Geoffrey M. Shaw

Research interests

Publications

• 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

  BioMedical Engineering OnLine · 2018 · https://doi.org/10.1186/s12938-018-0455-y

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

  Critical Care · 2015 · https://doi.org/10.1186/s13054-015-1131-2

• The urine output definition of acute kidney injury is too liberal

  Critical Care · 2013 · https://doi.org/10.1186/cc12784

• STAR Development and Protocol Comparison

  IEEE Transactions on Biomedical Engineering · 2012 · https://doi.org/10.1109/tbme.2012.2214384

• Model-based PEEP optimisation in mechanical ventilation

  BioMedical Engineering OnLine · 2011 · https://doi.org/10.1186/1475-925x-10-111

• A physiological Intensive Control Insulin-Nutrition-Glucose (ICING) model validated in critically ill patients

  Computer Methods and Programs in Biomedicine · 2011 · https://doi.org/10.1016/j.cmpb.2010.12.008

• Improved performance of urinary biomarkers of acute kidney injury in the critically ill by stratification for injury duration and baseline renal function

  Kidney International · 2011 · https://doi.org/10.1038/ki.2010.555

• Test Characteristics of Urinary Biomarkers Depend on Quantitation Method in Acute Kidney Injury

  Journal of the American Society of Nephrology · 2011 · https://doi.org/10.1681/asn.2011040325

• Validation of a model-based virtual trials method for tight glycemic control in intensive care

  BioMedical Engineering OnLine · 2010 · https://doi.org/10.1186/1475-925x-9-84

• Early intervention with erythropoietin does not affect the outcome of acute kidney injury (the EARLYARF trial)

  Kidney International · 2010 · https://doi.org/10.1038/ki.2010.25

• Urinary cystatin C is diagnostic of acute kidney injury and sepsis, and predicts mortality in the intensive care unit

  Critical Care · 2010 · https://doi.org/10.1186/cc9014

• Tight glycemic control in critical care – The leading role of insulin sensitivity and patient variability: A review and model-based analysis

  Computer Methods and Programs in Biomedicine · 2010 · https://doi.org/10.1016/j.cmpb.2010.11.006

• Organ failure and tight glycemic control in the SPRINT study

  Critical Care · 2010 · https://doi.org/10.1186/cc9224

• Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change

  Critical Care · 2008 · https://doi.org/10.1186/cc6868

• Stochastic modelling of insulin sensitivity and adaptive glycemic control for critical care

  Computer Methods and Programs in Biomedicine · 2007 · https://doi.org/10.1016/j.cmpb.2007.04.006

• A Simple Insulin-Nutrition Protocol for Tight Glycemic Control in Critical Illness: Development and Protocol Comparison

  Diabetes Technology & Therapeutics · 2006 · https://doi.org/10.1089/dia.2006.8.191

• Integral-based parameter identification for long-term dynamic verification of a glucose–insulin system model

  Computer Methods and Programs in Biomedicine · 2005 · https://doi.org/10.1016/j.cmpb.2004.10.006

• Minimal haemodynamic system model including ventricular interaction and valve dynamics

  Medical Engineering & Physics · 2003 · https://doi.org/10.1016/j.medengphy.2003.10.001

Current projects

No projects listed.