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Cited by 32Diabetes Management and ResearchIntravenous Infusion Technology and SafetyDiabetes Management and Education

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

Open access
Howsmon, Daniel P., Baysal, Nihat, Buckingham, Bruce A., Forlenza, Gregory P., Ly, Trang T., Maahs, David M., Marcal, Tatiana, Towers, Lindsey +9 more·2018·Journal of Diabetes Science and Technology
DOI 10.1177/1932296818755173PMC PMC6154252
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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

AI snapshot

Written from the paper’s full text.

TL;DR

In a randomized crossover outpatient trial, a real-time infusion site failure detection algorithm under Zone-MPC closed-loop achieved 88.0% sensitivity with 0.22 false positives/day, compared with retrospective SAP performance of 73.3% sensitivity and 0.27 false positives/day. The algorithm detected failures earlier than patients in many cases, but additional studies are needed to characterize its impact on hyperglycemia.

Key findings

  • Real-time closed-loop Zone-MPC: sensitivity 88.0% (n = 25), 0.22 false positives/day (one FP alarm every 4.55 days), mean glucose at detection 260 mg/dL, and three false negatives.
  • Retrospective SAP: sensitivity 73.3% (n = 15), 0.27 false positives/day; if alerts had been acted on in real time, there would have been 33% less time spent in hyperglycemia in the four hours prior to SF detection compared with patient detection.
  • No association was observed between intervention strategy and infusion set duration (P = .58), nor with the number of sets lasting the full seven days (P = .64); there were 25 and 16 patient-confirmed SFs under Zone-MPC + SF detection and SAP, respectively.
  • Hyperglycemia duration (>250 mg/dL) in the four hours preceding an SF had median reductions from 92 to 63 minutes with retrospective SAP detection (P = .085) and from 63 to 30 minutes with real-time Zone-MPC detection (P = .060); excluding subject 13, reductions were 99 to 67 minutes (P = .086) and 67 to 23 minutes (P = .087).

Methods

  • Six-week outpatient randomized crossover controlled trial at Barbara Davis Center and Stanford University; 20 subjects enrolled, 19 completed (10 at Stanford, 9 at Barbara Davis), with 1:1 randomization. Group A started with Zone-MPC + SF detection and Group B with sensor-augmented pump (SAP); there was one week of washout, and each block consisted of one week of sensor run-in followed by two weeks of prolonged infusion set and sensor wear up to seven days.
  • Zone-MPC was implemented on the DiAs platform with Dexcom G4 PLATINUM CGM; the Roche Accu-Chek Spirit Combo pump was used in the Zone-MPC + SF detection arm, while each patient’s personal pump was used in the SAP arm. The SF detection algorithm used adaptive patient-specific glucose fault metric (GFM), insulin fault metric (IFM), and glucose slope thresholds; alarms were confirmed or refuted by patients and clinical staff.
  • Analysis assessed sensitivity and false positives per day on an alarm-event basis, compared infusion set survival using Kaplan-Meier estimates and log-rank tests, compared censored sets with chi-square tests, and analyzed relationships between hyperglycemia and carbohydrate intake via F-tests for regression.

Limitations

  • The paper states that additional studies are needed to fully characterize the effects of real-time SF detection and various intervention strategies on SF-associated hyperglycemia, and describes the work as a pilot study.
  • The paper notes three false negatives: one was detected very early by a diligent patient, and two were attributed to prolonged SF dynamics from gradual increases in insulin feedback under closed-loop control, slower than the open-loop cases on which the algorithm was trained.
  • Evident from the text: the sample was small (19 completers), each arm lasted only two weeks, the algorithm used a previously identified fixed parameter set, and false positives may alert patients to other inadequate-control scenarios such as unannounced meals because meal information was not provided to the algorithm.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1177/1932296818755173

Cites

40 of 40 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

7 of 30 cited sources checked · 6 supported · 0 flagged · accuracy not yet measured

  • Premisecitation
    § Conclusion

    Problems with infusion sets are a reason for patients discontinuing insulin pump therapy.

