References of "Signal, Matthew"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailUsing Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition: A Review.
Thomas, Felicity Louise ULg; Signal, Matthew; Chase, J. Geoffrey

in Journal of diabetes science and technology (2015), 9(6), 1327-35

Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood ... [more ▼]

Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood glucose (BG) levels. Continuous glucose monitoring (CGM) devices allow glycemic dynamics to be captured much more frequently (every 2-5 minutes) than traditional measures of blood glucose and have begun to be used in critical care patients and neonates to help monitor dysglycemia. In an attempt to obtain a better insight relating biomedical signals and patient status, some researchers have turned toward advanced time series analysis methods. In particular, Detrended Fluctuation Analysis (DFA) has been a topic of many recent studies in to glycemic dynamics. DFA investigates the "complexity" of a signal, how one point in time changes relative to its neighboring points, and DFA has been applied to signals like the inter-beat-interval of human heartbeat to differentiate healthy and pathological conditions. Analyzing the glucose metabolic system with such signal processing tools as DFA has been enabled by the emergence of high quality CGM devices. However, there are several inconsistencies within the published work applying DFA to CGM signals. Therefore, this article presents a review and a "how-to" tutorial of DFA, and in particular its application to CGM signals to ensure the methods used to determine complexity are used correctly and so that any relationship between complexity and patient outcome is robust. [less ▲]

Detailed reference viewed: 42 (2 ULg)
Full Text
Peer Reviewed
See detailImpact of sensor and measurement timing errors on model-based insulin sensitivity
Pretty, Christopher ULg; Signal, Matthew; Fisk, Liam et al

in Computer Methods & Programs in Biomedicine (2013)

Detailed reference viewed: 16 (2 ULg)
Full Text
Peer Reviewed
See detailComplexity of continuous glucose monitoring data in critically ill patients: continuous glucose monitoring devices, sensor locations, and detrended fluctuation analysis methods.
Signal, Matthew; Thomas, Felicity Louise ULg; Shaw, Geoffrey M. et al

in Journal of diabetes science and technology (2013), 7(6), 1492-506

BACKGROUND: Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of ... [more ▼]

BACKGROUND: Critically ill patients often experience high levels of insulin resistance and stress-induced hyperglycemia, which may negatively impact outcomes. However, evidence surrounding the causes of negative outcomes remains inconclusive. Continuous glucose monitoring (CGM) devices allow researchers to investigate glucose complexity, using detrended fluctuation analysis (DFA), to determine whether it is associated with negative outcomes. The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA. METHODS: This study uses CGM data from critically ill patients who were each monitored concurrently using Medtronic iPro2s on the thigh and abdomen and a Medtronic Guardian REAL-Time on the abdomen. This allowed interdevice/calibration type and intersensor site variation to be assessed. Detrended fluctuation analysis is a technique that has previously been used to determine the complexity of CGM data in critically ill patients. Two variants of DFA, monofractal and multifractal, were used to assess the complexity of sensor glucose data as well as the precalibration raw sensor current. Monofractal DFA produces a scaling exponent (H), where H is inversely related to complexity. The results of multifractal DFA are presented graphically by the multifractal spectrum. RESULTS: From the 10 patients recruited, 26 CGM devices produced data suitable for analysis. The values of H from abdominal iPro2 data were 0.10 (0.03-0.20) higher than those from Guardian REAL-Time data, indicating consistently lower complexities in iPro2 data. However, repeating the analysis on the raw sensor current showed little or no difference in complexity. Sensor site had little effect on the scaling exponents in this data set. Finally, multifractal DFA revealed no significant associations between the multifractal spectrums and CGM device type/calibration or sensor location. CONCLUSIONS: Monofractal DFA results are dependent on the device/calibration used to obtain CGM data, but sensor location has little impact. Future studies of glucose complexity should consider the findings presented here when designing their investigations. [less ▲]

Detailed reference viewed: 38 (4 ULg)
Full Text
Peer Reviewed
See detailCumulative time in band (cTIB): glycemic level, variability and patient outcome all in one
Penning, Sophie ULg; Signal, Matthew; PREISER, Jean-Charles ULg et al

Conference (2012, October 15)

Detailed reference viewed: 31 (2 ULg)
Full Text
Peer Reviewed
See detailCumulative time in band: glycemic level, variability and patient outcome vs. mortality
Penning, Sophie ULg; Signal, Matthew; PREISER, Jean-Charles ULg et al

Poster (2012, October)

Detailed reference viewed: 19 (0 ULg)
Full Text
Peer Reviewed
See detailCumulative Time in Band (cTIB): Glycemic Level, Variability and Patient Outcome All in 1
Penning, Sophie ULg; Signal, Matthew; PREISER, Jean-Charles ULg et al

in Intensive Care Medicine (2012, October), 38 (Suppl 1)

Detailed reference viewed: 45 (4 ULg)
Full Text
Peer Reviewed
See detailDoes Tight Glycemic Control positively impact on patient mortality?
Penning, Sophie ULg; Le Compte, Aaron J.; Signal, Matthew et al

Poster (2012, March 20)

Detailed reference viewed: 20 (5 ULg)
Full Text
Peer Reviewed
See detailDoes Tight Glycemic Control positively impact on patient mortality?
Penning, Sophie ULg; Le Compte, Aaron J.; Signal, Matthew et al

in Critical Care (2012, March 20)

Detailed reference viewed: 20 (4 ULg)
Full Text
Peer Reviewed
See detailPilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control.
Evans, Alicia; Shaw, Geoffrey M; Le Compte, Aaron et al

in Annals of intensive care (2011), 1

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly ... [more ▼]

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) < 4.0 mmol/L. This research assesses the safety, efficacy, and clinical burden of a STAR TGC controller modulating both insulin and nutrition inputs in pilot trials. METHODS: Seven patients covering 660 hours. Insulin and nutrition interventions are given 1-3 hourly as chosen by the nurse to allow them to manage workload. Interventions are calculated by using clinically validated computer models of human metabolism and its variability in critical illness to maximize the overlap of the model-predicted (5-95th percentile) range of BG outcomes with the 4.0-6.5 mmol/L band while ensuring a maximum 5% risk of BG < 4.0 mmol/L. Carbohydrate intake (all sources) was selected to maximize intake up to 100% of SCCM/ACCP goal (25 kg/kcal/h). Maximum insulin doses and dose changes were limited for safety. Measurements were made with glucometers. Results are compared to those for the SPRINT study, which reduced mortality 25-40% for length of stay >/=3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. RESULTS: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. CONCLUSIONS: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT. [less ▲]

Detailed reference viewed: 21 (10 ULg)