Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.
Identifieur interne : 000721 ( Main/Exploration ); précédent : 000720; suivant : 000722Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.
Auteurs : Daniel P. Howsmon [États-Unis] ; Nihat Baysal [États-Unis] ; Bruce A. Buckingham [États-Unis] ; Gregory P. Forlenza [États-Unis] ; Trang T. Ly [États-Unis] ; David M. Maahs [États-Unis] ; Tatiana Marcal [États-Unis] ; Lindsey Towers [États-Unis] ; Eric Mauritzen [États-Unis] ; Sunil Deshpande [États-Unis] ; Lauren M. Huyett [États-Unis] ; Jordan E. Pinsker [États-Unis] ; Ravi Gondhalekar [États-Unis] ; Francis J. Doyle [États-Unis] ; Eyal Dassau [États-Unis] ; Juergen Hahn [États-Unis] ; B Wayne Bequette [États-Unis]Source :
- Journal of diabetes science and technology [ 1932-2968 ] ; 2018.
Descripteurs français
- KwdFr :
- Acidocétose diabétique (prévention et contrôle), Acidocétose diabétique (étiologie), Adulte (MeSH), Adulte d'âge moyen (MeSH), Algorithmes (MeSH), Diabète de type 1 (traitement médicamenteux), Femelle (MeSH), Humains (MeSH), Hypoglycémiants (administration et posologie), Insuline (administration et posologie), Mâle (MeSH), Pancréas artificiel (effets indésirables), Panne d'appareillage (MeSH), Pompes à insuline (effets indésirables), Études croisées (MeSH).
- MESH :
- administration et posologie : Hypoglycémiants, Insuline.
- effets indésirables : Pancréas artificiel, Pompes à insuline.
- prévention et contrôle : Acidocétose diabétique.
- traitement médicamenteux : Diabète de type 1.
- étiologie : Acidocétose diabétique.
- Adulte, Adulte d'âge moyen, Algorithmes, Femelle, Humains, Mâle, Panne d'appareillage, Études croisées.
English descriptors
- KwdEn :
- Adult (MeSH), Algorithms (MeSH), Cross-Over Studies (MeSH), Diabetes Mellitus, Type 1 (drug therapy), Diabetic Ketoacidosis (etiology), Diabetic Ketoacidosis (prevention & control), Equipment Failure (MeSH), Female (MeSH), Humans (MeSH), Hypoglycemic Agents (administration & dosage), Insulin (administration & dosage), Insulin Infusion Systems (adverse effects), Male (MeSH), Middle Aged (MeSH), Pancreas, Artificial (adverse effects).
- MESH :
- chemical , administration & dosage : Hypoglycemic Agents, Insulin.
- adverse effects : Insulin Infusion Systems, Pancreas, Artificial.
- drug therapy : Diabetes Mellitus, Type 1.
- etiology : Diabetic Ketoacidosis.
- prevention & control : Diabetic Ketoacidosis.
- Adult, Algorithms, Cross-Over Studies, Equipment Failure, Female, Humans, Male, Middle Aged.
Abstract
BACKGROUND
As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures.
METHODS
An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed.
RESULTS
In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58).
CONCLUSIONS
As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875.
DOI: 10.1177/1932296818755173
PubMed: 29390915
PubMed Central: PMC6154252
Affiliations:
- États-Unis
- Californie, Colorado, Massachusetts, État de New York
- Cambridge (Massachusetts)
- Université Harvard
Links toward previous steps (curation, corpus...)
