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YpsoPump Occlusion Detection Algorithm: Collection of Real-world Data for In-silico Evaluation of a New Software Algorithm to Refine Occlusion Detection in Subjects With Type 1 Diabetes Using Continuous Subcutaneous Insulin Infusion

NCT05096325 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 40

Last updated 2022-03-22

No results posted yet for this study

Summary

A common difficulty related to the insulin pumps are occlusions of the insulin infusion set (IIS). This study aims to evaluate the performance of a new software algorithm to detect catheter-occlusion in silico in order to refine the current automated occlusion detection algorithm of the mylife™ YpsoPump®.

Conditions

  • Diabetes Mellitus, Type 1

Interventions

DEVICE

YpsoPump® insulin pump system

The subjects will receive a CE-certified mylife™ YpsoPump® insulin pump system that allows detailed logging of pressure data. Data will then be analysed in silico comparing the new occlusion detection algorithm with the common occlusion detection algorithm.

Sponsors & Collaborators

  • DCB Research AG

    collaborator OTHER

  • Ypsomed Diabetes Care AG

    lead INDUSTRY

Principal Investigators

  • Ingo Braun · Ypsomed Diabetes Care AG

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-01-03
Primary Completion
2022-03-01
Completion
2022-03-01

Countries

  • Germany
  • Switzerland

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05096325 on ClinicalTrials.gov