The Identifiable Virtual Patient Model: Comparison of Simulation and Clinical Closed-Loop Study Results

Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be...

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Published inJournal of diabetes science and technology Vol. 6; no. 2; pp. 371 - 379
Main Authors Kanderian, Sami S., Weinzimer, Stuart A., Steil, Garry M.
Format Journal Article
LanguageEnglish
Published United States Diabetes Technology Society 01.03.2012
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ISSN1932-2968
1932-3107
DOI10.1177/193229681200600223

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Abstract Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal. Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively. The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29). Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.
AbstractList Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal.BACKGROUNDOptimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal.Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively.METHODSTen adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively.The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29).RESULTSThe meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29).Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.CONCLUSIONSClosed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.
Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient. However, model simulation studies that evaluate changes to the control algorithm need to produce conclusions similar to those that would be obtained from a clinical study evaluating the same modification. We evaluated the ability of a low-order identifiable virtual patient (IVP) model to achieve this goal. Ten adult subjects (42.5 ± 11.5 years of age; 18.0 ± 13.5 years diabetes; 6.9 ± 0.8% hemoglobin A1c) previously characterized with the IVP model were studied following the procedures independently reported in a pediatric study assessing proportional-integral-derivative control with and without a 50% meal insulin bolus. Peak postprandial glucose levels with and without the meal bolus and use of supplemental carbohydrate to treat hypoglycemia were compared using two-way analysis of variance and chi-square tests, respectively. The meal bolus decreased the peak postprandial glucose levels in both the adult-simulation and pediatricclinical study (231 ± 38 standard deviation to 205 ± 33 mg/dl and 226 ± 51 to 194 ± 47 mg/dl, respectively; p = .0472). No differences were observed between the peak postprandial levels obtained in the two studies (clinical and simulation study not different, p = .57; interaction p = .83) or in the use of supplemental carbohydrate (3 occurrences in 17 patient days of closed-loop control in the clinical-pediatric study; 7 occurrences over 20 patient days in the adult-simulation study, p = .29). Closed-loop simulations using an IVP model can predict clinical study outcomes in patients studied independently from those used to develop the model.
Author Steil, Garry M.
Kanderian, Sami S.
Weinzimer, Stuart A.
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2012 Diabetes Technology Society 2012
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Snippet Optimizing a closed-loop insulin delivery algorithm for individuals with type 1 diabetes can be potentially facilitated by a mathematical model of the patient....
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StartPage 371
SubjectTerms Adolescent
Adult
Algorithms
Analysis of Variance
Biomarkers - blood
Blood Glucose - drug effects
Blood Glucose - metabolism
Chi-Square Distribution
Computer Simulation
Diabetes Mellitus, Type 1 - blood
Diabetes Mellitus, Type 1 - drug therapy
Dietary Carbohydrates - administration & dosage
Eating
Fasting - blood
Glycated Hemoglobin A - metabolism
Humans
Hypoglycemia - chemically induced
Hypoglycemic Agents - administration & dosage
Hypoglycemic Agents - adverse effects
Hypoglycemic Agents - blood
Insulin - administration & dosage
Insulin - adverse effects
Insulin - blood
Insulin Infusion Systems
Middle Aged
Models, Biological
Original
Patient Simulation
Postprandial Period
Reproducibility of Results
Time Factors
Title The Identifiable Virtual Patient Model: Comparison of Simulation and Clinical Closed-Loop Study Results
URI https://www.ncbi.nlm.nih.gov/pubmed/22538149
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