Le document en format XML
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Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Hahn, Juergen" sort="Hahn, Juergen" uniqKey="Hahn J" first="Juergen" last="Hahn">Juergen Hahn</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>8 Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>8 Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Bequette, B Wayne" sort="Bequette, B Wayne" uniqKey="Bequette B" first="B Wayne" last="Bequette">B Wayne Bequette</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2018">2018</date><idno type="RBID">pubmed:29390915</idno><idno type="pmid">29390915</idno><idno type="doi">10.1177/1932296818755173</idno><idno type="pmc">PMC6154252</idno><idno type="wicri:Area/Main/Corpus">000958</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000958</idno><idno type="wicri:Area/Main/Curation">000958</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000958</idno><idno type="wicri:Area/Main/Exploration">000958</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.</title><author><name sortKey="Howsmon, Daniel P" sort="Howsmon, Daniel P" uniqKey="Howsmon D" first="Daniel P" last="Howsmon">Daniel P. Howsmon</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Baysal, Nihat" sort="Baysal, Nihat" uniqKey="Baysal N" first="Nihat" last="Baysal">Nihat Baysal</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></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 wicri:level="2"><nlm:affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Forlenza, Gregory P" sort="Forlenza, Gregory P" uniqKey="Forlenza G" first="Gregory P" last="Forlenza">Gregory P. Forlenza</name><affiliation wicri:level="2"><nlm:affiliation>3 Barbara Davis Center, University of Colorado Denver, Denver, CO, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>3 Barbara Davis Center, University of Colorado Denver, Denver, CO</wicri:regionArea><placeName><region type="state">Colorado</region></placeName></affiliation></author><author><name sortKey="Ly, Trang T" sort="Ly, Trang T" uniqKey="Ly T" first="Trang T" last="Ly">Trang T. Ly</name><affiliation wicri:level="2"><nlm:affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Maahs, David M" sort="Maahs, David M" uniqKey="Maahs D" first="David M" last="Maahs">David M. Maahs</name><affiliation wicri:level="2"><nlm:affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Marcal, Tatiana" sort="Marcal, Tatiana" uniqKey="Marcal T" first="Tatiana" last="Marcal">Tatiana Marcal</name><affiliation wicri:level="2"><nlm:affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Towers, Lindsey" sort="Towers, Lindsey" uniqKey="Towers L" first="Lindsey" last="Towers">Lindsey Towers</name><affiliation wicri:level="2"><nlm:affiliation>3 Barbara Davis Center, University of Colorado Denver, Denver, CO, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>3 Barbara Davis Center, University of Colorado Denver, Denver, CO</wicri:regionArea><placeName><region type="state">Colorado</region></placeName></affiliation></author><author><name sortKey="Mauritzen, Eric" sort="Mauritzen, Eric" uniqKey="Mauritzen E" first="Eric" last="Mauritzen">Eric Mauritzen</name><affiliation wicri:level="2"><nlm:affiliation>4 Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>4 Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Deshpande, Sunil" sort="Deshpande, Sunil" uniqKey="Deshpande S" first="Sunil" last="Deshpande">Sunil Deshpande</name><affiliation wicri:level="4"><nlm:affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Huyett, Lauren M" sort="Huyett, Lauren M" uniqKey="Huyett L" first="Lauren M" last="Huyett">Lauren M. Huyett</name><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>7 Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>7 Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Pinsker, Jordan E" sort="Pinsker, Jordan E" uniqKey="Pinsker J" first="Jordan E" last="Pinsker">Jordan E. Pinsker</name><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Gondhalekar, Ravi" sort="Gondhalekar, Ravi" uniqKey="Gondhalekar R" first="Ravi" last="Gondhalekar">Ravi Gondhalekar</name><affiliation wicri:level="4"><nlm:affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></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 wicri:level="4"><nlm:affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Dassau, Eyal" sort="Dassau, Eyal" uniqKey="Dassau E" first="Eyal" last="Dassau">Eyal