    “and a reason for patients discontinuing insulin pump therapy. 21,37-39”
    Quote in the paperSubstantial claim3 of 4 sources checked
    • cites [21] 21. Wong JC, Boyle C, DiMeglio LA, et al. Evaluation of pump discontinuation and associated factors in the T1D exchange clinical registry. J Diabetes Sci Technol . 2017;11(2):224-232. source
    • cites [37] 37. Wood JR, Moreland EC, Volkening LK, Svoren BM, Butler DA, Laffel LM. Durability of insulin pump use in pediatric patients with type 1 diabetes. Diabetes Care . 2006;29(11):2355-2360. source
    • cites [38] 38. de Vries L, Grushka Y, Lebenthal Y, Shalitin S, Phillip M. Factors associated with increased risk of insulin pump discontinuation in pediatric patients with type 1 diabetes. Pediatr Diabetes . 2011;12(5):506-512. source
    • cites [39] 39. Lombardo F, Scaramuzza AE, Iafusco D. Failure of glycated hemoglobin drop after continuous subcutaneous insulin infusion initiation may indicate patients who discontinue: a 4-year follow-up study in children and adolescents with type 1 diabetes. Acta Diabetol . 2012;49(1):99-105. source
  • Findingown data
    § Impact of SF Detection on SF-Induced Hyperglycemia

    Retrospective analysis of the SAP arm indicates a median reduction in hyperglycemia duration from 92 to 63 minutes (P = 0.085) if the SF detection algorithm had been active.

    “Retrospective analysis of the SAP arm indicates a median reduction in hyperglycemia duration from 92 to 63 minutes ( P = 0.085) if the SF detection algorithm had been active.”
    Quote in the paperSubstantial claim
  • Findingown data
    § Characteristics of SFs

    In 76 weeks of prolonged infusion set wear, there were 25 patient-confirmed SFs under Zone-MPC + SF detection and 16 under SAP.

    “In 76 weeks of prolonged infusion set wear, there were 25 and 16 patientconfirmed SFs under Zone-MPC + SF detection and SAP conditions, respectively (Table 4, Figure 2).”
    Quote in the paperNot a substantial claim
  • Findingown data
    § Results

    Nineteen patients (10 at Stanford and 9 at Barbara Davis) successfully completed the six-week study.

    “Nineteen patients (10 at Stanford and 9 at Barbara Davis) successfully completed the six-week study”
    Quote in the paperSubstantial claim
  • Findingown data
    § Characteristics of SFs

    No association was found between the intervention strategy and the number of infusion sets that lasted the full seven days (P = 0.64).

    “No association was found between the intervention strategy and the number of infusion sets that lasted the full seven days ( P = 0.64).”
    Quote in the paperNot a substantial claim
  • Classified as probably not a substantial claim. 28
  • May combine two claims. 17

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation
    § Characteristics of SFs

    The observed infusion set duration is comparable to infusion set durations observed in previous studies.

    “The observed infusion set duration is comparable to infusion set durations observed in previous studies. 15,29”
    Quote in the paperNot a substantial claim1 of 2 sources checked
    • cites [15] 15. Karlin AW, Ly TT, Pyle L, et al. Duration of infusion set survival in lipohypertrophy versus nonlipohypertrophied tissue in patients with type 1 diabetes. Diabetes Technol Ther . 2016;18(7):429-435. source
    • cites [29] 29. Patel PJ, Benasi K, Ferrari G, et al. Randomized trial of infusion set function: steel versus Teflon. Diabetes Technol Ther . 2013;16(1):15-19. source

  • May combine two claims.

    Interpretationcitation
    § Discussion

    The retrospective SAP sensitivity of 73.3% with 0.27 FPs/day agrees closely with previous results.

    “agreeing closely with previous results 22 (Table 3)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites [22] 22. Howsmon DP, Cameron F, Baysal N, et al. Continuous glucose monitoring enables the detection of Losses in Infusion Set Actuation (LISAs). Sensors . 2017;17(1):161. source

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation
    § Keywords

    SFs may be caused by catheter kinking, insulin precipitation, insulin leakage, site inflammation, set detachment, or otherwise unknown reasons and lead to hyperglycemia and ketonemia if left uncorrected.

