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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report</title><author><name sortKey="Maahs, David M" sort="Maahs, David M" uniqKey="Maahs D" first="David M." last="Maahs">David M. Maahs</name><affiliation><nlm:aff id="aff1">Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO</nlm:aff></affiliation></author><author><name sortKey="Buckingham, Bruce A" sort="Buckingham, Bruce A" uniqKey="Buckingham B" first="Bruce A." last="Buckingham">Bruce A. Buckingham</name><affiliation><nlm:aff id="aff2">Division of Pediatric Endocrinology and Diabetes, Department of Pediatric Endocrinology, Stanford University, Stanford, CA</nlm:aff></affiliation></author><author><name sortKey="Castle, Jessica R" sort="Castle, Jessica R" uniqKey="Castle J" first="Jessica R." last="Castle">Jessica R. Castle</name><affiliation><nlm:aff id="aff3">Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR</nlm:aff></affiliation></author><author><name sortKey="Cinar, Ali" sort="Cinar, Ali" uniqKey="Cinar A" first="Ali" last="Cinar">Ali Cinar</name><affiliation><nlm:aff id="aff4">Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL</nlm:aff></affiliation></author><author><name sortKey="Damiano, Edward R" sort="Damiano, Edward R" uniqKey="Damiano E" first="Edward R." last="Damiano">Edward R. Damiano</name><affiliation><nlm:aff id="aff5">Department of Biomedical Engineering, Boston University, Boston, MA</nlm:aff></affiliation></author><author><name sortKey="Dassau, Eyal" sort="Dassau, Eyal" uniqKey="Dassau E" first="Eyal" last="Dassau">Eyal Dassau</name><affiliation><nlm:aff id="aff6">Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</nlm:aff></affiliation><affiliation><nlm:aff id="aff7">Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</nlm:aff></affiliation></author><author><name sortKey="Devries, J Hans" sort="Devries, J Hans" uniqKey="Devries J" first="J. Hans" last="Devries">J. Hans Devries</name><affiliation><nlm:aff id="aff8">Department of Endocrinology, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands</nlm:aff></affiliation></author><author><name sortKey="Doyle, Francis J" sort="Doyle, Francis J" uniqKey="Doyle F" first="Francis J." last="Doyle">Francis J. Doyle</name><affiliation><nlm:aff id="aff6">Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</nlm:aff></affiliation><affiliation><nlm:aff id="aff7">Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</nlm:aff></affiliation></author><author><name sortKey="Griffen, Steven C" sort="Griffen, Steven C" uniqKey="Griffen S" first="Steven C." last="Griffen">Steven C. Griffen</name><affiliation><nlm:aff id="aff9">JDRF, New York, NY</nlm:aff></affiliation></author><author><name sortKey="Haidar, Ahmad" sort="Haidar, Ahmad" uniqKey="Haidar A" first="Ahmad" last="Haidar">Ahmad Haidar</name><affiliation><nlm:aff id="aff10">Biomedical Engineering Department, McGill University, Montreal, Quebec, Canada</nlm:aff></affiliation><affiliation><nlm:aff id="aff11">Division of Endocrinology, McGill University, Montreal, Quebec, Canada</nlm:aff></affiliation></author><author><name sortKey="Heinemann, Lutz" sort="Heinemann, Lutz" uniqKey="Heinemann L" first="Lutz" last="Heinemann">Lutz Heinemann</name><affiliation><nlm:aff id="aff12">Profil Institute for Metabolic Research, Neuss, Germany</nlm:aff></affiliation></author><author><name sortKey="Hovorka, Roman" sort="Hovorka, Roman" uniqKey="Hovorka R" first="Roman" last="Hovorka">Roman Hovorka</name><affiliation><nlm:aff id="aff13">Wellcome Trust-Medical Research Clinical Institute of Metabolic Science, University of Cambridge, Cambridge, U.K.</nlm:aff></affiliation></author><author><name sortKey="Jones, Timothy W" sort="Jones, Timothy W" uniqKey="Jones T" first="Timothy W." last="Jones">Timothy W. Jones</name><affiliation><nlm:aff id="aff14">Telethon Kids Institute, The University of Western Australia, Perth, Australia</nlm:aff></affiliation></author><author><name sortKey="Kollman, Craig" sort="Kollman, Craig" uniqKey="Kollman C" first="Craig" last="Kollman">Craig Kollman</name><affiliation><nlm:aff id="aff15">Jaeb Center for Health Research, Tampa, FL</nlm:aff></affiliation></author><author><name sortKey="Kovatchev, Boris" sort="Kovatchev, Boris" uniqKey="Kovatchev B" first="Boris" last="Kovatchev">Boris Kovatchev</name><affiliation><nlm:aff id="aff16">University of Virginia, Charlottesville, VA</nlm:aff></affiliation></author><author><name sortKey="Levy, Brian L" sort="Levy, Brian L" uniqKey="Levy B" first="Brian L." last="Levy">Brian L. Levy</name><affiliation><nlm:aff id="aff17">Johnson & Johnson Diabetes Care Companies, Wayne, PA</nlm:aff></affiliation></author><author><name sortKey="Nimri, Revital" sort="Nimri, Revital" uniqKey="Nimri R" first="Revital" last="Nimri">Revital Nimri</name><affiliation><nlm:aff id="aff18">Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel</nlm:aff></affiliation></author><author><name sortKey="O Eal, David N" sort="O Eal, David N" uniqKey="O Eal D" first="David N." last="O Eal">David N. O Eal</name><affiliation><nlm:aff id="aff19">Department of Medicine, University of Melbourne, St. Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia</nlm:aff></affiliation></author><author><name sortKey="Philip, Moshe" sort="Philip, Moshe" uniqKey="Philip M" first="Moshe" last="Philip">Moshe Philip</name><affiliation><nlm:aff id="aff18">Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel</nlm:aff></affiliation></author><author><name sortKey="Renard, Eric" sort="Renard, Eric" uniqKey="Renard E" first="Eric" last="Renard">Eric Renard</name><affiliation><nlm:aff id="aff20">Department of Endocrinology, Diabetes, and Nutrition, Montpellier University Hospital, INSERM Clinical Investigation Centre 1411, Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France</nlm:aff></affiliation></author><author><name sortKey="Russell, Steven J" sort="Russell, Steven J" uniqKey="Russell S" first="Steven J." last="Russell">Steven J. Russell</name><affiliation><nlm:aff id="aff5">Department of Biomedical Engineering, Boston University, Boston, MA</nlm:aff></affiliation><affiliation><nlm:aff id="aff21">Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA</nlm:aff></affiliation></author><author><name sortKey="Weinzimer, Stuart A" sort="Weinzimer, Stuart A" uniqKey="Weinzimer S" first="Stuart A." last="Weinzimer">Stuart A. Weinzimer</name><affiliation><nlm:aff id="aff22">Yale University School of Medicine, New Haven, CT</nlm:aff></affiliation></author><author><name sortKey="Zisser, Howard" sort="Zisser, Howard" uniqKey="Zisser H" first="Howard" last="Zisser">Howard Zisser</name><affiliation><nlm:aff id="aff6">Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</nlm:aff></affiliation></author><author><name sortKey="Lum, John W" sort="Lum, John W" uniqKey="Lum J" first="John W." last="Lum">John W. Lum</name><affiliation><nlm:aff id="aff15">Jaeb Center for Health Research, Tampa, FL</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">27330126</idno><idno type="pmc">4915553</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4915553</idno><idno type="RBID">PMC:4915553</idno><idno type="doi">10.2337/dc15-2716</idno><date when="2016">2016</date><idno type="wicri:Area/Pmc/Corpus">000658</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000658</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report</title><author><name sortKey="Maahs, David M" sort="Maahs, David M" uniqKey="Maahs D" first="David M." last="Maahs">David M. Maahs</name><affiliation><nlm:aff id="aff1">Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO</nlm:aff></affiliation></author><author><name sortKey="Buckingham, Bruce A" sort="Buckingham, Bruce A" uniqKey="Buckingham B" first="Bruce A." last="Buckingham">Bruce A. Buckingham</name><affiliation><nlm:aff id="aff2">Division of Pediatric Endocrinology and Diabetes, Department of Pediatric Endocrinology, Stanford University, Stanford, CA</nlm:aff></affiliation></author><author><name sortKey="Castle, Jessica R" sort="Castle, Jessica R" uniqKey="Castle J" first="Jessica R." last="Castle">Jessica R. Castle</name><affiliation><nlm:aff id="aff3">Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR</nlm:aff></affiliation></author><author><name sortKey="Cinar, Ali" sort="Cinar, Ali" uniqKey="Cinar A" first="Ali" last="Cinar">Ali Cinar</name><affiliation><nlm:aff id="aff4">Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL</nlm:aff></affiliation></author><author><name sortKey="Damiano, Edward R" sort="Damiano, Edward R" uniqKey="Damiano E" first="Edward R." last="Damiano">Edward R. Damiano</name><affiliation><nlm:aff id="aff5">Department of Biomedical Engineering, Boston University, Boston, MA</nlm:aff></affiliation></author><author><name sortKey="Dassau, Eyal" sort="Dassau, Eyal" uniqKey="Dassau E" first="Eyal" last="Dassau">Eyal Dassau</name><affiliation><nlm:aff id="aff6">Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</nlm:aff></affiliation><affiliation><nlm:aff id="aff7">Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</nlm:aff></affiliation></author><author><name sortKey="Devries, J Hans" sort="Devries, J Hans" uniqKey="Devries J" first="J. Hans" last="Devries">J. Hans Devries</name><affiliation><nlm:aff id="aff8">Department of Endocrinology, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands</nlm:aff></affiliation></author><author><name sortKey="Doyle, Francis J" sort="Doyle, Francis J" uniqKey="Doyle F" first="Francis J." last="Doyle">Francis J. Doyle</name><affiliation><nlm:aff id="aff6">Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</nlm:aff></affiliation><affiliation><nlm:aff id="aff7">Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</nlm:aff></affiliation></author><author><name sortKey="Griffen, Steven C" sort="Griffen, Steven C" uniqKey="Griffen S" first="Steven C." last="Griffen">Steven C. Griffen</name><affiliation><nlm:aff id="aff9">JDRF, New York, NY</nlm:aff></affiliation></author><author><name sortKey="Haidar, Ahmad" sort="Haidar, Ahmad" uniqKey="Haidar A" first="Ahmad" last="Haidar">Ahmad Haidar</name><affiliation><nlm:aff id="aff10">Biomedical Engineering Department, McGill University, Montreal, Quebec, Canada</nlm:aff></affiliation><affiliation><nlm:aff id="aff11">Division of Endocrinology, McGill University, Montreal, Quebec, Canada</nlm:aff></affiliation></author><author><name sortKey="Heinemann, Lutz" sort="Heinemann, Lutz" uniqKey="Heinemann L" first="Lutz" last="Heinemann">Lutz Heinemann</name><affiliation><nlm:aff id="aff12">Profil Institute for Metabolic Research, Neuss, Germany</nlm:aff></affiliation></author><author><name sortKey="Hovorka, Roman" sort="Hovorka, Roman" uniqKey="Hovorka R" first="Roman" last="Hovorka">Roman Hovorka</name><affiliation><nlm:aff id="aff13">Wellcome Trust-Medical Research Clinical Institute of Metabolic Science, University of Cambridge, Cambridge, U.K.