Dassau</name><affiliation wicri:level="4"><nlm:affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA</wicri:regionArea><placeName><region type="state">Massachusetts</region><settlement type="city">Cambridge (Massachusetts)</settlement></placeName><orgName type="university">Université Harvard</orgName></affiliation><affiliation wicri:level="2"><nlm:affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>6 Sansum Diabetes Research Institute, Santa Barbara, CA</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Hahn, Juergen" sort="Hahn, Juergen" uniqKey="Hahn J" first="Juergen" last="Hahn">Juergen Hahn</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation><affiliation wicri:level="2"><nlm:affiliation>8 Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>8 Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author><author><name sortKey="Bequette, B Wayne" sort="Bequette, B Wayne" uniqKey="Bequette B" first="B Wayne" last="Bequette">B Wayne Bequette</name><affiliation wicri:level="2"><nlm:affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY</wicri:regionArea><placeName><region type="state">État de New York</region></placeName></affiliation></author></analytic><series><title level="j">Journal of diabetes science and technology</title><idno type="eISSN">1932-2968</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult (MeSH)</term><term>Algorithms (MeSH)</term><term>Cross-Over Studies (MeSH)</term><term>Diabetes Mellitus, Type 1 (drug therapy)</term><term>Diabetic Ketoacidosis (etiology)</term><term>Diabetic Ketoacidosis (prevention & control)</term><term>Equipment Failure (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Hypoglycemic Agents (administration & dosage)</term><term>Insulin (administration & dosage)</term><term>Insulin Infusion Systems (adverse effects)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Pancreas, Artificial (adverse effects)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Acidocétose diabétique (prévention et contrôle)</term><term>Acidocétose diabétique (étiologie)</term><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Algorithmes (MeSH)</term><term>Diabète de type 1 (traitement médicamenteux)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Hypoglycémiants (administration et posologie)</term><term>Insuline (administration et posologie)</term><term>Mâle (MeSH)</term><term>Pancréas artificiel (effets indésirables)</term><term>Panne d'appareillage (MeSH)</term><term>Pompes à insuline (effets indésirables)</term><term>Études croisées (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="administration & dosage" xml:lang="en"><term>Hypoglycemic Agents</term><term>Insulin</term></keywords><keywords scheme="MESH" qualifier="administration et posologie" xml:lang="fr"><term>Hypoglycémiants</term><term>Insuline</term></keywords><keywords scheme="MESH" qualifier="adverse effects" xml:lang="en"><term>Insulin Infusion Systems</term><term>Pancreas, Artificial</term></keywords><keywords scheme="MESH" qualifier="drug therapy" xml:lang="en"><term>Diabetes Mellitus, Type 1</term></keywords><keywords scheme="MESH" qualifier="effets indésirables" xml:lang="fr"><term>Pancréas artificiel</term><term>Pompes à insuline</term></keywords><keywords scheme="MESH" qualifier="etiology" xml:lang="en"><term>Diabetic Ketoacidosis</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Diabetic Ketoacidosis</term></keywords><keywords scheme="MESH" qualifier="prévention et contrôle" xml:lang="fr"><term>Acidocétose diabétique</term></keywords><keywords scheme="MESH" qualifier="traitement médicamenteux" xml:lang="fr"><term>Diabète de type 1</term></keywords><keywords scheme="MESH" qualifier="étiologie" xml:lang="fr"><term>Acidocétose diabétique</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Algorithms</term><term>Cross-Over Studies</term><term>Equipment Failure</term><term>Female</term><term>Humans</term><term>Male</term><term>Middle Aged</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Algorithmes</term><term>Femelle</term><term>Humains</term><term>Mâle</term><term>Panne d'appareillage</term><term>Études croisées</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>BACKGROUND</b></p><p>As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58).