights placed on bellows to simulate compartments of different superimposed pressure and compliance, as well as different levels of lung disease, such as Acute Respiratory Distress Syndrome (ARDS). The model was directly connected to a ventilator and the resulting pressure volume curves recorded. RESULTS: The model effectively captures the fundamental lung dynamics for a variety of conditions, and showed the effects of different ventilator settings. It was particularly effective at showing the impact of Positive End Expiratory Pressure (PEEP) therapy on lung recruitment to improve oxygenation, a particulary difficult dynamic to capture. CONCLUSION: Application of PEEP therapy is difficult to teach and demonstrate clearly. Therefore, the model provide opportunity to train, teach, and aid further understanding of lung mechanics and the treatment of lung diseases in critical care, such as ARDS and asthma. Finally, the model's pure mechanical nature and accurate lung volumes mean that all results are both clearly visible and thus intuitively simple to grasp

Model-based optimal PEEP in mechanically ventilated ARDS patients in the Intensive Care Unit

Background: The optimal level of positive end-expiratory pressure (PEEP) is still widely debated in treating acute respiratory distress syndrome (ARDS) patients. Current methods of selecting PEEP only provide a range of values and do not provide unique patient-specific solutions. Model-based methods offer a novel way of using non-invasive pressure-volume (PV) measurements to estimate patient recruitability. This paper examines the clinical viability of such models in pilot clinical trials to assist therapy, optimise patient-specific PEEP, assess the disease state and response over time. Methods: Ten patients with acute lung injury or ARDS underwent incremental PEEP recruitment manoeuvres. PV data was measured at increments of 5 cmH(2)O and fitted to the recruitment model. Inspiratory and expiratory breath holds were performed to measure airway resistance and auto-PEEP. Three model-based metrics are used to optimise PEEP based on opening pressures, closing pressures and net recruitment. ARDS status was assessed by model parameters capturing recruitment and compliance. Results: Median model fitting error across all patients for inflation and deflation was 2.8% and 1.02% respectively with all patients experiencing auto-PEEP. In all three metrics' cases, model-based optimal PEEP was higher than clinically selected PEEP. Two patients underwent multiple recruitment manoeuvres over time and model metrics reflected and tracked the state or their ARDS. Conclusions: For ARDS patients, the model-based method presented in this paper provides a unique, non-invasive method to select optimal patient-specific PEEP. In addition, the model has the capability to assess disease state over time using these same models and methods

Ascorbate-dependent vasopressor synthesis: a rationale for vitamin C administration in severe sepsis and septic shock?

Severe systemic inflammatory response to infection results in severe sepsis and septic shock, which are the leading causes of death in critically ill patients. Septic shock is characterised by refractory hypotension and is typically managed by fluid resuscitation and administration of catecholamine vasopressors such as norepinephrine. Vasopressin can also be administered to raise mean arterial pressure or decrease the norepinephrine dose. Endogenous norepinephrine and vasopressin are synthesised by the copper-containing enzymes dopamine β-hydroxylase and peptidylglycine α-amidating monooxygenase, respectively. Both of these enzymes require ascorbate as a cofactor for optimal activity. Patients with severe sepsis present with hypovitaminosis C, and pre-clinical and clinical studies have indicated that administration of high-dose ascorbate decreases the levels of pro-inflammatory biomarkers, attenuates organ dysfunction and improves haemodynamic parameters. It is conceivable that administration of ascorbate to septic patients with hypovitaminosis C could improve endogenous vasopressor synthesis and thus ameliorate the requirement for exogenously administered vasopressors. Ascorbate-dependent vasopressor synthesis represents a currently underexplored biochemical mechanism by which ascorbate could act as an adjuvant therapy for severe sepsis and septic shock

Insulin Sensitivity and Sepsis Score: A Correlation between Model-based Metric and Sepsis Scoring System in Critically Ill Patients