</nlm:aff></affiliation></author><author><name sortKey="Jones, Timothy W" sort="Jones, Timothy W" uniqKey="Jones T" first="Timothy W." last="Jones">Timothy W. Jones</name><affiliation><nlm:aff id="aff14">Telethon Kids Institute, The University of Western Australia, Perth, Australia</nlm:aff></affiliation></author><author><name sortKey="Kollman, Craig" sort="Kollman, Craig" uniqKey="Kollman C" first="Craig" last="Kollman">Craig Kollman</name><affiliation><nlm:aff id="aff15">Jaeb Center for Health Research, Tampa, FL</nlm:aff></affiliation></author><author><name sortKey="Kovatchev, Boris" sort="Kovatchev, Boris" uniqKey="Kovatchev B" first="Boris" last="Kovatchev">Boris Kovatchev</name><affiliation><nlm:aff id="aff16">University of Virginia, Charlottesville, VA</nlm:aff></affiliation></author><author><name sortKey="Levy, Brian L" sort="Levy, Brian L" uniqKey="Levy B" first="Brian L." last="Levy">Brian L. Levy</name><affiliation><nlm:aff id="aff17">Johnson & Johnson Diabetes Care Companies, Wayne, PA</nlm:aff></affiliation></author><author><name sortKey="Nimri, Revital" sort="Nimri, Revital" uniqKey="Nimri R" first="Revital" last="Nimri">Revital Nimri</name><affiliation><nlm:aff id="aff18">Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel</nlm:aff></affiliation></author><author><name sortKey="O Eal, David N" sort="O Eal, David N" uniqKey="O Eal D" first="David N." last="O Eal">David N. O Eal</name><affiliation><nlm:aff id="aff19">Department of Medicine, University of Melbourne, St. Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia</nlm:aff></affiliation></author><author><name sortKey="Philip, Moshe" sort="Philip, Moshe" uniqKey="Philip M" first="Moshe" last="Philip">Moshe Philip</name><affiliation><nlm:aff id="aff18">Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel</nlm:aff></affiliation></author><author><name sortKey="Renard, Eric" sort="Renard, Eric" uniqKey="Renard E" first="Eric" last="Renard">Eric Renard</name><affiliation><nlm:aff id="aff20">Department of Endocrinology, Diabetes, and Nutrition, Montpellier University Hospital, INSERM Clinical Investigation Centre 1411, Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France</nlm:aff></affiliation></author><author><name sortKey="Russell, Steven J" sort="Russell, Steven J" uniqKey="Russell S" first="Steven J." last="Russell">Steven J. Russell</name><affiliation><nlm:aff id="aff5">Department of Biomedical Engineering, Boston University, Boston, MA</nlm:aff></affiliation><affiliation><nlm:aff id="aff21">Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA</nlm:aff></affiliation></author><author><name sortKey="Weinzimer, Stuart A" sort="Weinzimer, Stuart A" uniqKey="Weinzimer S" first="Stuart A." last="Weinzimer">Stuart A. Weinzimer</name><affiliation><nlm:aff id="aff22">Yale University School of Medicine, New Haven, CT</nlm:aff></affiliation></author><author><name sortKey="Zisser, Howard" sort="Zisser, Howard" uniqKey="Zisser H" first="Howard" last="Zisser">Howard Zisser</name><affiliation><nlm:aff id="aff6">Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</nlm:aff></affiliation></author><author><name sortKey="Lum, John W" sort="Lum, John W" uniqKey="Lum J" first="John W." last="Lum">John W. Lum</name><affiliation><nlm:aff id="aff15">Jaeb Center for Health Research, Tampa, FL</nlm:aff></affiliation></author></analytic><series><title level="j">Diabetes Care</title><idno type="ISSN">0149-5992</idno><idno type="eISSN">1935-5548</idno><imprint><date when="2016">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community <italic>1</italic>) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA<sub>1c</sub>, and <italic>2</italic>) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Bergenstal, Rm" uniqKey="Bergenstal R">RM Bergenstal</name></author><author><name sortKey="Tamborlane, Wv" uniqKey="Tamborlane W">WV Tamborlane</name></author><author><name sortKey="Ahmann, A" uniqKey="Ahmann A">A Ahmann</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hermanides, J" uniqKey="Hermanides J">J Hermanides</name></author><author><name sortKey="N Rgaard, K" uniqKey="N Rgaard K">K Nørgaard</name></author><author><name sortKey="Bruttomesso, D" uniqKey="Bruttomesso D">D 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<front><journal-meta><journal-id journal-id-type="nlm-ta">Diabetes Care</journal-id><journal-id journal-id-type="iso-abbrev">Diabetes Care</journal-id><journal-id journal-id-type="hwp">diacare</journal-id><journal-id journal-id-type="pmc">dcare</journal-id><journal-id journal-id-type="publisher-id">Diabetes Care</journal-id><journal-title-group><journal-title>Diabetes Care</journal-title></journal-title-group><issn pub-type="ppub">0149-5992</issn><issn pub-type="epub">1935-5548</issn><publisher><publisher-name>American Diabetes Association</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">27330126</article-id><article-id pub-id-type="pmc">4915553</article-id><article-id pub-id-type="publisher-id">2716</article-id><article-id pub-id-type="doi">10.2337/dc15-2716</article-id><article-categories><subj-group subj-group-type="heading"><subject>The Artificial Pancreas in 2016: A Digital Treatment Ecosystem for Diabetes</subject></subj-group></article-categories><title-group><article-title>Outcome Measures for Artificial Pancreas Clinical Trials: A Consensus Report</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Maahs</surname><given-names>David M.</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Buckingham</surname><given-names>Bruce A.</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Castle</surname><given-names>Jessica R.</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author"><name><surname>Cinar</surname><given-names>Ali</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib><contrib contrib-type="author"><name><surname>Damiano</surname><given-names>Edward R.</given-names></name><xref ref-type="aff" rid="aff5"><sup>5</sup></xref></contrib><contrib contrib-type="author"><name><surname>Dassau</surname><given-names>Eyal</given-names></name><xref ref-type="aff" rid="aff6"><sup>6</sup></xref><xref ref-type="aff" rid="aff7"><sup>7</sup></xref></contrib><contrib contrib-type="author"><name><surname>DeVries</surname><given-names>J. Hans</given-names></name><xref ref-type="aff" rid="aff8"><sup>8</sup></xref></contrib><contrib contrib-type="author"><name><surname>Doyle</surname><given-names>Francis J.