A Deviation in BG dynamics during liver transplantation comparing ICU patients: a model-based approach

A proper glycemic control would beneficial affect on the outcomes of the liver-transplantation. Model based validated tight glycemic control protocol, STAR exists for ICU treatments. The validated metabolic model ICING for STAR differ in the blood glucose dynamics. By localizing the places of the extraordinary LT patients dynamics we can specify modifications on the ICU patient model. Based on the analyzes of ICING model, these dynamics mainly occurs in the 1) pre-anhepatic phase at the beginning of the surgery, 2) at the portal vein reperfusion and 3) in the post-anhepatic phase before 500 minutes from the reperfusion

Insulin sensitivity and blood glucose levels analysis of Hungarian Patients in their early phase of ICU treatment under model-based glycemic control

Critically ill intensive care unit (ICU) patients frequently experience acute insulin resistance (low insulin sen- sitivity) manifesting as stress-induced hyperglycemia and hyperinsulinemia, especially in the early stages of the treatment. High inter/intra-patient variability makes glycemic control difficult. Stochastic TARgeted (STAR) a model-based glycemic control, directly manages this variability using model-based insulin sensitivity (SI) and a second model of its variability. This study analyses insulin sensitivity and blood glucose levels of ICU patients in the Hungarian cohorts and compares the first 24h of the treatment and the rest of the treatment in order to assess the differences. Using clinical data from 93 patients treated with STAR, insulin sensitivity and blood glucose are compared at first between the first 24h and the rest of the treatment, then the first 24h and the successive treatment days. Results show that insulin sensitivity is lower in the first 24h compared to the rest of the treatment and in the first 24h compared to the five successive days. Blood glucose levels were higher in the first 24h compared to the rest of the treatment time and in the first 24h compared to the five successive days. Patients in the early stages of ICU have low insulin sensitivity and high blood glucose levels, as expected, given the stress response physiology. Given the results, this study initiates the idea of implementing a customized model-based control designed only for the early phase of Hungarian ICU patient’s model-based treatment that can effectively handle hyperglycemia and insulin resistance and create a space for further development

Improved insulin sensitivity prediction method using sex information of the patients

Applying tight glycaemic control (TGC) is an essential treatment in the intensive care therapy in order to avoid stress-induced hyperglycaemia, resulting in clinical benefits. The STAR (Stochastic-TARgeted) protocol is a model-based TGC protocol successfully implementing safe and efficient patient treatment. It uses the ICING model to describe the metabolic dynamics of the patients and a prediction system to manage patient specific metabolic variability. In the STAR protocol most of the parameters of the ICING model are fixed constants, except the inslulin sensitivity (SI) that is used as the state descriptor of the patient. In our previous researches we succesfully created nowel neural network based methods to predict the 90% confidence interval of the future SI value by using different input parameter construction. Recently we created models that uses the sex of the patient to make the prediction more patient-specific

In-silico Simulation Based Evaluation of Insulin Prediction Method for Personalized Medical Treatment

Stress-induced hyperglycaemia is a frequent and serious issue in the intensive care causing elevated mortality rate. Insulin therapy is often applied in ICUs to normalize the patient’s blood glucose level. This treatment method is generally referred to as Tight Glycaemic Control (TGC). The most widely used TGC protocol is the STAR (Stochastic-TARgeted) protocol, which uses the patient’s insulin sensitivity (SI) as a key parameter to describe the patient’s actual state. STAR protocol uses the clinically validated ICING model to describe the human metabolic system and a stochastic model to predict the patient’s future SI values. In this paper, the evaluation of two new, artificial neural network based SI prediction methods is presented. The models were trained on a dataset collected during the STAR treatment. The models were evaluated by using a so-called in-silico validation, simulating the clinical interventions on virtual patients created from historical treatment data. The results proved that the new models could be applied in the SI prediction. The prediction accuracy was the same or even better in some aspects than the currently used model. The methods also support higher dimensional SI prediction, which is the field of recent research and resulted in improved personalized treatment based on the evaluation presented

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

Background: Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. Methods: Changes in patient-specific lung elastance over a pressure–volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. Results: Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony. Conclusions: Experimental test-lung validation demonstrates the method’s reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool

Respiratory mechanics assessment for reverse-triggered breathing cycles using pressure reconstruction

Monitoring patient-specific respiratory mechanics can be used to guide mechanical ventilation (MV) therapy in critically ill patients. However, many patients can exhibit spontaneous breathing (SB) efforts during ventilator supported breaths, altering airway pressure waveforms and hindering model-based (or other) identification of the true, underlying respiratory mechanics necessary to guide MV. This study aims to accurately assess respiratory mechanics for breathing cycles masked by SB efforts. A cumulative pressure reconstruction method is used to ameliorate SB by identifying SB affected waveforms and reconstructing unaffected pressure waveforms for respiratory mechanics identification using a single-compartment model. Performance is compared to conventional identification without reconstruction, where identified values from reconstructed waveforms should be less variable. Results are validated with 9485 breaths affected by SB, including periods of muscle paralysis that eliminates SB, as a validation test set where reconstruction should have no effect. In this analysis, the patients are their own control, with versus without reconstruction, as assessed by breath-to-breath variation using the non-parametric coefficient of variation (CV) of respiratory mechanics. Pressure reconstruction successfully estimates more consistent respiratory mechanics. CV of estimated respiratory elastance is reduced up to 78% compared to conventional identification (p < 0.05). Pressure reconstruction is comparable (p > 0.05) to conventional identification during paralysis, and generally performs better as paralysis weakens, validating the algorithm’s purpose. Pressure reconstruction provides less-affected pressure waveforms, ameliorating the effect of SB, resulting in more accurate respiratory mechanics identification. Thus providing the opportunity to use respiratory mechanics to guide mechanical ventilation without additional muscle relaxants, simplifying clinical care and reducing risk

