First use of model predictive control in outpatient wearable artificial pancreas

  • Del Favero S
  • Bruttomesso D
  • Di Palma F
 et al. 
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{OBJECTIVE}: Inpatient studies suggest that model predictive control ({MPC}) is one of the most promising algorithms for artificial pancreas ({AP}). So far, outpatient trials have used hypo/hyperglycemia-mitigation or medical-expert systems. In this study, we report the first wearable {AP} outpatient study based on {MPC} and investigate specifically its ability to control postprandial glucose, one of the major challenges in glucose control.
{RESEARCH} {DESIGN} {AND} {METHODS}: A new modular {MPC} algorithm has been designed focusing on meal control. Six type 1 diabetes mellitus patients underwent 42-h experiments: sensor-augmented pump therapy in the first 14 h (open-loop) and closed-loop in the remaining 28 h.
{RESULTS}: {MPC} showed satisfactory dinner control versus open-loop: time-in-target (70-180 mg/{dL}) 94.83 vs. 68.2% and time-in-hypo 1.25 vs. 11.9%. Overnight control was also satisfactory: time-in-target 89.4 vs. 85.0% and time-in-hypo: 0.00 vs. 8.19%.
{CONCLUSIONS}: This outpatient study confirms inpatient evidence of suitability of {MPC}-based strategies for {AP}. These encouraging results pave the way to randomized crossover outpatient studies.

Author-supplied keywords

  • Adult
  • Algorithms
  • Artificial
  • Blood Glucose
  • Diabetes Mellitus
  • Female
  • Humans
  • Hyperglycemia
  • Hypoglycemia
  • Hypoglycemic Agents
  • Insulin
  • Insulin Infusion Systems
  • Male
  • Pancreas
  • Postprandial Period
  • Treatment Outcome
  • Type 1

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  • Simone Del Favero

  • Daniela Bruttomesso

  • Federico Di Palma

  • Giordano Lanzola

  • Roberto Visentin

  • Alessio Filippi

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