Sepsis is highly correlated with mortality and morbidity. Sepsis is a clinical condition demarcated as the existence of infection and systemic inflammatory response syndrome, SIRS. Confirmation of infection requires a blood culture test, which requires incubation, and thus results take at least 48 h for a syndrome that requires early direct treatment. Since sepsis has a strong inflammatory component, it is hypothesized that metabolic markers affected by inflammation, such as insulin sensitivity, might provide a metric for more rapid, real-time diagnosis. This study uses clinical data from 30 sepsis patients (7624 h in ICU) of whom 60% are male. Median age and median Apache II score are 63 years and 19, respectively. Model-identified insulin sensitivity (SI) profiles were obtained for each patient, and insulin sensitivity and its hourly changes were correlated with modified hourly sepsis scores (SSH1). SI profiles and values were similar across the cohort. The sepsis score is highly variable and changes rapidly. The modified hourly sepsis score, SSH1, shows a better relation with insulin sensitivity due to less fluctuation in the SIRS element. Median SI and median SI of the cohort is 0.4193e-3 and 0.004253e-3 L/mU.min, respectively. Additionally, median SI are 4.392 × 10−4 L/mU min (SSH1 = 0), 4.153 × 10−4 L/mU min (SSH1 = 1), 3.752 × 10−4 L/mU min (SSH1 = 2) and 2.353 × 10−4 L/mU min (SSH1 = 3). Significant relationship between insulin sensitivity across different SSH1 groups was observed (p < 0.05) even when corrected for multiple comparisons. CDF of SI indicates that insulin sensitivity is more significant when comparing an hourly sepsis score at a very distinguished level

Improved pressure contour analysis for estimating cardiac stroke volume using pulse wave velocity measurement.

peer reviewedBACKGROUND: Pressure contour analysis is commonly used to estimate cardiac performance for patients suffering from cardiovascular dysfunction in the intensive care unit. However, the existing techniques for continuous estimation of stroke volume (SV) from pressure measurement can be unreliable during hemodynamic instability, which is inevitable for patients requiring significant treatment. For this reason, pressure contour methods must be improved to capture changes in vascular properties and thus provide accurate conversion from pressure to flow. METHODS: This paper presents a novel pressure contour method utilizing pulse wave velocity (PWV) measurement to capture vascular properties. A three-element Windkessel model combined with the reservoir-wave concept are used to decompose the pressure contour into components related to storage and flow. The model parameters are identified beat-to-beat from the water-hammer equation using measured PWV, wave component of the pressure, and an estimate of subject-specific aortic dimension. SV is then calculated by converting pressure to flow using identified model parameters. The accuracy of this novel method is investigated using data from porcine experiments (N = 4 Pietrain pigs, 20-24.5 kg), where hemodynamic properties were significantly altered using dobutamine, fluid administration, and mechanical ventilation. In the experiment, left ventricular volume was measured using admittance catheter, and aortic pressure waveforms were measured at two locations, the aortic arch and abdominal aorta. RESULTS: Bland-Altman analysis comparing gold-standard SV measured by the admittance catheter and estimated SV from the novel method showed average limits of agreement of +/-26% across significant hemodynamic alterations. This result shows the method is capable of estimating clinically acceptable absolute SV values according to Critchely and Critchely. CONCLUSION: The novel pressure contour method presented can accurately estimate and track SV even when hemodynamic properties are significantly altered. Integrating PWV measurements into pressure contour analysis improves identification of beat-to-beat changes in Windkessel model parameters, and thus, provides accurate estimate of blood flow from measured pressure contour. The method has great potential for overcoming weaknesses associated with current pressure contour methods for estimating SV

Model-Based Prediction of the Patient-Specific Response to Adrenaline

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

Impact of sensor and measurement timing errors on model-based insulin sensitivity

peer reviewe

The Impact of Parameter Identification Methods on Drug Therapy Control in an Intensive Care Unit

This paper investigates the impact of fast parameter identification methods, which do not require any forward simulations, on model-based glucose control, using retrospective data in the Christchurch Hospital Intensive Care Unit. The integral-based identification method has been previously clinically validated and extensively applied in a number of biomedical applications; and is a crucial element in the presented model-based therapeutics approach. Common non-linear regression and gradient descent approaches are too computationally intense and not suitable for the glucose control applications presented. The main focus in this paper is on better characterizing and understanding the importance of the integral in the formulation and the effect it has on model-based drug therapy control. As a comparison, a potentially more natural derivative formulation which has the same computation speed advantages is investigated, and is shown to go unstable with respect to modelling error which is always present clinically. The integral method remains robust