Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

Introduction: Hyperglycaemia is a common complication of stress and prematurity in extremely low-birth-weight infants. Model-based insulin therapy protocols have the ability to safely improve glycaemic control for this group. Estimating non-insulin-mediated brain glucose uptake by the central nervous system in these models is typically done using population-based body weight models, which may not be ideal. Method: A head circumference-based model that separately treats small-for-gestational-age (SGA) and appropriate-for-gestational-age (AGA) infants is compared to a body weight model in a retrospective analysis of 48 patients with a median birth weight of 750g and median gestational age of 25 weeks. Estimated brain mass, model-based insulin sensitivity (SI) profiles, and projected glycaemic control outcomes are investigated. SGA infants (5) are also analyzed as a separate cohort. Results: Across the entire cohort, estimated brain mass deviated by a median 10% between models, with a per-patient median difference in SI of 3.5%. For the SGA group, brain mass deviation was 42%, and per-patient SI deviation 13.7%. In virtual trials, 87-93% of recommended insulin rates were equal or slightly reduced (δ<0.16mU/h) under the head circumference method, while glycaemic control outcomes showed little change. Conclusion: The results suggest that body weight methods are not as accurate as head circumference methods. Head circumference-based estimates may offer improved modelling accuracy and a small reduction in insulin administration, particularly for SGA infants. © 2014 Elsevier Ireland Ltd

The practice of glycaemic control in intensive care units: A multicentre survey of nursing and medical professionals.

AIMS AND OBJECTIVES: To determine the views of nurses and physicians working in intensive care units (ICU) about the aims of glycaemic control and use of their protocols. BACKGROUND: Evidence about the optimal aims and methods for glycaemic control in ICU is controversial, and current local protocols guiding practice differ between ICUs, both nationally and internationally. The views of professionals on glycaemic control can influence their practice. DESIGN: Cross-sectional, multicentre, survey-based study. METHODS: An online short survey was sent to all physicians and nurses of seven ICUs, including questions on effective glycaemic control, treatment of hypoglycaemia and deviations from protocols' instructions. STROBE reporting guidelines were followed. RESULTS: Over half of the 40 respondents opined that a patient spending <75% admission time within the target glycaemic levels constituted poor glycaemic control. Professionals with more than 5 years of experience were more likely to rate a patient spending 50%-74% admission time within target glycaemic levels as poor than less experienced colleagues. Physicians were more likely to rate a patient spending <50% admission time within target as poor than nurses. There was general agreement on how professionals would rate most deviations from their protocols. Nurses were more likely to rate insulin infusions restarted late and incorrect dosage of rescue glucose as major deviations than physicians. Most professionals agreed on when they would treat hypoglycaemia. CONCLUSIONS: When surveyed on various aspects of glycaemic control, ICU nurses and physicians often agreed, although there were certain areas of disagreement, in which their profession and level of experience seemed to play a role. RELEVANCE TO CLINICAL PRACTICE: Differing views on glycaemic control amongst professionals may affect their practice and, thus, could lead to health inequalities. Clinical leads and the multidisciplinary ICU team should assess and, if necessary, address these differing opinions.Nottingham University Hospitals (NUH) Charity and the NUH Department of Research and Innovation University of Nottingham School of Health Sciences director of research small grant

Impact of variation in patient response on model-based control of glycaemia in critically ill patients

Critically ill patients commonly experience stress-induced hyperglycaemia, and several studies have shown tight glycaemic control (TGC) can reduce patient mortality. However, tight control is often difficult to achieve due to conflicting drug therapies and evolving patient condition. Thus, a number of studies have failed to achieve TGC possibly due to use of fixed insulin dosing protocols over adaptive patient-specific methods. Model-based targeted glucose control can adapt insulin and dextrose interventions to match identified patient sensitivity. This study explores the impact on control of assuming patient response to insulin is constant versus time-varying. Simulated trials of glucose control were performed on adult and neonatal virtual patient cohorts. Results indicate assumptions of constant insulin sensitivity can lead to significantly increased rates of hypoglycaemia, a commonly cited issue preventing increased adoption of tight glycaemic control in critical care. It is clear that adaptive, patientspecific, approaches are better able to manage inter- and intra- patient variability than typical, fixed protocols

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

Impact of variation in patient response on model-based control of glycaemia in critically ill patients

Critically ill patients commonly experience stress-induced hyperglycaemia, and several studies have shown tight glycaemic control (TGC) can reduce patient mortality. However, tight control is often difficult to achieve due to conflicting drug therapies and evolving patient condition. Thus, a number of studies have failed to achieve consistently safe and effective TGC possibly due to the use of fixed insulin dosing protocols over adaptive patient-specific methods. Model-based targeted glucose control can adapt insulin and dextrose interventions to match identified patient insulin sensitivity. This study explores the impact on glycemic control of assuming patient response to insulin is constant, as many protocols do, versus time-varying. Validated virtual trial simulations of glucose control were performed on adult and neonatal virtual patient cohorts. Results indicate assumptions of constant insulin sensitivity can lead to six-fold increases in incidence of hypoglycaemia, similar to literature reports and a commonly cited issue preventing increased adoption of TGC in critical care. It is clear that adaptive, patient-specific, approaches are better able to manage inter- and intra- patient variability than typical, fixed protocols