</given-names><suffix>III</suffix></name><xref ref-type="aff" rid="aff6"><sup>6</sup></xref><xref ref-type="aff" rid="aff7"><sup>7</sup></xref></contrib><contrib contrib-type="author"><name><surname>Griffen</surname><given-names>Steven C.</given-names></name><xref ref-type="aff" rid="aff9"><sup>9</sup></xref></contrib><contrib contrib-type="author"><name><surname>Haidar</surname><given-names>Ahmad</given-names></name><xref ref-type="aff" rid="aff10"><sup>10</sup></xref><xref ref-type="aff" rid="aff11"><sup>11</sup></xref></contrib><contrib contrib-type="author"><name><surname>Heinemann</surname><given-names>Lutz</given-names></name><xref ref-type="aff" rid="aff12"><sup>12</sup></xref></contrib><contrib contrib-type="author"><name><surname>Hovorka</surname><given-names>Roman</given-names></name><xref ref-type="aff" rid="aff13"><sup>13</sup></xref></contrib><contrib contrib-type="author"><name><surname>Jones</surname><given-names>Timothy W.</given-names></name><xref ref-type="aff" rid="aff14"><sup>14</sup></xref></contrib><contrib contrib-type="author"><name><surname>Kollman</surname><given-names>Craig</given-names></name><xref ref-type="aff" rid="aff15"><sup>15</sup></xref></contrib><contrib contrib-type="author"><name><surname>Kovatchev</surname><given-names>Boris</given-names></name><xref ref-type="aff" rid="aff16"><sup>16</sup></xref></contrib><contrib contrib-type="author"><name><surname>Levy</surname><given-names>Brian L.</given-names></name><xref ref-type="aff" rid="aff17"><sup>17</sup></xref></contrib><contrib contrib-type="author"><name><surname>Nimri</surname><given-names>Revital</given-names></name><xref ref-type="aff" rid="aff18"><sup>18</sup></xref></contrib><contrib contrib-type="author"><name><surname>O’Neal</surname><given-names>David N.</given-names></name><xref ref-type="aff" rid="aff19"><sup>19</sup></xref></contrib><contrib contrib-type="author"><name><surname>Philip</surname><given-names>Moshe</given-names></name><xref ref-type="aff" rid="aff18"><sup>18</sup></xref></contrib><contrib contrib-type="author"><name><surname>Renard</surname><given-names>Eric</given-names></name><xref ref-type="aff" rid="aff20"><sup>20</sup></xref></contrib><contrib contrib-type="author"><name><surname>Russell</surname><given-names>Steven J.</given-names></name><xref ref-type="aff" rid="aff5"><sup>5</sup></xref><xref ref-type="aff" rid="aff21"><sup>21</sup></xref></contrib><contrib contrib-type="author"><name><surname>Weinzimer</surname><given-names>Stuart A.</given-names></name><xref ref-type="aff" rid="aff22"><sup>22</sup></xref></contrib><contrib contrib-type="author"><name><surname>Zisser</surname><given-names>Howard</given-names></name><xref ref-type="aff" rid="aff6"><sup>6</sup></xref></contrib><contrib contrib-type="author" corresp="yes"><name><surname>Lum</surname><given-names>John W.</given-names></name><xref ref-type="aff" rid="aff15"><sup>15</sup></xref><xref ref-type="corresp" rid="cor1"></xref></contrib><aff id="aff1"><sup>1</sup>Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO</aff><aff id="aff2"><sup>2</sup>Division of Pediatric Endocrinology and Diabetes, Department of Pediatric Endocrinology, Stanford University, Stanford, CA</aff><aff id="aff3"><sup>3</sup>Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, OR</aff><aff id="aff4"><sup>4</sup>Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL</aff><aff id="aff5"><sup>5</sup>Department of Biomedical Engineering, Boston University, Boston, MA</aff><aff id="aff6"><sup>6</sup>Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA</aff><aff id="aff7"><sup>7</sup>Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</aff><aff id="aff8"><sup>8</sup>Department of Endocrinology, Academic Medical Centre, University of Amsterdam, Amsterdam, the Netherlands</aff><aff id="aff9"><sup>9</sup>JDRF, New York, NY</aff><aff id="aff10"><sup>10</sup>Biomedical Engineering Department, McGill University, Montreal, Quebec, Canada</aff><aff id="aff11"><sup>11</sup>Division of Endocrinology, McGill University, Montreal, Quebec, Canada</aff><aff id="aff12"><sup>12</sup>Profil Institute for Metabolic Research, Neuss, Germany</aff><aff id="aff13"><sup>13</sup>Wellcome Trust-Medical Research Clinical Institute of Metabolic Science, University of Cambridge, Cambridge, U.K.</aff><aff id="aff14"><sup>14</sup>Telethon Kids Institute, The University of Western Australia, Perth, Australia</aff><aff id="aff15"><sup>15</sup>Jaeb Center for Health Research, Tampa, FL</aff><aff id="aff16"><sup>16</sup>University of Virginia, Charlottesville, VA</aff><aff id="aff17"><sup>17</sup>Johnson & Johnson Diabetes Care Companies, Wayne, PA</aff><aff id="aff18"><sup>18</sup>Jesse Z and Sara Lea Shafer Institute of Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Sackler Faculty of Medicine, Tel Aviv University, Petah Tikva, Israel</aff><aff id="aff19"><sup>19</sup>Department of Medicine, University of Melbourne, St. Vincent's Hospital Melbourne, Fitzroy, Victoria, Australia</aff><aff id="aff20"><sup>20</sup>Department of Endocrinology, Diabetes, and Nutrition, Montpellier University Hospital, INSERM Clinical Investigation Centre 1411, Institute of Functional Genomics, CNRS UMR 5203, INSERM U1191, University of Montpellier, Montpellier, France</aff><aff id="aff21"><sup>21</sup>Diabetes Unit and Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA</aff><aff id="aff22"><sup>22</sup>Yale University School of Medicine, New Haven, CT</aff></contrib-group><author-notes><corresp id="cor1">Corresponding author: John W. Lum, <email>jlum@jaeb.org</email>.