Evidence map›Paper›PMID 29390915›Full record

Trial reportJournal of diabetes science and technology2018

Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.

Daniel P Howsmon, Nihat Baysal, Bruce A Buckingham, Gregory P Forlenza, Trang T Ly, David M Maahs, Tatiana Marcal, Lindsey Towers, Eric Mauritzen, Sunil Deshpande and 7 more

Registry-linked trialAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of diabetes science and technology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02773875 (A Randomized Crossover Study to Evaluate the Efficacy of Fault Detection Algorithms Using the ZoneMPC Algorithm and DiAs System in Adult Subjects With Type 1 Diabetes in the Outpatient Setting), which is not on this map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02773875 naunknown statusnot on this map

A Randomized Crossover Study to Evaluate the Efficacy of Fault Detection Algorithms Using the ZoneMPC Algorithm and DiAs System in Adult Subjects With Type 1 Diabetes in the Outpatient Setting

TypeinterventionalSponsorRensselaer Polytechnic InstituteRan2016 to 2016Enrolled20ConditionsType 1 DiabetesArmsArtificial pancreas system (Algorithm + CGM + pump)
3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025
    Review
  2. Early Stages of Automated Insulin Delivery.Journal of diabetes science and technology · 2025
    Review
  3. Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025
    Review
  4. Early Stages of Automated Insulin Delivery.Diabetes technology & therapeutics · 2025
    Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Human-in-the-Loop Insulin Dosing.Journal of diabetes science and technology · 2021
    Article
  10. Review
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Daniel P Howsmon1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.ORCID 0000-0002-7177-1342
Nihat Baysal1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
Bruce A Buckingham2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.
Gregory P Forlenza3 Barbara Davis Center, University of Colorado Denver, Denver, CO, USA.
Trang T Ly2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.ORCID 0000-0002-5995-1447
David M Maahs2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.
Tatiana Marcal2 Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA.
Lindsey Towers3 Barbara Davis Center, University of Colorado Denver, Denver, CO, USA.
Eric Mauritzen4 Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA, USA.
Sunil Deshpande5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Lauren M Huyett6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.
Jordan E Pinsker6 Sansum Diabetes Research Institute, Santa Barbara, CA, USA.ORCID 0000-0003-4080-9034
Ravi Gondhalekar5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Francis J Doyle5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Eyal Dassau5 Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Juergen Hahn1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
B Wayne Bequette1 Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.

Funding

Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
Ambulatory Artificial Pancreas: merging physiology, behavior, and control designDP3DK094331 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI BASU, ANANDA, DOYLE, FRANCIS J · 2011 to 2014
$4.6M
Pediatric Artificial Pancreas System for Enhanced Diabetes Management in Young Children with Type 1 DiabetesDP3DK104057 · NIDDK · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI DASSAU, EYAL, DOYLE, FRANCIS J · 2014 to 2014
$1.8M
A probabilistic closed-loop artificial pancreas to handle unannounced meals R01DK102188 · NIDDK · RENSSELAER POLYTECHNIC INSTITUTE · PI BEQUETTE, B. WAYNE, BUCKINGHAM, BRUCE A · 2014 to 2016
$1.0M
NCATS NIH HHS UL1 TR001085NIDDK NIH HHS DP3 DK094331NIDDK NIH HHS DP3 DK104057NIDDK NIH HHS R01 DK102188
6 · The paper itself

Abstract

backgroundAs 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.

methodsAn 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.

resultsIn 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).

conclusionsAs 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.

Indexed as

AlgorithmsAdultCross-Over StudiesDiabetes Mellitus, Type 1Diabetic KetoacidosisEquipment FailureFemaleHumansHypoglycemic AgentsInsulinInsulin Infusion SystemsMaleMiddle AgedPancreas, ArtificialHypoglycemic AgentsInsulinartificial pancreasfault detectioninfusion site failuremodel predictive controlsafetytype 1 diabetes

Identifiers

PMID29390915
PMCPMC6154252

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.