Model based PEEP titration versus standard practice in mechanical ventilation: A randomised controlled trial

Background: Positive end-expiratory pressure (PEEP) at minimum respiratory elastance during mechanical ventilation (MV) in patients with acute respiratory distress syndrome (ARDS) may improve patient care and outcome. The Clinical utilisation of respiratory elastance (CURE) trial is a two-arm, randomised controlled trial (RCT) investigating the performance of PEEP selected at an objective, model-based minimal respiratory system elastance in patients with ARDS. Methods and design: The CURE RCT compares two groups of patients requiring invasive MV with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio ≤ 200; one criterion of the Berlin consensus definition of moderate (≤ 200) or severe (≤ 100) ARDS. All patients are ventilated using pressure controlled (bi-level) ventilation with tidal volume = 6-8 ml/kg. Patients randomised to the control group will have PEEP selected per standard practice (SPV). Patients randomised to the intervention will have PEEP selected based on a minimal elastance using a model-based computerised method. The CURE RCT is a single-centre trial in the intensive care unit (ICU) of Christchurch hospital, New Zealand, with a target sample size of 320 patients over a maximum of 3 years. The primary outcome is the area under the curve (AUC) ratio of arterial blood oxygenation to the fraction of inspired oxygen over time. Secondary outcomes include length of time of MV, ventilator-free days (VFD) up to 28 days, ICU and hospital length of stay, AUC of oxygen saturation (SpO )/FiO during MV, number of desaturation events (SpO < 88%), changes in respiratory mechanics and chest x-ray index scores, rescue therapies (prone positioning, nitric oxide use, extracorporeal membrane oxygenation) and hospital and 90-day mortality. Discussion: The CURE RCT is the first trial comparing significant clinical outcomes in patients with ARDS in whom PEEP is selected at minimum elastance using an objective model-based method able to quantify and consider both inter-patient and intra-patient variability. CURE aims to demonstrate the hypothesized benefit of patient-specific PEEP and attest to the significance of real-time monitoring and decision-support for MV in the critical care environment. Trial registration: Australian New Zealand Clinical Trial Registry, ACTRN12614001069640. Registered on 22 September 2014. (https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366838&isReview=true) The CURE RCT clinical protocol and data usage has been granted by the New Zealand South Regional Ethics Committee (Reference number: 14/STH/132). 2 2

Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

© 2018 The Author(s). Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care

Expression of costimulatory molecules in the bovine corpus luteum

BACKGROUND: Bovine luteal parenchymal cells express class II major histocompatibility complex (MHC) molecules and stimulate class II MHC-dependent activation of T cells in vitro. The ability of a class II MHC-expressing cell type to elicit a response from T cells in vivo is also dependent on expression of costimulatory molecules by the antigen presenting cell and delivery of a costimulatory signal to the T cell. Whether bovine luteal parenchymal cells express costimulatory molecules and can deliver the costimulatory signal is currently unknown. METHODS: Bovine luteal tissue was collected during the early (day 5; day of estrus = day 0), mid (day 11–12), or late (day 18) luteal phase of the estrous cycle, and at 0, 0.5, 1, 4, 12 or 24 hours following administration of PGF2alpha to cows on day 10 of the estrous cycle. Northern analysis was used to measure CD80 or CD86 mRNA concentrations in luteal tissue samples. Mixed luteal parenchymal cell cultures and purified luteal endothelial cell cultures were prepared, and real-time RT-PCR was used to examine the presence of CD80 and CD86 mRNA in each culture type. Monoclonal antibodies to CD80 and CD86 were added to a mixed luteal parenchymal cell-T cell co-culture in vitro T cell proliferation assay to assess the functional significance of costimulatory molecules on activation of T lymphocytes by luteal parenchymal cells. RESULTS: Northern analysis revealed CD80 and CD86 mRNAs in luteal tissue, with greatest steady-state concentrations at midcycle. CD80 and CD86 mRNAs were detected in mixed luteal parenchymal cell cultures, but only slight amounts of CD80 (and not CD86) mRNA were detected in cultures of luteal endothelial cells. Luteinizing hormone, PGF2alpha and TNF-alpha were without effect on concentrations of CD80 or CD86 mRNA in mixed luteal parenchymal cells cultures. Anti-CD80 or anti-CD86 monoclonal antibodies inhibited T cell proliferation in the in vitro T cell proliferation assay. CONCLUSION: It can be concluded from this study that parenchymal cells within the bovine CL express functional costimulatory molecules that facilitate interactions between with T cells, and these components of the antigen presentation pathway are expressed maximally in the midcycle CL