References of "Moorhead, Katherine"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailEnhanced insulin sensitivity variability in the first 3 days of ICU stay: Implications for TGC
Chase, Geoffrey; Le Compte, Aaron; Penning, Sophie ULg et al

Poster (2011, March)

Detailed reference viewed: 22 (8 ULg)
Full Text
Peer Reviewed
See detailInsulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care
Chase, J. Geoffrey; Le Compte, Aaron J.; Preiser, Jean-Charles et al

in 18th World Congress of the International Federation of Automatic Control (IFAC) (2011)

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a ... [more ▼]

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a <br />patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and <br />evolution of stress response, and are independently associated with mortality. This study examines the <br />daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, <br />54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour <br />and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as <br />well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points <br />(25th and 75th percentiles) are used to assess differences between groups and their evolution over time. <br />Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < <br />0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). <br />Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < <br />0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have <br />lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to <br />an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability <br />with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining <br />glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will <br />require greater measurement frequency, reduced reliance on insulin, and more explicit specification of <br />carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome. [less ▲]

Detailed reference viewed: 38 (10 ULg)
Full Text
See detailTight Glycemic Control Models for Critically Ill Patients in Intensive Care Units
Penning, Sophie ULg; Le Compte, Aaron; Desaive, Thomas ULg et al

Poster (2010, November 26)

Detailed reference viewed: 16 (8 ULg)
Full Text
See detailTight Glycemic Control Models for Critically Ill Patients in Intensive Care Units
Penning, Sophie ULg; Le Compte, Aaron; Moorhead, Katherine ULg et al

in 9th Belgian Day on Biomedical Engineering, "Bridging the gap between medicine and engineering', Friday November 26th 2010 in the Academy Palace, Hertogstraat 1, 1000 Brussels (2010, November 26)

Critically ill patients often present stress-induced hyperglycemia and low insulin sensitivity. Recent studies have shown that high blood glucose (BG) levels are linked to worsened patient outcomes and ... [more ▼]

Critically ill patients often present stress-induced hyperglycemia and low insulin sensitivity. Recent studies have shown that high blood glucose (BG) levels are linked to worsened patient outcomes and increased mortality. Tight glycemic control (TGC) aims at reducing BG levels taking into account inter-patient variability, evolving physiological patient conditions and minimizing hypoglycemic risks. Clinical protocols are used to specify insulin and nutrition rates and BG measurement time interval during control. This research compares different protocols to determine the best one to use at the CHU of Liege. [less ▲]

Detailed reference viewed: 27 (6 ULg)
Full Text
Peer Reviewed
See detailminimal cardiovascular system model including physiological mitral valve opening
Paeme, Sabine ULg; Moorhead, Katherine ULg; chase, J. Geoffrey et al

in 9th Belgian National Day on Biomedical Engineering, Bruxelles, 26th november (2010, November 26)

A minimal cardiovascular system (CVS) model has been previously validated in silico, and in several animal model studies. It accounts for valve dynamics by means of a Heaviside function to simulate the ... [more ▼]

A minimal cardiovascular system (CVS) model has been previously validated in silico, and in several animal model studies. It accounts for valve dynamics by means of a Heaviside function to simulate the “open on pressure, close on flow” law. However, this model does not describe the progressive valve opening and therefore, it is not suitable for studying valve dysfunctions. [less ▲]

Detailed reference viewed: 18 (6 ULg)
Full Text
See detailMinimal cardiovascular system model including physiological mitral valve opening
Paeme, Sabine ULg; Moorhead, Katherine ULg; Chase, J. Geoffrey et al

Poster (2010, November 26)

This research describes a new closed-loop cardiovascular system (CVS) model including a model of the left atrium and a model describing the progressive aperture of the mitral valve

Detailed reference viewed: 25 (9 ULg)
Full Text
Peer Reviewed
See detailPilot Trials of the STAR TGC Protocol in a Cardiac Surgery ICU
LeCompte, Aaron J.; Penning, Sophie ULg; Moorhead, Katherine ULg et al

in Proceedings of the 10th Annual Diabetes Technology Meeting (2010, November)