    “SFs may be caused by catheter kinking, insulin precipitation, insulin leakage, site inflammation, set detachment, or otherwise unknown reasons and lead to hyperglycemia and ketonemia if left uncorrected.”
    Quote in the paperSubstantial claim1 of 2 sources checked
    • cites [15] 15. Karlin AW, Ly TT, Pyle L, et al. Duration of infusion set survival in lipohypertrophy versus nonlipohypertrophied tissue in patients with type 1 diabetes. Diabetes Technol Ther . 2016;18(7):429-435. source
    • cites [16] 16. Deiss D, Adolfsson P, Alkemade-van Zomeren M, et al. Insulin infusion set use: European perspectives and recommendations. Diabetes Technol Ther . 2016;18(9):517-524. source

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationcitation
    § Discussion

    Informed alarms dictating the appropriate patient-directed intervention potentially increase safety in advanced diabetes technology by improving the interactions between patients and technology.

    “Informed alarms dictating the appropriate patient-directed intervention potentially increase safety in advanced diabetes technology by improving the interactions between patients and technology. 29”
    Quote in the paperNot a substantial claim1 of 1 sources checked
    • cites [29] 29. Patel PJ, Benasi K, Ferrari G, et al. Randomized trial of infusion set function: steel versus Teflon. Diabetes Technol Ther . 2013;16(1):15-19. source

      The Introduction (pp. 15–19; passage page unspecified) describes patients checking glucose and changing infusion sets when glucose did not improve. A search for alarms and alerts found no passage addressing whether alarms that specify patient interventions improve safety or patient–technology interactions. (LLM, conf=0.73)

      Experimental — accuracy not yet measured
    • Rests on an assumption: The root causes of hyperglycemia (insufficient insulin administration versus SF) require very different interventions of giving an additional insulin bolus or replacing the infusion set.

  • May combine two claims.

    Findingown data
    § Discussion

    SFs were detected at a higher sensitivity of 88.0% and while only issuing 0.22 FPs/day under real-time, closed-loop conditions.

    “Furthermore, SFs were detected at a higher sensitivity of 88.0% and while only issuing 0.22 FPs/day under real-time, closed-loop conditions (Table 3).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Characteristics of SFs

    The median first-weekly infusion set duration coincided with the censoring time of seven days.

    “Furthermore, the median first-weekly infusion set duration coincided with the censoring time of seven days (Figure 2).”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Results

    The study led to 38 patient-weeks of prolonged infusion set wear under both SAP and Zone-MPC + SF detection scenarios, for a total of 76 patient-weeks.

    “leading to 38 patient-weeks of prolonged infusion set wear under both SAP and Zone-MPC + SF detection scenarios for a total of 76 patient-weeks.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingnone given
    § Discussion

    The glucose remained elevated overnight and into the next day despite frequent boluses.

    “However, the glucose remained elevated overnight and into the next day despite frequent boluses.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given
    § Discussion

    The patient experienced a slight trend upward in glucose values during the previous day, with normal glucose excursion due to manual bolusing and meals.

    “The patient experienced a slight trend upward in their glucose values during the previous day, with normal glucose excursion due to manual bolusing and meals.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingreasoning
    § Discussion

    The infusion set was finally deemed unresponsive to correction boluses and was replaced.

    “The infusion set was finally deemed unresponsive to correction boluses and was replaced.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning
    § Discussion

    An FP was issued after the glucose level reached 300 mg/dL, but the set was not deemed to have failed at this point because a correction bolus had its intended effect.

    “An FP was issued after the glucose level reached 300 mg/dL, but the set was not deemed to have failed at this point because a correction bolus had its intended effect.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning
    § Discussion

    The SF detection algorithm issued an FP, indicating that it had detected the unusual trends in glucose and insulin; however, its failure to continue issuing alarms resulted in the FN.

    “The SF detection algorithm issued an FP, indicating that it had detected the unusual trends in glucose and insulin; however, its failure to continue issuing alarms resulted in the FN.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data
    § Discussion

    The other two undetected SFs presented in a similar way.

    “The other two undetected SFs presented in a similar way (Figure 5B), thus only one will be discussed in detail.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning
    § Discussion

    Since the SF detection algorithm requires glucose to be elevated relative to each patient's specific baseline, FPs may alert patients to other scenarios of inadequate control, such as unannounced meals.

