Insulin + nutrition control for tight critical care glycaemic regulation

A new insulin and nutrition control method for tight glycaemic control in critical care is presented from concept to clinical trials to clinical practice change. The primary results show that the method can provide very tight glycaemic control in critical care for a very critically ill cohort. More specifically, the final clinical practice change protocol provided 2100 hours of control with average blood glucose of 5.8 +/- 0.9 mmol/L for an initial 10 patient pilot study. It also used less insulin, while providing the same or greater nutritional input, as compared to retrospective hospital control for a relatively very critically ill cohort with high insulin resistance

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

Overview of Glycemic Control in Critical Care - Relating Performance and Clinical Results

Inagural review article invited for inaugural journalBackground: 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 95th percentile (±2 ) standard error (SE95%) to enable better comparison across cohorts. Results: Model-based control trials lower blood glucose into a 72-110mg/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 patient-specific 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 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

Development of a Clinical Type 1 Diabetes Metabolic System Model and in Silico Simulation Tool

Invited journal symposium paperObjectives: To develop a safe and effective protocol for the clinical control of Type 1 diabetes using conventional self-monitoring blood glucose (SMBG) measurements, and multiple daily injection (MDI) with insulin analogues. To develop an in silico simulation tool of Type 1 diabetes to predict long-term glycaemic control outcomes of clinical interventions. Methods: The virtual patient method is used to develop a simulation tool for Type 1 diabetes using data from a Type 1 diabetes patient cohort (n=40). The tool is 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 replacement are evaluated as a function of self-monitoring blood glucose (SMBG) frequency in conjunction with the (AC and CC) prandial control protocols. Results: In long-term glycaemic control, the AC protocol significantly decreases HbA1c in conditions of suboptimal basal insulin replacement for SMBG frequencies =6/day, and reduced the occurrence of mild and severe hypoglycaemia by 86-100% over controls over all SMBG frequencies in conditions of optimal basal insulin. Conclusions: A simulation tool to predict long-term glycaemic control outcomes from clinical interventions is 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 hypoglycaemia is a large psychological barrier to glycaemic control, the AC protocol may represent the next evolution of intensive insulin therapy to deliver increased glycaemic control with increased safety. Further clinical or experimental validation is needed to fully prove the concept

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

Hyperglycaemia is prevalent in critical illness and increases the risk of further complications and mortality, while tight control can reduce mortality up to 43%. Adaptive control methods are capable of highly accurate, targeted blood glucose regulation using limited numbers of manual measurements due to patient discomfort and labour intensity. Therefore, the option to obtain greater data density using emerging continuous glucose sensing devices is attractive. However, the few such systems currently available can have errors in excess of 20-30%. In contrast, typical bedside testing kits have errors of approximately 7-10%. Despite greater measurement frequency larger errors significantly impact the resulting glucose and patient specific parameter estimates, and thus the control actions determined creating an important safety and performance issue. This paper models the impact of the Continuous Glucose Monitoring System (CGMS, Medtronic, Northridge, CA) on model-based parameter identification and glucose prediction. An integral-based fitting and filtering method is developed to reduce the effect of these errors. A noise model is developed based on CGMS data reported in the literature, and is slightly conservative with a mean Clarke Error Grid (CEG) correlation of R=0.81 (range: 0.68-0.88) as compared to a reported value of R=0.82 in a critical care study. Using 17 virtual patient profiles developed from retrospective clinical data, this noise model was used to test the methods developed. Monte-Carlo simulation for each patient resulted in an average absolute one-hour 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

Pulmonary embolism diagnostics from the driver function

Ventricular driver functions are not readily measured in the ICU, but can clearly indicate the development of pulmonary embolism (PE) otherwise difficult to diagnose. Recent work has developed accurate methods of measuring these driver functions from readily available ICU measurements. This research tests those methods by assessing the ability of these driver functions to diagnose the evolution of PE

AZD8055 enhances in vivo efficacy of afatinib in chordomas

Chordomas are primary bone tumors that arise in the cranial base, mobile spine, and sacrococcygeal region, affecting patients of all ages. Currently, there are no approved agents for chordoma patients. Here, we evaluated the anti-tumor efficacy of small molecule inhibitors that target oncogenic pathways in chordoma, as single agents and in combination, to identify novel therapeutic approaches with the greatest translational potential. A panel of small molecule compounds was screened in vivo against patient-derived xenograft (PDX) models of chordoma, and potentially synergistic combinations were further evaluated using chordoma cell lines and xenograft models. Among the tested agents, inhibitors of EGFR (BIBX 1382, erlotinib, and afatinib), c-MET (crizotinib), and mTOR (AZD8055) significantly inhibited tumor growth in vivo but did not induce tumor regression. Co-inhibition of EGFR and c-MET using erlotinib and crizotinib synergistically reduced cell viability in chordoma cell lines but did not result in enhanced in vivo activity. Co-inhibition of EGFR and mTOR pathways using afatinib and AZD8055 synergistically reduced cell viability in chordoma cell lines. Importantly, this dual inhibition completely suppressed tumor growth in vivo, showing improved tumor control. Together, these data demonstrate that individual inhibitors of EGFR, c-MET, and mTOR pathways suppress chordoma growth both in vitro and in vivo. mTOR inhibition increased the efficacy of EGFR inhibition on chordoma growth in several preclinical models. The insights gained from our study potentially provide a novel combination therapeutic strategy for patients with chordoma. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland