The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity

2021 ◽  
pp. 193229682110216
Author(s):  
Nicholas Lam ◽  
Rua Murray ◽  
Paul D. Docherty ◽  
Lisa Te Morenga ◽  
J. Geoffrey Chase
Keyword(s):  
Insulin Sensitivity ◽  
Compartment Model ◽  
Monte Carlo Analysis ◽  
Mixing Model ◽  
Detection Methods ◽  
Test Model ◽  
Incomplete Mixing ◽  
Sensitivity Estimation ◽  
Data Points ◽  
Modelling Data

Background: The identification of insulin sensitivity in glycemic modelling can be heavily obstructed by the presence of outlying data or unmodelled effects. The effect of data indicative of local mixing is especially problematic with models assuming rapid mixing of compartments. Methods such as manual removal of data and outlier detection methods have been used to improve parameter ID in these cases, but modelling data with more compartments is another potential approach. Methods: This research compares a mixing model with local depot site compartments with an existing, clinically validated insulin sensitivity test model. The Levenberg-Marquardt (LM) parameter identification method was implemented alongside a modified version (aLM) capable of operator-independent omission of outlier data in accordance with the 3 standard deviation rule. Three cases were tested: LM where data points suspected to be affected by incomplete mixing at the depot site were removed, aLM, and LM with the more complex mixing model. Results: While insulin parameters identified in the mixing model differed greatly from those in the DISST model, there were strong Spearman correlations of approximately 0.93 for the insulin sensitivity values identified across all 3 methods. The 2 models also showed comparable identification stability in insulin sensitivity estimation through a Monte Carlo analysis. However, the mixing model required modifications to the identification process to improve convergence, and still failed to converge to feasible parameters on 5 of the 212 trials. Conclusions: The mixing compartment model effectively captured the dynamics of mixing behavior, but with no significant improvement in insulin sensitivity identification.

Drilling in the Digital Age: Machine Learning Assisted Bit Selection and Optimization

2021 ◽  
Author(s):  
Peter Batruny ◽  
Hafiz Zubir ◽  
Pete Slagel ◽  
Hanif Yahya ◽  
Zahid Zakaria ◽  
...  
Keyword(s):  
Machine Learning ◽  
Monte Carlo Analysis ◽  
Specific Model ◽  
Planning Decisions ◽  
Drilling Performance ◽  
Weight On Bit ◽  
Data Points ◽  
Ideal Tool ◽  
Rotary Speed ◽  
Artificial Neural Network Ann

Abstract Conventionally, a bit is selected from offset well bit run summaries. This method of selection is not always accurate since each bit is run under different conditions which might not be reflected in an offset study analysis. The large quantities of data generated from real time measurements in offset wells makes machine learning the ideal tool for analysis and comparison. Artificial Neural Network (ANN) is a relatively simple machine learning tool that combines inputs and calculation layers to compute a specified output layer. The ANN is fed over thousands of data points from 17-1/2 in hole sections across multiple wells. A specific model is then trained for every bit with weight on bit (WOB), rotary speed (RPM), bit hydraulics, and lithological properties as inputs and rate of penetration (ROP) as output. The model is finalized when a satisfactory statistical set of KPI's are achieved. Using a combination of Monte-Carlo analysis and sensitivity analysis, different bits are compared by varying parameters for the same bit and varying the bit under the same parameters. A bit and its optimized parameters are proposed, resulting in an average instantaneous ROP improvement of 32%. Performance benchmarked with individual drilling parameters shows improved ROP response to WOB, RPM, and bit hydraulics in the optimized run. This project solidifies machine learning as a powerful tool for bit selection and parameter optimization to improve drilling performance. Machine learning will become a significant part of well planning, design, and operations in the future. This study demonstrates how ANN's can be used to learn from previous operations and influence planning decisions to improve bit performance.

scholarly journals Evidence for altered control of glucose disposal after total colectomy

2000 ◽  
Vol 84 (6) ◽  
pp. 813-819 ◽  
Author(s):  
M. Denise Robertson ◽  
Geoff Livesey ◽  
Shelagh M. Hampton ◽  
John C. Mathers
Keyword(s):  
Insulin Sensitivity ◽  
Insulin Resistance Syndrome ◽  
Compartment Model ◽  
Short Chain Fatty Acids ◽  
Postprandial Glucose ◽  
Insulin Concentration ◽  
Glucose Disposal ◽  
Control Group ◽  
Plasma Insulin Concentration ◽  
Steady State Kinetics

Colonic fermentation of organic matter to short-chain fatty acids has been implicated in the improvement in insulin sensitivity achieved by feeding diets rich in complex carbohydrates. The present study assessed the potential role of the colon in determining postprandial glucose kinetics. Metabolic responses to a complex-carbohydrate test meal were determined in conjunction with a primed continuous infusion of D-[6,6-2H]glucose in a group of ileostomists and sex-matched controls. Glucose disposal (GD) was computed using non-steady-state kinetics on a single compartment model. Insulin sensitivity was derived using cumulative GD as the dependent variable, and time and the integrated insulin concentration as independent variables. The ileostomist group had a significantly higher postprandial plasma insulin concentration (P=0·034) compared with the control group, but no difference in the plasma glucose concentration. Total GD was similar in each group, although the insulin-dependent GD was substantially lower in the ileostomists (0·46 v. 0·13 mg glucose/min per pmol, P=0·015). The ileostomist group also showed a 50 % lower rate of glucose oxidation in the postprandial period (P=0·005), although the rate of non-oxidative GD was not significantly affected. The present study indicates that loss of the colon is associated with several characteristics of the insulin resistance syndrome, and favours a view that the colon has a role in the control of postprandial glucose.

Modified protocols improve insulin sensitivity estimation using the minimal model

1987 ◽  
Vol 253 (6) ◽  
pp. E595-E602 ◽  
Author(s):  
Y. J. Yang ◽  
J. H. Youn ◽  
R. N. Bergman
Keyword(s):  
Standard Deviation ◽  
Insulin Sensitivity ◽  
Minimal Model ◽  
Insulin Response ◽  
Tolerance Test ◽  
Intravenous Glucose Tolerance Test ◽  
Improve Insulin Sensitivity ◽  
Intravenous Glucose ◽  
Sensitivity Estimation ◽  
Model Technique

We attempted to improve the precision of the estimation of insulin sensitivity (S1) from the minimal model technique by modifying insulin dynamics during a frequently sampled intravenous glucose tolerance test (FSIGT). Tolbutamide and somatostatin (SRIF) were used to change the insulin dynamics without directly affecting insulin sensitivity. Injection of tolbutamide (100 mg) at t = 20 min provoked an immediate secondary peak in insulin response, resulting in a greater integrated incremental insulin than the standard FSIGT. SRIF, injected at t = -1 min, delayed insulin secretion in proportion to the dose without any change in magnitude. Computer simulation was used to assess the precision of S1 estimation. Insulin dynamics from both standard and modified protocols were adjusted in magnitude, with the shape unchanged and analyzed to determine the effect of the magnitude of insulin response. Fractional standard deviation was reduced from 73% with the standard insulin profile to 23% with tolbutamide and 18% with the highest dose of SRIF. In addition, the fractional standard deviation of S1 estimates decreased exponentially with increasing magnitude of insulin response. Modified FSIGTs require a smaller insulin response than the standard protocol to achieve the same precision.

Models for tagging data that allow for incomplete mixing of newly tagged animals

1998 ◽  
Vol 55 (6) ◽  
pp. 1477-1483 ◽  
Author(s):  
John M Hoenig ◽  
Nicholas J Barrowman ◽  
Kenneth H Pollock ◽  
Elizabeth N Brooks ◽  
William S Hearn ◽  
...  
Keyword(s):  
Recovery Rate ◽  
Survival Rates ◽  
Mortality Rates ◽  
Mixing Model ◽  
Natural Mortality ◽  
Fishing Season ◽  
Total Survival ◽  
Incomplete Mixing ◽  
Tag Shedding

The Brownie models for tagging data allow one to estimate age- and year-specific total survival rates as well as tag recovery rate parameters. The latter can provide estimates of exploitation rates if the tag reporting, tag shedding, and tag-induced mortality rates can be estimated. A limitation of the models is that they do not allow for newly tagged animals to have different survival rates than previously tagged animals because of lack of complete mixing. We develop a model that allows for the animals to be incompletely mixed, or not fully recruited, into the population during the entire year in which they are tagged. There is a penalty in terms of precision associated with the use of this model. To increase the precision, we also developed a model for which it is assumed that animals become fully mixed (recruited) after a portion of the year has elapsed. Sometimes, animals must be tagged after the fishing season has begun. In this case, newly tagged animals experience fishing and natural mortality for only a fraction of the year. The partial-year non-mixing model can be modified to account for this situation.