    “Since the SF detection algorithm requires the glucose to be elevated relative to each patient's specific baseline, FPs may alert patients to other scenarios of inadequate control, such as unannounced meals.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning
    § Retrospective Analysis of SF Detection Under Open-Loop

    Since specificity cannot be determined on a per event basis, the FP rate is reported as the average number of FPs per day.

    “Since specificity cannot be determined on a per event basis, the FP rate is reported as the average number of FPs per day.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning
    § Keywords

    Early SF detection promises to decrease both exposure to hyperglycemia and incidence of DKA.

    “Early SF detection promises to decrease both exposure to hyperglycemia and incidence of DKA.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given
    § SF Detection

    Sliding window lengths and thresholds were selected to simultaneously maximize the sensitivity and reduce the number of false positives per day.

    “Sliding window lengths and thresholds were selected to simultaneously maximize the sensitivity and reduce the number of false positives per day.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisenone given
    § Keywords

    Although SFs are often classified into different failure modes for diagnostic purposes, they result in the same action from the patient, that is, inserting a new infusion set in a different location.

    “Although SFs are often classified into different failure modes for diagnostic purposes, they result in the same action from the patient, that is, inserting a new infusion set in a different location.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductreasoning
    § Statistical Analysis

    The major difference between the real-time and retrospective analysis is that SF start times do not need to be calculated as the infusion set status is determined by the patient and clinical staff for each alarm.

    “The major difference between the real-time and retrospective analysis is that SF start times do not need to be calculated as the infusion set status is determined by the patient and clinical staff for each alarm.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § Keywords

    Advanced device technology, including continuous glucose monitors (CGMs) and continuous subcutaneous insulin infusion (CSII) pumps, improves care for patients living with type 1 diabetes and has recently moved to closed-loop artificial pancreas (AP) systems.

    “Advanced device technology, including continuous glucose monitors (CGMs) and continuous subcutaneous insulin infusion (CSII) pumps, improves care for patients living with type 1 diabetes and has recently moved to closed-loop artificial pancreas (AP) systems.”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [1] 1. Bequette BW. Challenges and recent progress in the development of a closed-loop artificial pancreas. Annu Rev Control . 2012;36(2):255-266.

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [2] 2. Peyser T, Dassau E, Breton M, Skyler JS. The artificial pancreas: current status and future prospects in the management of diabetes. Ann N Y Acad Sci . 2014;1311(1):102-123. source
    • cites [3] 3. Thabit H, Hovorka R. Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia . 2016;59(9):1795-1705. source

      Queued — the background check reaches it, usually within a day.

    • cites [4] 4. Garg SK, Weinzimer SA, Tamborlane WV, et al. Glucose outcomes with the in-home use of a hybrid closed-loop insulin delivery system in adolescents and adults with type 1 diabetes. Diabetes Technol Ther . 2017;19(3):155-163. source

      Queued — the background check reaches it, usually within a day.

  • May combine two claims.

    Interpretationown data
    § Discussion

    The SF detection algorithm is applicable to both open-loop and closed-loop scenarios, with arguably better performance in closed-loop.

    “Overall, these results suggest that the SF detection algorithm is applicable to both open-loop and closed-loop scenarios, with arguably better performance in closed-loop.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationreasoning
    § Keywords

    Detecting SFs would increase trust in an AP, unless there was a high incidence of false alarms.

    “Detecting SFs would increase trust in an AP, unless there was a high incidence of false alarms.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given
    § Keywords

    Only manual intervention from the patient in replacing the infusion set can restore satisfactory glycemic control and prevent eventual development of diabetic ketoacidosis.

    “Only manual intervention from the patient in replacing the infusion set can restore satisfactory glycemic control and prevent eventual development of diabetic ketoacidosis.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given
    § Study Design

    Each SF event was confirmed or refuted (and thus classified as true positive or false positive) following immediate communication with the patient, and patients were instructed to change infusion sets and infusion set locations for confirmed SFs.

    “Each SF event was confirmed or refuted (and thus classified as true positive or false positive events) following immediate communication with the patient and patients were instructed to change infusion sets and infusion set locations for confirmed SFs.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § Study Design

    Randomization occurred in a 1:1 ratio at each clinical site.