</corresp></author-notes><pub-date pub-type="ppub"><month>7</month><year>2016</year></pub-date><pub-date pub-type="epub"><day>20</day><month>3</month><year>2016</year></pub-date><volume>39</volume><issue>7</issue><fpage>1175</fpage><lpage>1179</lpage><permissions><copyright-statement>© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.</copyright-statement><copyright-year>2016</copyright-year></permissions><self-uri xlink:title="pdf" xlink:href="dc152716.pdf"></self-uri><abstract><p>Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community <italic>1</italic>) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA<sub>1c</sub>, and <italic>2</italic>) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.</p></abstract><counts><page-count count="5"></page-count></counts></article-meta></front><body><sec sec-type="introduction" id="s1"><title>Introduction</title><p content-type="flushleft">Since the publication of the Diabetes Control and Complications Trial (DCCT) in 1993 (<xref rid="B1" ref-type="bibr">1</xref>), the main outcome measures for glycemic control in people with type 1 diabetes have been hemoglobin A1c (HbA<sub>1c</sub>) due to the clear link to the development of complications and episodes of severe hypoglycemia (SH) as it is an immediate life-threatening event. Advances in diabetes treatment and technology have since resulted in improved care, reflected in lower HbA<sub>1c</sub> and rates of SH for people with type 1 diabetes (<xref rid="B2" ref-type="bibr">2</xref>–<xref rid="B6" ref-type="bibr">6</xref>). However, many patients still struggle with glucose control and have large and erratic swings in glycemia (<xref rid="B7" ref-type="bibr">7</xref>). Research on and commercial development of the artificial pancreas (AP), either as automated insulin-only delivery or as multihormonal delivery, continue to progress rapidly, and the AP promises to become a part of clinical care (<xref rid="B8" ref-type="bibr">8</xref>). An AP system may benefit individual patients in unique ways that would not be reflected in HbA<sub>1c</sub> improvements alone; for example, a patient with a low HbA<sub>1c</sub> and frequent hypoglycemia may have an increase in HbA<sub>1c</sub> on an AP system while hypoglycemia and quality of life improve.</p></sec><sec id="s2"><title>Objective and Rationale</title><p content-type="flushleft">In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community <italic>1</italic>) advocate for the use of continuous glucose monitoring (CGM) glucose metrics as glycemic outcome measures in AP trials, in addition to HbA<sub>1c</sub>, and <italic>2</italic>) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Currently, the U.S. Food and Drug Administration accepts the use of various CGM glucose metrics in AP trials (<xref rid="B9" ref-type="bibr">9</xref>), but investigators do not always use a consistent set of measures that enables comparison. Thus, one rationale for the current report is to enable basic comparison between different AP research studies and with other clinical studies on glycemic control in type 1 diabetes. We acknowledge there are methodological limitations with between-study comparison that require careful consideration of study design differences. However, the standardization of these simple metrics provides a starting point for regulators, payers, health care providers, and patients to interpret AP and other study data with interventions on glycemic control. This will be especially important as AP systems become part of the daily lives of people with type 1 diabetes. Standardization of these measures does not preclude the addition of other metrics specific for a particular AP approach or used by particular research groups. In this report, we specifically advocate for the use of a basic set of CGM glucose metrics in AP studies. We suggest that their use in general type 1 diabetes studies is broadly applicable and highly relevant given the increasing adoption of CGM in research and clinical care (<xref rid="B7" ref-type="bibr">7</xref>).</p><p>Improvements in and adoption of AP-related technology, particularly in the reliability and accuracy of CGM systems, have focused attention on determining the best metrics for assessing outcomes in studies with people with type 1 diabetes (<xref rid="B10" ref-type="bibr">10</xref>–<xref rid="B16" ref-type="bibr">16</xref>). Currently available CGM systems with glucose readings up to every 5 min, or 288 times daily, provide considerably more data than do the American Diabetes Association recommendation of checking blood glucose 6–10 times daily (<xref rid="B17" ref-type="bibr">17</xref>) or the 7-point blood glucose measurements performed quarterly for research purposes in the DCCT. Although 7-point blood glucose profiles do provide insights into glycemic excursions that are not apparent with HbA<sub>1c</sub>, the profiles are very dependent on patient motivation and the chosen day of performance and provide only limited information about glucose control compared with glucose values provided by CGM systems. From a patient perspective, a CGM glycemic profile is more meaningful in that it shows highs, lows, trends, and variability as well as the effect of behaviors on glucose levels (<xref rid="B18" ref-type="bibr">18</xref>–<xref rid="B21" ref-type="bibr">21</xref>). This contrasts with HbA<sub>1c</sub> as a metric of integrated glycemic exposure over time. In particular, HbA<sub>1c</sub> does not provide information on frequency and extent of hypo- or hyperglycemia, which is a crucial aspect to evaluating glucose control in people with type 1 diabetes.