Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4
Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Adults (≥18 years of age) with typ...
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| Veröffentlicht in: | Diabetes technology & therapeutics 2022-09, Vol.24 (9), p.635-642 |
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| creator | Pinsker, Jordan E Dassau, Eyal Deshpande, Sunil Raghinaru, Dan Buckingham, Bruce A Kudva, Yogish C Laffel, Lori M Levy, Carol J Church, Mei Mei Desrochers, Hannah Ekhlaspour, Laya Kaur, Ravinder Jeet Levister, Camilla Shi, Dawei Lum, John W Kollman, Craig Doyle, Francis J |
| description | Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings.
Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome.
Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%],
= 0.22). Median time |
| doi_str_mv | 10.1089/dia.2022.0084 |
| format | Article |
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Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome.
Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%],
= 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%],
= 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks.
Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.</description><identifier>ISSN: 1520-9156</identifier><identifier>EISSN: 1557-8593</identifier><identifier>DOI: 10.1089/dia.2022.0084</identifier><identifier>PMID: 35549708</identifier><language>eng</language><publisher>United States: Mary Ann Liebert, Inc</publisher><subject>Adaptation ; Adult ; Automation ; Blood Glucose ; Case reports ; Closed loop systems ; Cross-Over Studies ; Diabetes ; Diabetes Mellitus, Type 1 - drug therapy ; Glucose monitoring ; Humans ; Hypoglycemic Agents - therapeutic use ; Infant, Newborn ; Infusion pumps ; Insulin ; Insulin - therapeutic use ; Insulin Infusion Systems ; Insulin, Regular, Human - therapeutic use ; Medical technology ; Middle Aged ; Original ; Outpatients ; Sensors ; Young Adult</subject><ispartof>Diabetes technology & therapeutics, 2022-09, Vol.24 (9), p.635-642</ispartof><rights>Copyright Mary Ann Liebert, Inc. Sep 2022</rights><rights>Copyright 2022, Mary Ann Liebert, Inc., publishers 2022 Mary Ann Liebert, Inc., publishers</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-722a5a5ff283ec5a5885687813efbb624c1c1800076870a1a4b123d7e3a8d9593</citedby><cites>FETCH-LOGICAL-c415t-722a5a5ff283ec5a5885687813efbb624c1c1800076870a1a4b123d7e3a8d9593</cites><orcidid>0000-0002-3293-9114 ; 0000-0003-2613-4966 ; 0000-0003-0285-6622 ; 0000-0002-9675-3001 ; 0000-0002-0006-4376 ; 0000-0001-5333-6892 ; 0000-0002-3263-1419</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35549708$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pinsker, Jordan E</creatorcontrib><creatorcontrib>Dassau, Eyal</creatorcontrib><creatorcontrib>Deshpande, Sunil</creatorcontrib><creatorcontrib>Raghinaru, Dan</creatorcontrib><creatorcontrib>Buckingham, Bruce A</creatorcontrib><creatorcontrib>Kudva, Yogish C</creatorcontrib><creatorcontrib>Laffel, Lori M</creatorcontrib><creatorcontrib>Levy, Carol J</creatorcontrib><creatorcontrib>Church, Mei Mei</creatorcontrib><creatorcontrib>Desrochers, Hannah</creatorcontrib><creatorcontrib>Ekhlaspour, Laya</creatorcontrib><creatorcontrib>Kaur, Ravinder Jeet</creatorcontrib><creatorcontrib>Levister, Camilla</creatorcontrib><creatorcontrib>Shi, Dawei</creatorcontrib><creatorcontrib>Lum, John W</creatorcontrib><creatorcontrib>Kollman, Craig</creatorcontrib><creatorcontrib>Doyle, Francis J</creatorcontrib><creatorcontrib>iDCL Trial Research Group</creatorcontrib><creatorcontrib>for the iDCL Trial Research Group</creatorcontrib><title>Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4</title><title>Diabetes technology & therapeutics</title><addtitle>Diabetes Technol Ther</addtitle><description>Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings.
Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome.
Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%],
= 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%],
= 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks.
Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.</description><subject>Adaptation</subject><subject>Adult</subject><subject>Automation</subject><subject>Blood Glucose</subject><subject>Case reports</subject><subject>Closed loop systems</subject><subject>Cross-Over Studies</subject><subject>Diabetes</subject><subject>Diabetes Mellitus, Type 1 - drug therapy</subject><subject>Glucose monitoring</subject><subject>Humans</subject><subject>Hypoglycemic Agents - therapeutic use</subject><subject>Infant, Newborn</subject><subject>Infusion pumps</subject><subject>Insulin</subject><subject>Insulin - therapeutic use</subject><subject>Insulin Infusion Systems</subject><subject>Insulin, Regular, Human - therapeutic use</subject><subject>Medical technology</subject><subject>Middle Aged</subject><subject>Original</subject><subject>Outpatients</subject><subject>Sensors</subject><subject>Young Adult</subject><issn>1520-9156</issn><issn>1557-8593</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkk1v1DAQhiMEoqVw5Ioscekli-182OGAtNrlo9KiVqWAxMXyJpPWxbGD7Sxa_i5_hAktFXDyyPP4nfHMm2VPGV0wKpsXndELTjlfUCrLe9khqyqRy6op7s8xp3nDqvogexTjNaVUFJw9zA6KqiobQeVh9vN0SqNOBlwi59p1fjA_oCOr4GP0Owhk5YdRBxO9I74nX7wD8t53YMlZgM60yewAGZeCt2Q5JT_ohO9PXJyscWQNFoGwJ99NuiKfAb7aPVnrpMk6YMKRZafHhPVR_hOEOEXyAVz0IV9OlwM2hVpn0zC-JOeAiimSPviBpCvAEgmC-_1UW7I2egsJIllZH6HLN96P5CIYTJWPswe9thGe3J5H2cc3ry9W7_LN6duT1XKTtyWrUi4415Wu-p7LAlqMpKxqKSQroN9ua162rGVyHiLeUs10uWW86AQUWnYNTvwoe3WjO07bAboW2w_aqjGYQYe98tqofzPOXKlLv1NNybloGAoc3woE_22CmNRgYgvWagd-iorXdSlkUwqB6PP_0Gs_4TgsUrhYUdeMU6TyG6qd9xmgv2uGUTXbR6F91GwfNdsH-Wd__-CO_uOX4hcE1cYT</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Pinsker, Jordan E</creator><creator>Dassau, Eyal</creator><creator>Deshpande, Sunil</creator><creator>Raghinaru, Dan</creator><creator>Buckingham, Bruce A</creator><creator>Kudva, Yogish C</creator><creator>Laffel, Lori M</creator><creator>Levy, Carol J</creator><creator>Church, Mei Mei</creator><creator>Desrochers, Hannah</creator><creator>Ekhlaspour, Laya</creator><creator>Kaur, Ravinder Jeet</creator><creator>Levister, Camilla</creator><creator>Shi, Dawei</creator><creator>Lum, John W</creator><creator>Kollman, Craig</creator><creator>Doyle, Francis J</creator><general>Mary Ann Liebert, Inc</general><general>Mary Ann Liebert, Inc., publishers</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3293-9114</orcidid><orcidid>https://orcid.org/0000-0003-2613-4966</orcidid><orcidid>https://orcid.org/0000-0003-0285-6622</orcidid><orcidid>https://orcid.org/0000-0002-9675-3001</orcidid><orcidid>https://orcid.org/0000-0002-0006-4376</orcidid><orcidid>https://orcid.org/0000-0001-5333-6892</orcidid><orcidid>https://orcid.org/0000-0002-3263-1419</orcidid></search><sort><creationdate>20220901</creationdate><title>Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4</title><author>Pinsker, Jordan E ; Dassau, Eyal ; Deshpande, Sunil ; Raghinaru, Dan ; Buckingham, Bruce A ; Kudva, Yogish C ; Laffel, Lori M ; Levy, Carol J ; Church, Mei Mei ; Desrochers, Hannah ; Ekhlaspour, Laya ; Kaur, Ravinder Jeet ; Levister, Camilla ; Shi, Dawei ; Lum, John W ; Kollman, Craig ; Doyle, Francis J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-722a5a5ff283ec5a5885687813efbb624c1c1800076870a1a4b123d7e3a8d9593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptation</topic><topic>Adult</topic><topic>Automation</topic><topic>Blood Glucose</topic><topic>Case reports</topic><topic>Closed loop systems</topic><topic>Cross-Over Studies</topic><topic>Diabetes</topic><topic>Diabetes Mellitus, Type 1 - drug therapy</topic><topic>Glucose monitoring</topic><topic>Humans</topic><topic>Hypoglycemic Agents - therapeutic use</topic><topic>Infant, Newborn</topic><topic>Infusion pumps</topic><topic>Insulin</topic><topic>Insulin - therapeutic use</topic><topic>Insulin Infusion Systems</topic><topic>Insulin, Regular, Human - therapeutic use</topic><topic>Medical technology</topic><topic>Middle Aged</topic><topic>Original</topic><topic>Outpatients</topic><topic>Sensors</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pinsker, Jordan E</creatorcontrib><creatorcontrib>Dassau, Eyal</creatorcontrib><creatorcontrib>Deshpande, Sunil</creatorcontrib><creatorcontrib>Raghinaru, Dan</creatorcontrib><creatorcontrib>Buckingham, Bruce