Patricio Colmegna, United States of America

University of Virginia Center For Diabetes Technology
I am a Control Engineer by formation, and my research interests include modeling, optimization, and control of mechanical and biomedical processes. Since I started my doctorate studies in 2010, I have been working on technology-based solutions to improve glucose control in type 1 diabetes. I initially focused my research on designing artificial pancreas systems that automatically regulate the blood glucose level while minimizing patient intervention. More recently, I have also started analyzing and designing clinical decision support systems that optimize self-management practices.

Presenter of 1 Presentation

 Conference Calendar
ORAL PRESENTATION SESSION

ENABLING PATIENT-DATA INTERACTIONS IN TYPE 1 DIABETES THROUGH A CLOUD-BASED SIMULATION TOOL

Date
Wed, 02.06.2021
Session Name
0070 - Oral Presentation 03 (ID 68)
Lecture Time
19:15 - 19:25
Presenter
Authors

Abstract

Background and Aims

Advanced insulin therapy in type 1 diabetes (T1D) relies on key individual treatment profiles such as basal rate, carbohydrate ratio, and correction factor. Periodic adjustments of these profiles are needed based on review of data that usually require manual downloads from multiple devices. The aim of this project is to design and test a novel, cloud-based, centralized platform – the Web-Based Simulation Tool (WST) – that allows users to quickly and safely explore changes to their treatment practices.

Methods

WST automatically collects data from the patients’ insulin pumps via Tandem t:connect technology and generates personalized models of their glucose metabolism on a daily basis. It is equipped with a simple user-interface where users can visualize their data, run simulations with modified meals and insulin parameters, and generate reports. An outpatient pilot study with fifteen adult participants with T1D is currently being conducted to evaluate WST usability and performance.

Results

WST has already processed 233 days of data from which it was able to generate 195 models (83.69% success rate) with an average RMSE and MARD of 15.98±6.45 mg/dl and 7.95±3.15%, respectively, and with 99.59% of reconstructed glucose values in the A- and B-zones of the Clarke Error Grid. Analysis of responses to technology expectation/acceptance and psychobehavioral questionnaires will be completed at the end of the trial.

Conclusions

Simulation technologies helps leverage the vast amount of diabetes data currently available, enabling novel patient-data interactions that could facilitate decision-making processes related to the optimization of T1D treatment strategies.

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