A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model
We introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms t...
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| Veröffentlicht in: | PloS one 2019-07, Vol.14 (7), p.e0217301-e0217301 |
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| creator | Resalat, Navid El Youssef, Joseph Tyler, Nichole Castle, Jessica Jacobs, Peter G |
| description | We introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms that are currently being used to help people with T1D better manage their glucose levels. We present validation results comparing these virtual patients with true clinical patients undergoing AP control and demonstrate that the virtual patients behave similarly to people with T1D.
A single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70-180 mg/dL), time in hypoglycemia (180 mg/dL) for both the virtual patients and the actual patients.
The subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, |
| doi_str_mv | 10.1371/journal.pone.0217301 |
| format | Article |
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A single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70-180 mg/dL), time in hypoglycemia (<70 mg/dL) and time in hyperglycemia (>180 mg/dL) for both the virtual patients and the actual patients.
The subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, the difference was not statistically significant.
We have verified that a SH-VPP and a DH-VPP performed comparably with actual patients undergoing AP control using an identical control algorithm. The SH-VPP and DH-VPP may be used as a simulator for pre-evaluation of T1D control algorithms.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0217301</identifier><identifier>PMID: 31344037</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Aged ; Algorithms ; Artificial pancreas ; Biology and Life Sciences ; Biomedical engineering ; Blood Glucose - metabolism ; Body weight ; Carbohydrates ; Care and treatment ; Computer simulation ; Control algorithms ; Control theory ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (insulin dependent) ; Diabetes Mellitus, Type 1 - blood ; Diabetes Mellitus, Type 1 - drug therapy ; Diabetes Mellitus, Type 1 - physiopathology ; Diabetes therapy ; Differential equations ; Endocrinology ; Engineering ; Exercise ; Female ; Glucagon ; Glucagon - blood ; Glucose ; Hormones ; Humans ; Hyperglycemia ; Hypoglycemia ; Insulin ; Insulin - blood ; Insulin - therapeutic use ; Kinetics ; Male ; Mathematical models ; Meals ; Medical research ; Medicine and Health Sciences ; Metabolism ; Middle Aged ; Models, Biological ; Normal distribution ; Pancreas ; Parameter sensitivity ; Patient simulation ; Patients ; Physical Sciences ; Physiology ; Population (statistical) ; Research and Analysis Methods ; Sensitivity ; Sensitivity analysis ; Sensors ; Statistical analysis ; Studies ; Type 1 diabetes</subject><ispartof>PloS one, 2019-07, Vol.14 (7), p.e0217301-e0217301</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Resalat et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Resalat et al 2019 Resalat et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-f757260cd1568f4cc0957946cf0296c05908955b71a7d521f6fc6b38d15df5363</citedby><cites>FETCH-LOGICAL-c692t-f757260cd1568f4cc0957946cf0296c05908955b71a7d521f6fc6b38d15df5363</cites><orcidid>0000-0003-0893-7941</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657828/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6657828/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23847,27903,27904,53769,53771,79346,79347</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31344037$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Palumbo, Pasquale</contributor><creatorcontrib>Resalat, Navid</creatorcontrib><creatorcontrib>El Youssef, Joseph</creatorcontrib><creatorcontrib>Tyler, Nichole</creatorcontrib><creatorcontrib>Castle, Jessica</creatorcontrib><creatorcontrib>Jacobs, Peter G</creatorcontrib><title>A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms that are currently being used to help people with T1D better manage their glucose levels. We present validation results comparing these virtual patients with true clinical patients undergoing AP control and demonstrate that the virtual patients behave similarly to people with T1D.
A single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70-180 mg/dL), time in hypoglycemia (<70 mg/dL) and time in hyperglycemia (>180 mg/dL) for both the virtual patients and the actual patients.
The subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, the difference was not statistically significant.
