# Improving glucose control with advanced technology designed for high risk patients with type 1 diabetes

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $599,203

## Abstract

Summary
The objective of this proposal is to optimize the design and evaluate a robust artificial pancreas (R-AP) system
for use in patients with uncontrolled type 1 diabetes (T1D) with HbA1C greater than 8% and compare HbA1C
outcomes in these patients relative to a decision support system that utilizes continuous glucose monitoring
(CGM) and multiple daily injection (MDI) therapy. Although high risk patients have possibly the most to gain
from usage of AP technology, they are oftentimes under-represented or excluded from clinical trials. This has
been because of the increased risk of failure of these AP systems that were not designed to handle
inconsistent reporting of meals, variable activity level, and infusion set failures. An AP system for high risk
patients needs to be designed to achieve maximal benefit, including reducing the risk of acute and chronic
complications. A major obstacle for enabling the AP for usage by high-risk patients is that these patients may
be less compliant with use guidelines for the system including missed meal announcements, infrequent sensor
calibrations, and prolonged infusion set wear leading to infusion set failures. In this grant, we will integrate new
risk-mitigation features into the OHSU single-hormone AP to enable usage by high-risk patients that fall into
the categories described above. We present new algorithms for automating the detection of missed meal
announcements, missed calibrations, and robust handling of hybrid usage mode. While AP systems may be
an optimal choice for improving glycemic control, many people with T1D prefer MDI therapy. Decision support
systems such as the DailyDose decision support system developed at OHSU can be used to improve glycemic
control for patients who prefer MDI therapy. The DailyDose decision support system is designed for CGM
augmented MDI therapy. It enables on-demand calculation of insulin doses, automates insulin dose
adjustments based on pattern recognition, and uses machine learning approaches to alert the patients to
events such as predicted hypoglycemia and missed meal doses.The benefit of the DailyDose system is that it
is a simple system and does not require use of an insulin pump, which may be a challenge for some patients
with uncontrolled type 1 diabetes as pump therapy is more intensive and requires infusion set changes. It is
unknown in this high risk group of people whether patient needs, quality of life, and glycemic control are best
addressed with an AP system or decision support tool or if both treatments are appropriate. We have designed
a 3-month clinical study to compare glycemic outcomes during AP vs. decision support interventions in a high-
risk T1D cohort (HbA1C 8-10.5%), with the aim of demonstrating a significant clinically relevant reduction in
HbA1C. Our hypothesis is that both AP and decision support therapies will decrease HbA1C relative to
baseline but that the AP will provide further benefit over DailyDose.

## Key facts

- **NIH application ID:** 9969424
- **Project number:** 5R01DK120367-03
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Jessica R Castle
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $599,203
- **Award type:** 5
- **Project period:** 2018-09-30 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9969424

## Citation

> US National Institutes of Health, RePORTER application 9969424, Improving glucose control with advanced technology designed for high risk patients with type 1 diabetes (5R01DK120367-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9969424. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*