    “Randomization occurred in a 1:1 ratio at each clinical site.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § Retrospective Analysis of SF Detection Under SAP

    One SF was removed from analysis due to a CGM dropout that started three hours before and continued until two hours after the documented SF.

    “One SF was removed from analysis due to a CGM dropout that started three hours before and continued until two hours after the documented SF.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationnone given
    § Discussion

    Real-time analysis can better assess the performance of these algorithms than simulated scenarios with assumed SF dynamics.

    “Moreover, real-time analysis can better assess the performance of these algorithms than simulated scenarios with assumed SF dynamics.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationown data
    § Discussion

    This SF occurred on the eleventh day of the closed-loop portion of the trial and the patient had already experienced five SFs in this study arm, potentially increasing this patient's attention to their infusion set.

    “This SF occurred on the eleventh day of the closed-loop portion of the trial and the patient had already experienced five SFs in this study arm, potentially increasing this patient's attention to their infusion set.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductnone given
    § Study Design

    The study was approved by the US Food and Drug Administration and the institutional review boards at Stanford University and the University of Colorado Denver.

    “The study (www.clinicaltrials.gov,NCT02773875) was approved by the US Food and Drug Administration and the institutional review boards at Stanford University and the University of Colorado Denver.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § Results

    One participant was excluded midstudy due to failure to comply with the study protocol.

    “One participant was excluded midstudy due to failure to comply with the study protocol.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § Keywords

    The incidence of DKA is not increased by CSII pump use over multiple daily injections.

    “Although the incidence of DKA is not increased by CSII pump use over multiple daily injections”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [17]

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites [18] 18. Maahs DM, Hermann JM, Holman N, et al. Rates of diabetic ketoacidosis: international comparison with 49,859 pediatric patients with type 1 diabetes from England, Wales, the U.S., Austria, and Germany. Diabetes Care . 2015;38(10):1876-1882. source

  • Classified as probably not a substantial claim.

    Premisecitation
    § Conclusion

    It is currently recommended that insulin infusion sets are changed every two to three days.

    “It is currently recommended that insulin infusion sets are changed every two to three days, 16,28”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [16] 16. Deiss D, Adolfsson P, Alkemade-van Zomeren M, et al. Insulin infusion set use: European perspectives and recommendations. Diabetes Technol Ther . 2016;18(9):517-524. source

      Not checked in the free run.

    • cites [28] 28. Pfützner A, Sachsenheimer D, Grenningloh M, et al. Using insulin infusion sets in CSII for longer than the recommended usage time leads to a high risk for adverse events: results from a prospective randomized crossover study. J Diabetes Sci Technol . 2015;9(6):1292-1298. source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § Conclusion

    More reliable infusion sets and SF detection algorithms may potentially reduce the diabetes technology discontinuation rate by reducing patient burnout with changing infusion sets and relieving concerns over complications related to SFs.

    “More reliable infusion sets and SF detection algorithms may potentially reduce the diabetes technology discontinuation rate by reducing patient burnout with changing infusion sets and relieving concerns over complications related to SFs.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning
    § Discussion

    The failure to continuously issue an alarm is attributed to the increased insulin delivery due to the feedback control in the artificial pancreas, enabling the fault to present with slower dynamics than in the open-loop cases on which the algorithm was trained.

    “The failure to continuously issue an alarm is attributed to the increased insulin delivery due to the feedback control in the artificial pancreas, enabling the fault to present with slower dynamics than in the open-loop cases on which the algorithm was trained.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Interpretationreasoning
    § Conclusion

    Alarm systems that alert patients to specific manual interventions can potentially increase market penetration of artificial pancreas systems by enhancing human/device interactions and ameliorating concerns over troubleshooting these devices.

    “Furthermore, alarm systems that alert patients to specific manual interventions, such as providing a correction dose, changing an infusion set, or taking a specific amount of carbohydrates for treating hypoglycemia, can potentially increase market penetration of artificial pancreas systems by enhancing human/device interactions and ameliorating concerns over troubleshooting these devices.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning
    § Discussion

    Each fault metric in the SF detection algorithm was either increasing or had already exceeded its threshold, indicating that the SF detection algorithm may have detected this event, albeit at a later time than the patient.

