References of "Chase, Geoffrey"
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See detailImproved Blood Glucose Forecasting Models using Changes in Insulin Sensitivity in Intensive Care Patients
Uyttendaele, Vincent ULg; Dickson, Jennifer; Shaw, Geoff et al

Poster (2017, February 01)

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy ... [more ▼]

Introduction: Hyperglycaemia, hypoglycaemia and glycaemic variability are associated with worsened outcomes and increased mortality in intensive care units. Glycaemic control (GC) using insulin therapy has shown improved outcomes, but have been proven difficult to repeat or achieve safely. STAR (Stochastic TARgeted) is a model-based glycaemic control protocol using a stochastic model to forecast distributions of likely future changes in insulin sensitivity (SI) based on its current value. This can be used to determine likely future blood glucose (BG) levels for a given intervention, enabling the most optimal dose selection that best overlaps a clinically defined BG target band. This study presents a novel 3D model capable to predict likely future distribution of SI using both current SI and its prior variability (%ΔSI). Methods: Metabolic data from 3 clinical ICU cohorts totalling 819 episodes and 68629 hours of treatment under STAR and SPRINT protocols are used in this study. Data triplets (%ΔSIn, SIn, SIn+1) are created and binned together in a range of %ΔSI = [-100%, 200%] and SIn = [1.0e-7, 2.1e-3] in bin sizes of %ΔSI = 10% and SIn = 0.5e-4. The 5th, 50th, and 95th percentile of SIn+1 are determined for each bin where data density is high enough (>100 triplets) and compared to the previous stochastic model. The predictive power of the two models are compared by computing median [IQR] per-patient percentage prediction of SI within the 5th-95th and 25th-75th percentile ranges of model predictions. Results: Results show the previous model is over-conservative for ~77% of the data, mainly where %ΔSI is within an absolute 25% change. The percentage change in the 90% CI width in this region is reduced by ~25-40%. Conversely, non-conservative regions are also identified, with 90% CI width increased up to ~80%. Predictive power is similar for both model (60.3% [47.8%, 71.5%] vs. 51.2 [42.9%, 59.2%] within 25th-75th and 93.6% [85.7%, 97.3%] vs. 90.7% [84.4%, 94.6%] within 5th-95th range). Conclusions: The new 3D model achieved similar predictive power as the previous model by reducing the 5th-95th percentile prediction range for 77% of the data, predominantly where SI is stable. If the conservatism of the previous model reduces risk of hypoglycaemia, it also inhibits the controller’s ability to reduce BG to the normal range by safely using more aggressive dosing. The 3D new model thus better characterises patient-specific response to insulin, and allows more optimal dosing, increasing performance and safety. [less ▲]

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See detailWhen NICE is Not Nice: Performance of Two ICU Glycaemic Control Protocols
Uyttendaele, Vincent ULg; Dickson, Jennifer; Stewart, Kent et al

Poster (2016)

Objective: Hypoglycemia, hyperglycemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial (unexpectedly?) showed no clinical benefit from ... [more ▼]

Objective: Hypoglycemia, hyperglycemia and blood glucose (BG) variability are associated with worsened outcomes in critical care. However, NICE-SUGAR trial (unexpectedly?) showed no clinical benefit from intensive insulin therapy. This study compares the table-based NICE-SUGAR and model-based STAR protocols to assess their relative capability to achieve safe, effective control for all patients. Method: Validated virtual patients (n=443) were used to simulate glycemic outcomes of the NICE-SUGAR and STAR protocols. Key outcomes assess tightness and safety of control for all patients: %BG in 80–144 mg/dL range (PTR); Per-Patient Mean BG (PPM_BG); and Incidence Hypoglycemia (BG<40 mg/dL). These metrics assess performance overall, for each patient, and safety. Results are assessed for NICE-SUGAR measuring per-protocol (~24/day) and at reported average rate (~3-hourly; ~8/day). STAR measures 1-3-hourly, averaging 12/day. Result: Per-protocol, STAR provided tight control, with higher PTR (90.7% vs. 78.3%) and tighter median [IQR] PPM_BG (112[106-119] vs. 117[106–137] mg/dL), and greater safety from hypoglycemia (5 (1%) vs. 10 patients (2.5%)). The 5-95th percentile range PPM_BG for NICE-SUGAR (97–185 mg/dL) shows ~5% of NICE-SUGAR patients had mean BG above 180mg/dL matching clinically reported performance. STAR’s 90th percentile PPM_BG range was (97–146 mg/dL). Measuring as recorded clinically, NICE-SUGAR had PTR of 77%, PPM_BG of 122 [110-140] mg/dL and 24(6%) of patients experienced hypoglycemia. These results match clinically reported values well (mean BG 115 vs. 118 mg/dL clinically vs. simulation, clinically 7% of patients had a hypoglycemic event) Conclusions: Glycemic control protocols need to be both safe and effective for all patients before potential clinical benefits can be assessed. NICE-SUGAR (measured ~24/day or as reported ~8/day) was unable to achieve this outcome for all patients. [less ▲]

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See detailAre Survivors Easier to Control? Why the Association of Glycemia and Mortality in Critical Care is Real
Uyttendaele, Vincent ULg; Dickson, Jennifer; Stewart, Kent et al

Poster (2016)

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See detailEarly detection of abnormal left ventricular relaxation in acute myocardial ischemia with a quadratic model.
MORIMONT, Philippe ULg; Pironet, Antoine ULg; Desaive, Thomas ULg et al

in Medical engineering & physics (2014)

AIMS: The time constant of left ventricular (LV) relaxation derived from a monoexponential model is widely used as an index of LV relaxation rate, although this model does not reflect the non-uniformity ... [more ▼]

AIMS: The time constant of left ventricular (LV) relaxation derived from a monoexponential model is widely used as an index of LV relaxation rate, although this model does not reflect the non-uniformity of ventricular relaxation. This study investigates whether the relaxation curve can be better fitted with a "quadratic" model than with the "conventional" monoexponential model and if changes in the LV relaxation waveform due to acute myocardial ischemia could be better detected with the quadratic model. METHODS AND RESULTS: Isovolumic relaxation was assessed with quadratic and conventional models during acute myocardial ischemia performed in 6 anesthetized pigs. Mathematical development indicates that one parameter (Tq) of the quadratic model reflects the rate of LV relaxation, while the second parameter (K) modifies the shape of the relaxation curve. Analysis of experimental data obtained in anesthetized pigs showed that the shape of LV relaxation consistently deviates from the conventional monoexponential decay. During the early phase of acute myocardial ischemia, the rate and non-uniformity of LV relaxation, assessed with the quadratic function, were significantly enhanced. Tq increased by 16% (p<0.001) and K increased by 12% (p<0.001) within 30 and 60min, respectively, after left anterior descending (LAD) coronary artery occlusion. However, no significant changes were observed with the conventional monoexponential decay within 60min of ischemia. CONCLUSIONS: The quadratic model better fits LV isovolumic relaxation than the monoexponential model and can detect early changes in relaxation due to acute myocardial ischemia that are not detectable with conventional methods. [less ▲]

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See detailValidation of a Virtual Patient and Virtual Trials Method for Accurate Prediction of TGC Protocol Performance
Suhaimi, Fatanah; Le Compte, Aaron; Penning, Sophie ULg et al

Poster (2011, March)

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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)

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