</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29390915</PMID><DateCompleted><Year>2019</Year><Month>06</Month><Day>10</Day></DateCompleted><DateRevised><Year>2019</Year><Month>06</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1932-2968</ISSN><JournalIssue CitedMedium="Internet"><Volume>12</Volume><Issue>3</Issue><PubDate><Year>2018</Year><Month>05</Month></PubDate></JournalIssue><Title>Journal of diabetes science and technology</Title><ISOAbbreviation>J Diabetes Sci Technol</ISOAbbreviation></Journal><ArticleTitle>Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.</ArticleTitle><Pagination><MedlinePgn>599-607</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1177/1932296818755173</ELocationID><Abstract><AbstractText Label="BACKGROUND">As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures.</AbstractText><AbstractText Label="METHODS">An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed.</AbstractText><AbstractText Label="RESULTS">In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58).</AbstractText><AbstractText Label="CONCLUSIONS">As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Howsmon</LastName><ForeName>Daniel P</ForeName><Initials>DP</Initials><Identifier Source="ORCID">0000-0002-7177-1342</Identifier><AffiliationInfo><Affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Baysal</LastName><ForeName>Nihat</ForeName><Initials>N</Initials><AffiliationInfo><Affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Buckingham</LastName><ForeName>Bruce A</ForeName><Initials>BA</Initials><AffiliationInfo><Affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Forlenza</LastName><ForeName>Gregory P</ForeName><Initials>GP</Initials><AffiliationInfo><Affiliation>3 Barbara Davis Center, University of Colorado Denver, Denver, CO, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Ly</LastName><ForeName>Trang T</ForeName><Initials>TT</Initials><Identifier Source="ORCID">0000-0002-5995-1447</Identifier><AffiliationInfo><Affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Maahs</LastName><ForeName>David M</ForeName><Initials>DM</Initials><AffiliationInfo><Affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Marcal</LastName><ForeName>Tatiana</ForeName><Initials>T</Initials><AffiliationInfo><Affiliation>2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Towers</LastName><ForeName>Lindsey</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>3 Barbara Davis Center, University of Colorado Denver, Denver, CO, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mauritzen</LastName><ForeName>Eric</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>4 Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Deshpande</LastName><ForeName>Sunil</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Huyett</LastName><ForeName>Lauren M</ForeName><Initials>LM</Initials><AffiliationInfo><Affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>7 Department of Chemical Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pinsker</LastName><ForeName>Jordan E</ForeName><Initials>JE</Initials><Identifier Source="ORCID">0000-0003-4080-9034</Identifier><AffiliationInfo><Affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gondhalekar</LastName><ForeName>Ravi</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Doyle</LastName><ForeName>Francis J</ForeName><Initials>FJ</Initials><Suffix>3rd</Suffix><AffiliationInfo><Affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Dassau</LastName><ForeName>Eyal</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Hahn</LastName><ForeName>Juergen</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>8 Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bequette</LastName><ForeName>B Wayne</ForeName><Initials>BW</Initials><AffiliationInfo><Affiliation>1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><DataBankList CompleteYN="Y"><DataBank><DataBankName>ClinicalTrials.gov</DataBankName><AccessionNumberList><AccessionNumber>NCT02773875</AccessionNumber></AccessionNumberList></DataBank></DataBankList><GrantList CompleteYN="Y"><Grant><GrantID>DP3 DK094331</GrantID><Acronym>DK</Acronym><Agency>NIDDK NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>DP3 DK104057</GrantID><Acronym>DK</Acronym><Agency>NIDDK NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>R01 DK102188</GrantID><Acronym>DK</Acronym><Agency>NIDDK NIH HHS</Agency><Country>United States</Country></Grant><Grant><GrantID>UL1 TR001085</GrantID><Acronym>TR</Acronym><Agency>NCATS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016449">Randomized Controlled