A</creatorcontrib><creatorcontrib>Kudva, Yogish C</creatorcontrib><creatorcontrib>Laffel, Lori M</creatorcontrib><creatorcontrib>Levy, Carol J</creatorcontrib><creatorcontrib>Church, Mei Mei</creatorcontrib><creatorcontrib>Desrochers, Hannah</creatorcontrib><creatorcontrib>Ekhlaspour, Laya</creatorcontrib><creatorcontrib>Kaur, Ravinder Jeet</creatorcontrib><creatorcontrib>Levister, Camilla</creatorcontrib><creatorcontrib>Shi, Dawei</creatorcontrib><creatorcontrib>Lum, John W</creatorcontrib><creatorcontrib>Kollman, Craig</creatorcontrib><creatorcontrib>Doyle, Francis J</creatorcontrib><creatorcontrib>iDCL Trial Research Group</creatorcontrib><creatorcontrib>for the iDCL Trial Research Group</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Diabetes technology & therapeutics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pinsker, Jordan E</au><au>Dassau, Eyal</au><au>Deshpande, Sunil</au><au>Raghinaru, Dan</au><au>Buckingham, Bruce A</au><au>Kudva, Yogish C</au><au>Laffel, Lori M</au><au>Levy, Carol J</au><au>Church, Mei Mei</au><au>Desrochers, Hannah</au><au>Ekhlaspour, Laya</au><au>Kaur, Ravinder Jeet</au><au>Levister, Camilla</au><au>Shi, Dawei</au><au>Lum, John W</au><au>Kollman, Craig</au><au>Doyle, Francis J</au><aucorp>iDCL Trial Research Group</aucorp><aucorp>for the iDCL Trial Research Group</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4</atitle><jtitle>Diabetes technology & therapeutics</jtitle><addtitle>Diabetes Technol Ther</addtitle><date>2022-09-01</date><risdate>2022</risdate><volume>24</volume><issue>9</issue><spage>635</spage><epage>642</epage><pages>635-642</pages><issn>1520-9156</issn><eissn>1557-8593</eissn><abstract>Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings.
Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm. Each week, the AID insulin delivery settings were sequentially and automatically updated by an adaptation system running on the study phone. Primary outcome was sensor glucose time-in-range 70-180 mg/dL, with noninferiority in percent time below 54 mg/dL as a hierarchical outcome.
Thirty-five participants completed the trial (mean age 39 ± 16 years, HbA1c at enrollment 6.9% ± 1.0%). Mean time-in-range 70-180 mg/dL was 66% with SAP versus 69% with AID (mean adjusted difference +2% [95% confidence interval: -1% to +6%],
= 0.22). Median time <70 mg/dL improved from 3.0% with SAP to 1.6% with AID (-1.5% [-2.4% to -0.5%],
= 0.002). The adaptation system decreased initial basal rates by a median of 4% (-8%, 16%) and increased initial carbohydrate ratios by a median of 45% (32%, 59%) after 13 weeks.
Automated adaptation of insulin delivery settings with AID use did not significantly improve time-in-range in this very well-controlled population. Additional study and further refinement of the adaptation system are needed, especially in populations with differing degrees of baseline glycemic control, who may show larger benefits from adaptation.</abstract><cop>United States</cop><pub>Mary Ann Liebert, Inc</pub><pmid>35549708</pmid><doi>10.1089/dia.2022.0084</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-3293-9114</orcidid><orcidid>https://orcid.org/0000-0003-2613-4966</orcidid><orcidid>https://orcid.org/0000-0003-0285-6622</orcidid><orcidid>https://orcid.org/0000-0002-9675-3001</orcidid><orcidid>https://orcid.org/0000-0002-0006-4376</orcidid><orcidid>https://orcid.org/0000-0001-5333-6892</orcidid><orcidid>https://orcid.org/0000-0002-3263-1419</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
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| ispartof | Diabetes technology & therapeutics, 2022-09, Vol.24 (9), p.635-642 |
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| source | MEDLINE; Alma/SFX Local Collection |
| subjects | Adaptation Adult Automation Blood Glucose Case reports Closed loop systems Cross-Over Studies Diabetes Diabetes Mellitus, Type 1 - drug therapy Glucose monitoring Humans Hypoglycemic Agents - therapeutic use Infant, Newborn Infusion pumps Insulin Insulin - therapeutic use Insulin Infusion Systems Insulin, Regular, Human - therapeutic use Medical technology Middle Aged Original Outpatients Sensors Young Adult |
| title | Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4 |
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