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Sensor-Augmented Pump-Based Customized Mathematical Model for Type 1 Diabetes.

Benyamin Grosman1, Di Wu1, Diana Miller2

  • 11 Closed-Loop Development, Medtronic MiniMed, PLC , Northridge, California.

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|March 23, 2018
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Summary
This summary is machine-generated.

Mathematical models simulate virtual patients for type 1 diabetes therapy research. This new model, using real patient data, can predict artificial pancreas performance and improve diabetes management strategies.

Keywords:
Artificial pancreasSensor-augmented pumpsSimulation studiesType 1 diabetesVirtual patients

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Endocrinology

Background:

  • Mathematical models are crucial for developing and refining type 1 diabetes therapies.
  • Simulations aid in understanding disease progression and treatment efficacy.

Purpose of the Study:

  • To create a robust virtual patient model for diabetes research.
  • To validate the simulation model against real-world clinical data.
  • To establish a tool for predicting artificial pancreas performance.

Main Methods:

  • Utilized the Medtronic CareLink database to generate 2087 virtual patients.
  • Incorporated diverse parameters including insulin sensitivity, meal absorption, and pharmacokinetics.
  • Included dynamic changes in intra-insulin sensitivity over a 24-hour cycle.

Main Results:

  • Simulated virtual patients demonstrated comparable glycemic control to real patient data (74.1% vs 72.4% time in range 70-180 mg/dL).
  • Model accurately predicted outcomes of clinical trials, including insulin suspension effects and hybrid closed-loop system performance.
  • Low rates of hypoglycemia (<70 mg/dL) were predicted (1.7% vs 1% in real data).

Conclusions:

  • The Medtronic CareLink database successfully generated a large cohort of virtual patients.
  • This simulation model offers a powerful tool for evaluating and predicting outcomes of artificial pancreas algorithms and systems.
  • The model's ability to replicate clinical trial results highlights its potential for future diabetes research.