</p></sec><sec id="s3"><title>Basic Outcome Measures</title><p content-type="flushleft">The recommended basic set of outcome measures presented in <xref ref-type="table" rid="T1">Table 1</xref> includes CGM glucose metrics to define time spent in desired ranges as well as time in hypo- and hyperglycemia, measures of CGM glucose variability, safety measures such as SH and diabetic ketoacidosis, and technical metrics to evaluate AP system performance. It is intended that these measures be applicable across a wide range of AP study designs, including both short-term pilot studies and longer-term in-home or pivotal studies. Many of the glucose cut points and ranges are based on convention in AP research but were chosen to allow for comparison between studies.</p><table-wrap id="T1" orientation="portrait" position="float"><label>Table 1</label><caption><p>Recommended basic outcome measures to be reported for AP clinical trials</p></caption><table frame="hsides" rules="groups"><thead><tr><th rowspan="1" colspan="1"></th><th align="center" rowspan="1" colspan="1">Comments</th></tr></thead><tbody><tr><td colspan="2" rowspan="1">Glycemic metrics<xref ref-type="table-fn" rid="tblfn2"><bold>*</bold></xref><italic><sup>,</sup></italic><xref ref-type="table-fn" rid="tblfn3">†</xref></td></tr><tr><td rowspan="1" colspan="1"> HbA<sub>1c</sub></td><td align="center" rowspan="1" colspan="1">If intervention period ≥3 months</td></tr><tr><td rowspan="1" colspan="1"> Mean CGM glucose</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time <50 mg/dL (<2.8 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time <60 mg/dL (<3.3 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time <70 mg/dL (<3.9 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time 70–140 mg/dL (3.9–7.8 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time 70–180 mg/dL (3.9–10.0 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time >180 mg/dL (>10.0 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time >250 mg/dL (>13.9 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> % CGM time >300 mg/dL (>16.7 mmol/L)</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> SD and coefficient of variation of CGM values</td><td align="center" rowspan="1" colspan="1">SD is much more dependent on the mean than coefficient of variation</td></tr><tr><td rowspan="1" colspan="1"> Fasting blood glucose, mg/dL (mmol/L)</td><td align="center" rowspan="1" colspan="1">If available, depending on study design; CGM glucose at 06:00 can be taken as proxy</td></tr><tr><td colspan="2" rowspan="1">Safety metrics</td></tr><tr><td rowspan="1" colspan="1"> SH events</td><td align="center" rowspan="1" colspan="1">As defined by ADA (adults) (<xref rid="B32" ref-type="bibr">32</xref>) and ISPAD (children and adolescents) (<xref rid="B31" ref-type="bibr">31</xref>)</td></tr><tr><td rowspan="1" colspan="1"> Diabetic ketoacidosis events</td><td align="center" rowspan="1" colspan="1">Per ADA definition (<xref rid="B41" ref-type="bibr">41</xref>)</td></tr><tr><td colspan="2" rowspan="1">Technical performance metrics<xref ref-type="table-fn" rid="tblfn2">*</xref></td></tr><tr><td rowspan="1" colspan="1"> % Time closed-loop active</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Total daily dose of insulin</td><td rowspan="1" colspan="1"></td></tr><tr><td rowspan="1" colspan="1"> Total daily dose of glucagon or other hormones</td><td align="center" rowspan="1" colspan="1">If applicable</td></tr></tbody></table><table-wrap-foot><fn id="tblfn1"><p>ADA, American Diabetes Association; ISPAD, International Society for Pediatric and Adolescent Diabetes.</p></fn><fn id="tblfn2"><label>*</label><p>Metrics may have a skewed distribution. Report median (quartiles) instead of mean if not normally distributed.</p></fn><fn id="tblfn3"><label>†</label><p>All CGM measures should be reported for the overall 24-h period (if applicable) and also stratified by daytime and nighttime periods. The time period 00:00 to 06:00 is proposed as a definition of the nighttime period to exclude postprandial data as much as possible for a typical study population, though this definition may not be appropriate for all studies.</p></fn></table-wrap-foot></table-wrap><p>HbA<sub>1c</sub> remains the best currently available measure to assess long-term glycemic control and should be assessed in any AP study of 3 months or longer. However, it is clear that HbA<sub>1c</sub> only captures average glycemia and does not provide information on the frequency or severity of hypoglycemic events. Although HbA<sub>1c</sub> is currently the most accepted metric for risk stratification of long-term complications of diabetes, the proposed metrics more comprehensively describe glycemia.</p></sec><sec id="s4"><title>Additional Recommendations and Limitations</title><p content-type="flushleft">Graphical presentation of outcome data is also important, although standardization is less straightforward than with tabular data. A common figure for visualization of pooled-subject AP performance is the modal day glycemic control plot (or analogous insulin delivery plot) with median line and interquartile range bands. Inclusion of a cumulative histogram of CGM data would support the extraction of arbitrary glycemic ranges for comparison purposes (<xref rid="B22" ref-type="bibr">22</xref>). Numerous other graphical representations of data have been developed, and the choice of figures should be individualized for the data and the target audience (<xref rid="B23" ref-type="bibr">23</xref>).</p><p>The number of symptomatic hypoglycemia events per week may also be valuable as a meaningful clinical index of diabetes burden to the patient (<xref rid="B24" ref-type="bibr">24</xref>–<xref rid="B27" ref-type="bibr">27</xref>). Indeed, time spent below targeted glucose range according to CGM data may not fully capture the patient’s experience with debilitating glucose-related events, which might better illustrate diabetes burden. Because reliable capture of symptomatic hypoglycemia events requiring treatment may be challenging in longer-term AP studies, biochemical hypoglycemia event rate as measured by CGM could be reported as a proxy. For example, the rate of CGM excursions below 70 or 55 mg/dL (3.9 or 3.0 mg/dL) for at least 10 or 30 min or longer time periods could be reported, as could other metrics including area under the curve (<xref rid="B28" ref-type="bibr">28</xref>–<xref rid="B32" ref-type="bibr">32</xref>). Many other novel measures of AP performance and algorithms have been developed (<xref rid="B33" ref-type="bibr">33</xref>–<xref rid="B36" ref-type="bibr">36</xref>), and this is an area of active research.</p><p>CGM and pump make and model and the kind of device running the control algorithm (e.g., laptop, smartphone) should be specified, including any relevant CGM signal conditioning algorithm details. We note that bias can occur when the same CGM that informs the AP controller is also used to assess glycemic outcomes (<xref rid="B37" ref-type="bibr">37</xref>), but there often is no practical alternative to this approach. Any special system- or protocol-related design elements should be disclosed if they are intended to improve safety or impact glycemic control or if they place additional burden on the user. The amount and timing of contact between study staff and participants in both the AP and comparator arms of the studies should be reported for in-home studies. In addition, CGM calibration logistics should be disclosed, along with a description of how conventional capillary blood glucose measurements are performed. Determination of median (or mean) absolute relative difference (MARD) for CGM versus capillary blood glucose is often used to characterize CGM accuracy in AP studies, though blood glucose sampling bias may limit the generalizability of these results (<xref rid="B38" ref-type="bibr">38</xref>).</p><p>Depending on the study design, the outcomes described could be reported for the entire cohort of a study or the study participants could be stratified into relevant subgroups with outcomes reported separately. For example, improved HbA<sub>1c</sub> without increased risk for hypoglycemia could be reported for those who were poorly controlled at the baseline (e.g., baseline HbA<sub>1c</sub> >8% [>64 mmol/mol]), whereas reduced incidence of hypoglycemia without deterioration in HbA<sub>1c</sub> could be reported for those with well-controlled average glycemia at the baseline. The analysis of the primary and other important outcomes should be performed on an intention-to-treat basis.</p><p>Future areas of need for AP technology include expanded standardized metrics to evaluate the technical performance of AP systems (<xref rid="B22" ref-type="bibr">22</xref>) and to assess patient/caregiver usability, including psychosocial metrics such as quality of life and other measures of reduction of burden, which need to be developed, including stress, anxiety, depression, and use during exercise (<xref rid="B39" ref-type="bibr">39</xref>). In addition, the development of standard measures to assess patient preference may be used to support regulatory approval and to serve to inform health care providers and patients of the potential impact of the use of AP systems (<xref rid="B40" ref-type="bibr">40</xref>). Compelling health economic measures comparing AP system costs with the potential short- and long-term economic benefits are required to establish the financial viability of these systems and to drive acceptance by health care providers and people with diabetes (<xref rid="B8" ref-type="bibr">8</xref>).</p><p>Multiple large and longer in-home clinical trials will soon begin with different AP systems supported by the National Institute of Diabetes and Digestive and Kidney Diseases, JDRF, the Helmsley Charitable Trust, and other funders, as well as those being supported by the industry. Some of these have been designed as pivotal trials to provide data about relevant end points to be presented to U.S. Food and Drug Administration and other regulatory authorities for approval of AP products for clinical use and to support reimbursement. This report emphasizes the need for a set of basic, uniform, standardized, and comparable outcome measures with different AP systems. As AP technologies become available for clinical use, common data reports that compare systems will be desired for health care providers, payers, and people with type 1 diabetes and their families.</p><p>In summary, members of the JDRF Artificial Pancreas Project Consortium and the larger AP community advocate for the adoption of a set of basic outcome metrics that will allow for comparison between studies with different AP systems. This, in turn, will facilitate the interpretation of the information from trials and contribute to the ultimate goal of widespread adoption of AP technology to improve the life of people with type 1 diabetes.