Trial</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType 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Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003922" MajorTopicYN="N">Diabetes Mellitus, Type 1</DescriptorName><QualifierName UI="Q000188" MajorTopicYN="Y">drug therapy</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D016883" MajorTopicYN="N">Diabetic Ketoacidosis</DescriptorName><QualifierName UI="Q000209" MajorTopicYN="N">etiology</QualifierName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D004868" MajorTopicYN="N">Equipment Failure</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007004" MajorTopicYN="N">Hypoglycemic Agents</DescriptorName><QualifierName UI="Q000008" MajorTopicYN="N">administration & dosage</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007328" MajorTopicYN="N">Insulin</DescriptorName><QualifierName UI="Q000008" MajorTopicYN="N">administration & dosage</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007332" MajorTopicYN="N">Insulin Infusion Systems</DescriptorName><QualifierName UI="Q000009" MajorTopicYN="N">adverse effects</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D019397" MajorTopicYN="N">Pancreas, Artificial</DescriptorName><QualifierName UI="Q000009" MajorTopicYN="Y">adverse effects</QualifierName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">artificial pancreas</Keyword><Keyword MajorTopicYN="Y">fault detection</Keyword><Keyword MajorTopicYN="Y">infusion site 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Howsmon</name></region><name sortKey="Baysal, Nihat" sort="Baysal, Nihat" uniqKey="Baysal N" first="Nihat" last="Baysal">Nihat Baysal</name><name sortKey="Bequette, B Wayne" sort="Bequette, B Wayne" uniqKey="Bequette B" first="B Wayne" last="Bequette">B Wayne Bequette</name><name sortKey="Buckingham, Bruce A" sort="Buckingham, Bruce A" uniqKey="Buckingham B" first="Bruce A" last="Buckingham">Bruce A. Buckingham</name><name sortKey="Dassau, Eyal" sort="Dassau, Eyal" uniqKey="Dassau E" first="Eyal" last="Dassau">Eyal Dassau</name><name sortKey="Dassau, Eyal" sort="Dassau, Eyal" uniqKey="Dassau E" first="Eyal" last="Dassau">Eyal Dassau</name><name sortKey="Deshpande, Sunil" sort="Deshpande, Sunil" uniqKey="Deshpande S" first="Sunil" last="Deshpande">Sunil Deshpande</name><name sortKey="Deshpande, Sunil" sort="Deshpande, Sunil" uniqKey="Deshpande S" first="Sunil" last="Deshpande">Sunil Deshpande</name><name sortKey="Doyle, Francis J" sort="Doyle, Francis J" uniqKey="Doyle F" first="Francis J" last="Doyle">Francis J. Doyle</name><name sortKey="Doyle, Francis J" sort="Doyle, Francis J" uniqKey="Doyle F" first="Francis J" last="Doyle">Francis J. Doyle</name><name sortKey="Forlenza, Gregory P" sort="Forlenza, Gregory P" uniqKey="Forlenza G" first="Gregory P" last="Forlenza">Gregory P. Forlenza</name><name sortKey="Gondhalekar, Ravi" sort="Gondhalekar, Ravi" uniqKey="Gondhalekar R" first="Ravi" last="Gondhalekar">Ravi Gondhalekar</name><name sortKey="Gondhalekar, Ravi" sort="Gondhalekar, Ravi" uniqKey="Gondhalekar R" first="Ravi" last="Gondhalekar">Ravi Gondhalekar</name><name sortKey="Hahn, Juergen" sort="Hahn, Juergen" uniqKey="Hahn J" first="Juergen" last="Hahn">Juergen Hahn</name><name sortKey="Hahn, Juergen" sort="Hahn, Juergen" uniqKey="Hahn J" first="Juergen" last="Hahn">Juergen Hahn</name><name sortKey="Huyett, Lauren M" sort="Huyett, Lauren M" uniqKey="Huyett L" first="Lauren M" last="Huyett">Lauren M. Huyett</name><name sortKey="Huyett, Lauren M" sort="Huyett, Lauren M" uniqKey="Huyett L" first="Lauren M" last="Huyett">Lauren M. Huyett</name><name sortKey="Ly, Trang T" sort="Ly, Trang T" uniqKey="Ly T" first="Trang T" last="Ly">Trang T. Ly</name><name sortKey="Maahs, David M" sort="Maahs, David M" uniqKey="Maahs D" first="David M" last="Maahs">David M. Maahs</name><name sortKey="Marcal, Tatiana" sort="Marcal, Tatiana" uniqKey="Marcal T" first="Tatiana" last="Marcal">Tatiana Marcal</name><name sortKey="Mauritzen, Eric" sort="Mauritzen, Eric" uniqKey="Mauritzen E" first="Eric" last="Mauritzen">Eric Mauritzen</name><name sortKey="Pinsker, Jordan E" sort="Pinsker, Jordan E" uniqKey="Pinsker J" first="Jordan E" last="Pinsker">Jordan E. Pinsker</name><name sortKey="Towers, Lindsey" sort="Towers, Lindsey" uniqKey="Towers L" first="Lindsey" last="Towers">Lindsey Towers</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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