Detailed reference viewed: 28 (8 ULg)
Full Text
Peer Reviewed
See detailMathematical model of the mitral valve and the cardiovascular system, application for studying, monitoring and in the diagnosis of valvular pathologies
Paeme, Sabine ULg; Moorhead, Katherine ULg; Chase, J. Geoffrey et al

in UKACC international Conference on Control 2010 : Coventry, 7-10 september 2010 (2010, September 07)

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiological ... [more ▼]

A cardiovascular and circulatory system (CVS) model has been validated in silico, and in several animal model studies. It accounts for valve dynamics using Heaviside functions to simulate a physiological accurate “open on pressure, close on flow” law. Thus, it does not consider the real time scale of the valve aperture dynamics and thus doesn’t fully capture valve dysfunction particularly where the dysfunction involves partial closure. This research describes a new closed-loop CVS model including a model describing the progressive aperture of the mitral valve and valid over the full cardiac cycle. This new model is solved for a healthy and diseased mitral valve. [less ▲]

Detailed reference viewed: 105 (16 ULg)
Full Text
Peer Reviewed
See detailOrgan failure and tight glycemic control in the SPRINT study.
Chase, J Geoffrey; Pretty, Christopher G; Pfeifer, Leesa et al

in Critical Care (2010), 14(4), 154

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful ... [more ▼]

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful tight glycemic control (TGC) intervention (SPRINT) on organ failure, morbidity, and thus mortality. METHODS: A retrospective analysis of 371 patients (3,356 days) on SPRINT (August 2005 - April 2007) and 413 retrospective patients (3,211 days) from two years prior, matched by Acute Physiology and Chronic Health Evaluation (APACHE) III. SOFA is calculated daily for each patient. The effect of the SPRINT TGC intervention is assessed by comparing the percentage of patients with SOFA </=5 each day and its trends over time and cohort/group. Organ-failure free days (all SOFA components </=2) and number of organ failures (SOFA components >2) are also compared. Cumulative time in 4.0 to 7.0 mmol/L band (cTIB) was evaluated daily to link tightness and consistency of TGC (cTIB >/=0.5) to SOFA </=5 using conditional and joint probabilities. RESULTS: Admission and maximum SOFA scores were similar (P = 0.20; P = 0.76), with similar time to maximum (median: one day; IQR: 13 days; P = 0.99). Median length of stay was similar (4.1 days SPRINT and 3.8 days Pre-SPRINT; P = 0.94). The percentage of patients with SOFA </=5 is different over the first 14 days (P = 0.016), rising to approximately 75% for Pre-SPRINT and approximately 85% for SPRINT, with clear separation after two days. Organ-failure-free days were different (SPRINT = 41.6%; Pre-SPRINT = 36.5%; P < 0.0001) as were the percent of total possible organ failures (SPRINT = 16.0%; Pre-SPRINT = 19.0%; P < 0.0001). By Day 3 over 90% of SPRINT patients had cTIB >/=0.5 (37% Pre-SPRINT) reaching 100% by Day 7 (50% Pre-SPRINT). Conditional and joint probabilities indicate tighter, more consistent TGC under SPRINT (cTIB >/=0.5) increased the likelihood SOFA </=5. CONCLUSIONS: SPRINT TGC resolved organ failure faster, and for more patients, from similar admission and maximum SOFA scores, than conventional control. These reductions mirror the reduced mortality with SPRINT. The cTIB >/=0.5 metric provides a first benchmark linking TGC quality to organ failure. These results support other physiological and clinical results indicating the role tight, consistent TGC can play in reducing organ failure, morbidity and mortality, and should be validated on data from randomised trials. [less ▲]

Detailed reference viewed: 28 (4 ULg)
Full Text
Peer Reviewed
See detailValidation of a model-based virtual trials method for tight glycemic control in intensive care.
Chase, J Geoffrey; Suhaimi, Fatanah; Penning, Sophie ULg et al

in BioMedical Engineering OnLine (2010), 9

BACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully ... [more ▼]

BACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods. METHODS: Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results. RESULTS: Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols. CONCLUSIONS: This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology. [less ▲]

Detailed reference viewed: 18 (6 ULg)