scholarly journals 1540. A Population Pharmacokinetic Model for Vancomycin in Korean Patients Receiving Extracorporeal Membrane Oxygenation Therapy: A Prospective Study

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S562-S562
Author(s):  
Younghee Jung ◽  
Dong-Hwan Lee ◽  
Hyoung Soo Kim
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Creatinine Clearance ◽  
Compartment Model ◽  
Volume Of Distribution ◽  
Population Pharmacokinetic ◽  
Test Model ◽  
Mixed Effect ◽  
Korean Patients ◽  
Membrane Oxygenation ◽  
Population Pk

Abstract Background There is no literature on population pharmacokinetics (PK) of vancomycin in Korean patients receiving extracorporeal membrane oxygenation (ECMO) therapy. The aim of this study was to develop a population PK model for vancomycin in Korean ECMO patients. Methods We prospectively enrolled adult patients who were undergoing ECMO and receiving vancomycin from July 2018 to April 2019. After initial dose of vancomycin was administrated, serial blood samples (seven to nine times per patient) were drawn before the next dose. A population PK model for vancomycin was developed using a nonlinear mixed-effect modeling. Age, sex, creatinine clearance, and body weight were tested as potential covariates in the model. Model selection was based on log-likelihood test, model diagnostic plots, and clinical plausibility. Results Fourteen patients were included over the period. Ten received venovenous, three venoarterial, and one both type ECMO. Eleven were men and the median age was 54 (interquartile range 45–66.3). Mean estimated glomerular filtration rate (eGFR) was 69 ± 46 mL/minute/1.73m2 by the modification of diet in renal disease equation. A total of 123 vancomycin concentrations from the patients were included in the analysis. The population PK of vancomycin was best described by a two-compartment model with a proportional residual error model. The typical value (%between-subject variability) for total clearance was estimated to be 4.33 L/h (21.6%), central volume of distribution was 9.22 L, the intercompartmental clearance was 10.75 L/hr (34.9%) and the peripheral volume of distribution was 19.6 L (26.6%). The proportional residual variability was 8.81%. Creatinine clearance significantly influenced vancomycin clearance (CL). The proposed equation to estimate vancomycin clearance in Korean ECMO patients was CL = 4.33 + 0.199 × (eGFR – 56). Conclusion A two-compartment population PK model successfully describes vancomycin PK profiles in Korean ECMO patients. The model could be used to optimize the dosing regimen if more data become available from currently ongoing clinical study. Disclosures All authors: No reported disclosures.

scholarly journals Undermodeling affects minimal model indexes: insights from a two-compartment model

1999 ◽  
Vol 276 (6) ◽  
pp. E1171-E1193 ◽  
Author(s):  
Andrea Caumo ◽  
Paolo Vicini ◽  
Jeffrey J. Zachwieja ◽  
Angelo Avogaro ◽  
Kevin Yarasheski ◽  
...  
Keyword(s):  
Insulin Sensitivity ◽  
Minimal Model ◽  
Compartment Model ◽  
Minimal Models ◽  
Intravenous Glucose ◽  
Glucose Effectiveness ◽  
Glucose Disappearance ◽  
Single Compartment ◽  
Two Compartment Model ◽  
The Impact

The classic (hereafter cold) and the labeled (hereafter hot) minimal models are powerful tools to investigate glucose metabolism. The cold model provides, from intravenous glucose tolerance test (IVGTT) data, indexes of glucose effectiveness (SG) and insulin sensitivity (SI) that measure the effect of glucose and insulin, respectively, to enhance glucose disappearance and inhibit endogenous glucose production. The hot model provides, from hot IVGTT data, indexes of glucose effectiveness ([Formula: see text]) and insulin sensitivity ([Formula: see text]) that, respectively, measure the effects of glucose and insulin on glucose disappearance only. Recent reports call for a reexamination of some of the assumptions of the minimal models. We have previously pointed out the criticality of the single-compartment description of glucose kinetics on which both the minimal models are founded. In this paper we evaluate the impact of single-compartment undermodeling on SG, SI,[Formula: see text], and[Formula: see text] by using a two-compartment model to describe the glucose system. The relationships of the minimal model indexes to the analogous indexes measured with the glucose clamp technique are also examined. Theoretical analysis and simulation studies indicate that cold indexes are more affected than hot indexes by undermodeling. In particular, care must be exercised in the physiological interpretation of SG, because this index is a local descriptor of events taking place in the initial portion of the IVGTT. As a consequence, SG not only reflects glucose effect on glucose uptake and production but also the rapid exchange of glucose between the accessible and nonaccessible glucose pools that occurs in the early part of the test.