We have verified that a SH-VPP and a DH-VPP performed comparably with actual patients undergoing AP control using an identical control algorithm. The SH-VPP and DH-VPP may be used as a simulator for pre-evaluation of T1D control algorithms.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Artificial pancreas</subject><subject>Biology and Life Sciences</subject><subject>Biomedical engineering</subject><subject>Blood Glucose - metabolism</subject><subject>Body weight</subject><subject>Carbohydrates</subject><subject>Care and treatment</subject><subject>Computer simulation</subject><subject>Control algorithms</subject><subject>Control theory</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (insulin dependent)</subject><subject>Diabetes Mellitus, Type 1 - blood</subject><subject>Diabetes Mellitus, Type 1 - drug therapy</subject><subject>Diabetes Mellitus, Type 1 - physiopathology</subject><subject>Diabetes therapy</subject><subject>Differential equations</subject><subject>Endocrinology</subject><subject>Engineering</subject><subject>Exercise</subject><subject>Female</subject><subject>Glucagon</subject><subject>Glucagon - blood</subject><subject>Glucose</subject><subject>Hormones</subject><subject>Humans</subject><subject>Hyperglycemia</subject><subject>Hypoglycemia</subject><subject>Insulin</subject><subject>Insulin - blood</subject><subject>Insulin - therapeutic use</subject><subject>Kinetics</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Meals</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Metabolism</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><subject>Normal distribution</subject><subject>Pancreas</subject><subject>Parameter sensitivity</subject><subject>Patient simulation</subject><subject>Patients</subject><subject>Physical Sciences</subject><subject>Physiology</subject><subject>Population (statistical)</subject><subject>Research and Analysis Methods</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Sensors</subject><subject>Statistical analysis</subject><subject>Studies</subject><subject>Type 1 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statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model</title><author>Resalat, Navid ; El Youssef, Joseph ; Tyler, Nichole ; Castle, Jessica ; Jacobs, Peter G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c692t-f757260cd1568f4cc0957946cf0296c05908955b71a7d521f6fc6b38d15df5363</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Artificial pancreas</topic><topic>Biology and Life Sciences</topic><topic>Biomedical engineering</topic><topic>Blood Glucose - metabolism</topic><topic>Body weight</topic><topic>Carbohydrates</topic><topic>Care and treatment</topic><topic>Computer simulation</topic><topic>Control algorithms</topic><topic>Control theory</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (insulin 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One</addtitle><date>2019-07-25</date><risdate>2019</risdate><volume>14</volume><issue>7</issue><spage>e0217301</spage><epage>e0217301</epage><pages>e0217301-e0217301</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We introduce two validated single (SH) and dual hormone (DH) mathematical models that represent an in-silico virtual patient population (VPP) for type 1 diabetes (T1D). The VPP can be used to evaluate automated insulin and glucagon delivery algorithms, so-called artificial pancreas (AP) algorithms that are currently being used to help people with T1D better manage their glucose levels. We present validation results comparing these virtual patients with true clinical patients undergoing AP control and demonstrate that the virtual patients behave similarly to people with T1D.
A single hormone virtual patient population (SH-VPP) was created that is comprised of eight differential equations that describe insulin kinetics, insulin dynamics and carbohydrate absorption. The parameters in this model that represent insulin sensitivity were statistically sampled from a normal distribution to create a population of virtual patients with different levels of insulin sensitivity. A dual hormone virtual patient population (DH-VPP) extended this SH-VPP by incorporating additional equations to represent glucagon kinetics and glucagon dynamics. The DH-VPP is comprised of thirteen differential equations and a parameter representing glucagon sensitivity, which was statistically sampled from a normal distribution to create virtual patients with different levels of glucagon sensitivity. We evaluated the SH-VPP and DH-VPP on a clinical data set of 20 people with T1D who participated in a 3.5-day outpatient AP study. Twenty virtual patients were matched with the 20 clinical patients by total daily insulin requirements and body weight. The identical meals given during the AP study were given to the virtual patients and the identical AP control algorithm that was used to control the glucose of the virtual patients was used on the clinical patients. We compared percent time in target range (70-180 mg/dL), time in hypoglycemia (<70 mg/dL) and time in hyperglycemia (>180 mg/dL) for both the virtual patients and the actual patients.
The subjects in the SH-VPP performed similarly vs. the actual patients (time in range: 78.1 ± 5.1% vs. 74.3 ± 8.1%, p = 0.11; time in hypoglycemia: 3.4 ± 1.3% vs. 2.8 ± 1.7%, p = 0.23). The subjects in the DH-VPP also performed similarly vs. the actual patients (time in range: 75.6 ± 5.5% vs. 71.9 ± 10.9%, p = 0.13; time in hypoglycemia: 0.9 ± 0.8% vs. 1.3 ± 1%, p = 0.19). While the VPPs tended to over-estimate the time in range relative to actual patients, the difference was not statistically significant.
We have verified that a SH-VPP and a DH-VPP performed comparably with actual patients undergoing AP control using an identical control algorithm. The SH-VPP and DH-VPP may be used as a simulator for pre-evaluation of T1D control algorithms.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31344037</pmid><doi>10.1371/journal.pone.0217301</doi><tpages>e0217301</tpages><orcidid>https://orcid.org/0000-0003-0893-7941</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-07, Vol.14 (7), p.e0217301-e0217301 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2264435209 |
| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; PubMed Central; Free Full-Text Journals in Chemistry; EZB Electronic Journals Library |
| subjects | Adult Aged Algorithms Artificial pancreas Biology and Life Sciences Biomedical engineering Blood Glucose - metabolism Body weight Carbohydrates Care and treatment Computer simulation Control algorithms Control theory Diabetes Diabetes mellitus Diabetes mellitus (insulin dependent) Diabetes Mellitus, Type 1 - blood Diabetes Mellitus, Type 1 - drug therapy Diabetes Mellitus, Type 1 - physiopathology Diabetes therapy Differential equations Endocrinology Engineering Exercise Female Glucagon Glucagon - blood Glucose Hormones Humans Hyperglycemia Hypoglycemia Insulin Insulin - blood Insulin - therapeutic use Kinetics Male Mathematical models Meals Medical research Medicine and Health Sciences Metabolism Middle Aged Models, Biological Normal distribution Pancreas Parameter sensitivity Patient simulation Patients Physical Sciences Physiology Population (statistical) Research and Analysis Methods Sensitivity Sensitivity analysis Sensors Statistical analysis Studies Type 1 diabetes |
| title | A statistical virtual patient population for the glucoregulatory system in type 1 diabetes with integrated exercise model |
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