</p></sec></body><back><fn-group><fn id="fn1"><p>See accompanying articles, pp. <related-article id="d36e714" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc16-0824" vol="39" page="1123">1123</related-article>, <related-article id="d36e717" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc16-0089" vol="39" page="1127">1127</related-article>, <related-article id="d36e720" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc15-2344" vol="39" page="1135">1135</related-article>, <related-article id="d36e723" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc15-2468" vol="39" page="1143">1143</related-article>, <related-article id="d36e726" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc16-0008" vol="39" page="1151">1151</related-article>, <related-article id="d36e730" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc15-2449" vol="39" page="1161">1161</related-article>, <related-article id="d36e733" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc15-2078" vol="39" page="1168">1168</related-article>, and <related-article id="d36e736" related-article-type="companion" ext-link-type="doi" xlink:href="10.2337/dc15-2815" vol="39" page="1180">1180</related-article>.</p></fn></fn-group><ack><title>Article Information</title><p><bold>Funding and Duality of Interest.</bold> D.M.M. reports personal fees from Abbott and Insulet and research support from Medtronic, Dexcom, and Roche Diagnostics. B.A.B. reports grant support from JDRF, Medtronic, and Dexcom; personal fees from Medtronic, Sanofi, Tandem Diabetes Care, and Novo Nordisk; and other support from Roche Diagnostics, Dexcom, and Abbott. J.R.C. has a significant financial interest in Pacific Diabetes Technologies, Inc., a company that may have a commercial interest in the results of this research and technology. This potential conflict of interest has been reviewed and managed by Oregon Health & Science University. A.C. reports personal fees from Medtronic and a patent “Automatic insulin pumps using recursive multivariable models and adaptive control algorithms” (#US8690820) issued. E.R.D. reports a patent “Fully automated control system for type 1 diabetes” (#US7806854) issued, grants from JDRF and the National Institutes of Health (NIH), and personal fees from Automated Glucose Control LLC and Animas Corp. J.H.D. reports grants from Medtronic and personal fees from Johnson & Johnson and Sanofi. F.J.D. reports grants from JDRF and NIH and personal fees from Automated Glucose Control LLC and Animas Corp. A.H. owns intellectual property in the field of artificial pancreas (dosing algorithm). A.H. has received consultant and speaker honoraria from SNELL Medical Communication and the Diabetic Children's Foundation and research support from Medtronic. R.H. reports personal fees from Medtronic, Eli Lilly, Novo Nordisk, B. Braun, Sanofi, and Profil and grants from JDRF and Diabetes UK. In addition, R.H. has a patent “Substance monitoring and control in human R.H. reports personal fees from Medtronic, Eli Lilly, Novo Nordisk, B. Braun, Sanofi-Aventis, and Profil, as well as grants from JDRF, Diabetes UK, the National Institute of Diabetes and Digestive and Kidney Diseases, European Commission 7th Framework Programme for Research and Technological Development, and National Institute for Health Research Efficacy and Mechanism Evaluation Programme. In addition, R.H. has a patent “Substance monitoring and control in human or animal bodies” (#US9089305) license, a patent “Overnight closed-loop insulin delivery with model predictive control and glucose measurement error model” (#US8585637) issued, a patent “A system for insulin delivery using glucose regulation and measurement error models” (#US 8062249) issued, and a patent “Substance monitoring and control in human or animal bodies” (#US8977504) licensed. T.W.J. reports personal fees and nonfinancial support from Medtronic and personal fees from Novo Nordisk and Animas Corp. B.K. reports grants from Becton, Dickson, and Co. and Sanofi, personal fees from Sanofi, and nonfinancial support from Animas Corp., Roche Diagnostics, and Tandem Diabetes Care. In addition, B.K. has a patent “CGM-based prevention of hypoglycemia via hypoglycemia risk assessment and smooth reduction of insulin delivery” (#US8562587), with royalties paid to Animas Corp.; a patent “Method and apparatus for modular power management and protection of critical services in ambulatory medical devices” (PCT/US12/43883), licensed to TypeZero Technologies; and a patent “Unified platform for monitoring and control of blood glucose levels in diabetic patients” (PCT/US12/43910), licensed to TypeZero Technologies. R.H. is a shareholder in TypeZero Technologies. B.L.L. reports other support from LifeScan and Animas Corp. R.N. reports grants from JDRF and other support from Novo Nordisk and DreaMed Diabetes Ltd. D.N.O. reports grants and personal fees from Medtronic, Roche Diagnostics, Sanofi, and Novo Nordisk. M.P. report grants from Medtronic, Novo Nordisk, Roche Diagnostics, Eli Lilly, Merck, Andromeda, Sanofi, Bristol-Myers Squibb, Kamada, Pfizer, AstraZeneca, NG Solutions Ltd., and Nutriteen Professionals Ltd.; personal fees from Medtronic, Novo Nordisk, Eli Lilly, Sanofi, and Pfizer; and other support from Medtronic, Novo Nordisk, Roche Diagnostics, CGM3 Ltd., NG Solutions Ltd., DreaMed Diabetes Ltd., and Nutriteen Professionals Ltd. M.P. has a patent “Method and system for automatic monitoring of diabetes related treatments” (#US20120123234 A1) pending. E.R. reports personal fees from A. Menarini Diagnostics, Cellnovo, Eli Lilly, Johnson & Johnson, Animas Corp., LifeScan, Medtronic, Novo Nordisk, Abbott, Roche Diagnostics, Dexcom, and Sanofi and nonfinancial support from Abbott, Roche Diagnostics, Dexcom, and Insulet. S.J.R. reports personal fees from Dexcom, Tandem Diabetes Care, Sanofi, Eli Lilly, and Novo Nordisk; nonfinancial support from Eli Lilly, SweetSpot Diabetes, International Biomedical, Abbott, Insulet, and Medtronic; and other support from Companion Medical. S.J.R. has patents pending on aspects of bionic pancreas. S.A.W. reports grants from Medtronic and personal fees from Medtronic, Insulet, and Tandem Diabetes Care. H.Z. reports grants from NIH and JDRF. 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