scholarly journals Minimal model SGoverestimation and SIunderestimation: improved accuracy by a Bayesian two-compartment model

1999 ◽  
Vol 277 (3) ◽  
pp. E481-E488 ◽  
Author(s):  
Claudio Cobelli ◽  
Andrea Caumo ◽  
Matteo Omenetto
Keyword(s):  
Insulin Sensitivity ◽  
Minimal Model ◽  
Compartment Model ◽  
Tolerance Test ◽  
Intravenous Glucose Tolerance Test ◽  
Glucose Kinetics ◽  
Intravenous Glucose ◽  
Glucose Effectiveness ◽  
Normal Humans ◽  
Improved Accuracy

The intravenous glucose tolerance test (IVGTT) single-compartment minimal model (1CMM) method has recently been shown to overestimate glucose effectiveness and underestimate insulin sensitivity. Undermodeling, i.e., use of single- instead of two-compartment description of glucose kinetics, has been advocated to explain these limitations. We describe a new two-compartment minimal model (2CMM) into which we incorporate certain available knowledge on glucose kinetics. 2CMM is numerically identified using a Bayesian approach. Twenty-two standard IVGTT (0.30 g/kg) in normal humans were analyzed. In six subjects, the clamp-based index of insulin sensitivity ([Formula: see text]) was also measured. 2CMM glucose effectiveness ([Formula: see text]) and insulin sensitivity ([Formula: see text]) were, respectively, 60% lower ( P < 0.0001) and 35% higher ( P < 0.0001) than the corresponding 1CMM [Formula: see text] and[Formula: see text] indexes: 2.81 ± 0.29 (SE) vs.[Formula: see text] = 4.27 ± 0.33 ml ⋅ min−1 ⋅ kg−1and [Formula: see text] = 11.67 ± 1.71 vs.[Formula: see text] = 8.68 ± 1.62 102ml ⋅ min−1 ⋅ kg−1per μU/ml. [Formula: see text] was not different from[Formula: see text] = 12.61 ± 2.13 102ml ⋅ min−1 ⋅ kg−1per μU/ml (nonsignificant), whereas [Formula: see text]was 60% lower ( P < 0.02). In conclusion, a new 2CMM has been presented that improves the accuracy of glucose effectiveness and insulin sensitivity estimates of the classic 1CMM from a standard IVGTT in normal humans.

scholarly journals Quantitative kinetics of glucose appearance and disposal following a13C-labelled starch-rich meal: comparison of male and female subjects

2002 ◽  
Vol 87 (6) ◽  
pp. 569-577 ◽  
Author(s):  
M. Denise Robertson ◽  
Geoff Livesey ◽  
John C. Mathers
Keyword(s):  
Insulin Sensitivity ◽  
Indirect Calorimetry ◽  
Compartment Model ◽  
Whole Body ◽  
Glucose Disposal ◽  
Female Group ◽  
Acute Response ◽  
Fractional Rate ◽  
Steady State Kinetics ◽  
The Uk

In the UK, starch contributes up to 25 % of energy intake in adults (). The present study investigated the acute response to a starchy meal on whole-body glucose metabolism and assessed insulin sensitivity in men compared with women. Low insulin sensitivity has been postulated to pre-dispose individuals to a cluster of associated abnormalities known to increase the risk of CHD. Metabolic responses to a13C-labelled meal were determined in conjunction with a primed continuous infusion of D-[6,6-2H]glucose in groups of healthy age- and BMI-matched men and women. Peripheral plasma glucose disposal (Gd) was computed using non-steady state kinetics in a single compartment model, simultaneously with determination of whole-body net glucose oxidation by indirect calorimetry. Insulin sensitivity was derived using cumulative Gd as the dependent variable, and time and the integrated insulin concentration as independent variables. The female group had the higher fractional rate of glucose appearance in plasma from starch (P=0·019) immediately after ingestion. Females also had a higher rate of plasma Gd and a significantly higher insulin-dependent Gd (6·8v.5·6 μg glucose/(min.kg) per pmol insulin,P<0·05) compared with the males. A smaller absolute pool of endogenous glucose in females allowed the rate of exogenous13CO2production to be significantly higher in the females (P=0·007) corresponding also to a significantly higher (P<0·05) carbohydrate oxidation rate obtained by indirect calorimetry. The present study suggests that during the ingestion of a starchy meal, females exhibit higher glucose flux and greater whole-body insulin sensitivity than males.

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