    “It should be noted that each fault metric in the SF detection algorithm was either increasing or had already exceeded its threshold, indicating that the SF detection algorithm may have detected this event, albeit at a later time than the patient.”
    Quote in the paperNot a substantial claim
36 of 36

  • The paper relies on this without support.

    There has been no study to date that investigates real-time SF detection, where patients and clinical personnel can immediately give feedback on alarm performance and missed SF events.

    “However, there has been no study to date that investigates real-time SF detection, where patients and clinical personnel can immediately give feedback on alarm performance (ie, true/false positives) and missed SF events.”

  • The paper relies on this without support.

    SFs are the most common and most problematic issue with infusion pump therapy and will continue to occur with artificial pancreas systems.

    “SFs are the most common and most problematic issue with infusion pump therapy and will continue to occur with artificial pancreas systems.”

  • The authors state this as an assumption.

    An alert would have been responded to in real time.

    “if an alert had been responded to in real time”

  • The paper relies on this without support.

    Current alarm systems for advanced diabetes technology focus on alerting patients of current or impending hyper- and hypoglycemia.

    “Current alarm systems for advanced diabetes technology focus on alerting patients of current or impending hyper- and hypoglycemia.”

  • The paper relies on this without support.

    The root causes of hyperglycemia (insufficient insulin administration versus SF) require very different interventions of giving an additional insulin bolus or replacing the infusion set.

    “These root causes of hyperglycemia require very different interventions of either giving an additional insulin bolus or replacing the infusion set, respectively.”
5 of 5

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Zone-MPC failure total differs between text and Table 4

    Major

    The text reports 25 patient-confirmed failures under Zone-MPC, but Table 4 totals 24. The table's failure-mode counts also sum to 24.

  • V2 sensitivity is incompatible with its failure count

    Minor

    Table 3 lists 10 SFs for V2. With 10 failures, an integer number detected cannot yield the reported sensitivity of 71.4%; seven detected would yield 70.0%.

2 of 2

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Authors
Howsmon, D. P., Baysal, N., Buckingham, B. A., Forlenza, G. P., Ly, T. T., Maahs, D. M., Marcal, T., Towers, L., Mauritzen, E., Deshpande, S., Huyett, L. M., Pinsker, J. E., Gondhalekar, R., Doyle, F. J., Dassau, E., Hahn, J., & Bequette, B. W.
Affiliations
  • Rensselaer Polytechnic Institute (US)
  • Stanford Medicine (US)
  • University of Colorado Denver (US)
  • University of California San Diego (US)
  • Harvard University (US)
  • University of California, Santa Barbara (US)
  • Sansum Diabetes Research Institute (US)
Corresponding author
Bequette, B. Wayne
ORCID
Venue
Journal of Diabetes Science and Technology
Publisher
SAGE Publications
Published
2018-02-01
Volume
12
Issue
3
Pages
599-607
ISSN
1932-2968 (ISSN-L)
DOI
10.1177/1932296818755173
PMC
PMC6154252
OpenAlex
W2787658827
Cited by
32
References
39
Citations by year
2025: 7 · 2024: 3 · 2023: 5 · 2022: 1 · 2021: 3 · 2020: 3 · 2019: 7 · 2018: 2 · 2016: 1
FWCI
2.89
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Journal of Diabetes Science and Technology (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • Juvenile Diabetes Research Foundation (17-2013-471)
  • National Institutes of Health (DP3DK094331, DP3DK104057, UL1 TR001085)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Physical Sciences › Engineering › Biomedical Engineering › Intravenous Infusion Technology and Safety
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords
type 1 diabetes, fault detection algorithm, real-time alerts, sensor-augmented pump, artificial pancreas, hyperglycemia prevention, ketosis prevention
MeSH terms
Adult, Algorithms, Diabetes Mellitus, Type 1, Equipment Failure, Female, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Male, Middle Aged, Diabetic Ketoacidosis, Cross-Over Studies, Pancreas, Artificial

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-11-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-11-06.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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