Development of a personalized infusion failure detection algorithm combining tissue counter pressure and blood glucose data for closed-loop diabetes management

开发结合组织反压和血糖数据的个性化输注失败检测算法，用于闭环糖尿病管理

• 批准号: 10296116
• 负责人: John Clark Gray
• 金额: $ 30万
• 依托单位: DIATECH DIABETES, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10296116
• 关键词: 3-Dimensional; Address; Adhesions; Affect; Algorithms; Angiography; Artificial Pancreas; Blood Circulation; Blood Glucose; Bolus Infusion; Cannulas; Caring; Cessation of life; Characteristics; Clinical Data; Collection; Complex; Computer software; Data; Databases; Deposition; Detection; Development; Diabetes Mellitus; Diabetic Ketoacidosis; Diagnostic radiologic examination; Diffusion; Ensure; Equipment; Exercise; Failure; Family suidae; Glucose; Goals; Health; Hospital Costs; Hour; Hyperglycemia; Image; Immobilization; Infection; Infusion Pumps; Infusion procedures; Injury; Insulin; Insulin Infusion Systems; Insulin-Dependent Diabetes Mellitus; Label; Lead; Life; Life Style; Liquid substance; Machine Learning; Measures; Medical Care Costs; Medical Device; Methods; Microfluidics; Miniature Swine; Modeling; Modernization; Monitor; Noise; Patients; Performance; Pharmaceutical Preparations; Phase; Physiology; Prevention; Pump; Quality of life; Recommendation; Reporting; Research; Resistance; Risk; Safety; Skin; Small Business Innovation Research Grant; Source; Specimen; Symptoms; System; Technology; Testing; Time; Tissues; Training; Update; Vision; Work; base; cost; design; diabetes management; diabetic; experience; field study; fluid flow; glucose monitor; glycemic control; improved; in vivo; irritation; machine learning method; novel; pre-clinical; pressure; prevent; prototype; sensor; subcutaneous

PROJECT SUMMARY/ABSTRACT Advances in diabetes care technology over the past several decades, including improvements in continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM), have led to more streamlined treatments and reduced burden for patients with type 1 diabetes (T1D). As the field moves toward increasingly autonomous, closed-loop systems for management of T1D, infusion set failure (ISF) remains a health risk to patients and a barrier to the development of artificial pancreas systems. ISF, the disruption of fluid flow from insulin pump to patient causing loss of glycemic control, affects an estimated 50% of pump wearers, placing them at risk for hyperglycemia and life-threatening complications such as diabetic ketoacidosis. Because modern insulin pumps are not equipped with a mechanism for monitoring infusion performance, patients may not know an ISF has occurred until experiencing symptoms of hyperglycemia. There is no existing technology capable of accurately identifying ISF in real-time, before dysregulation of blood glucose (BG). Diatech Diabetes is addressing this unmet need with SmartFusion, an AI-based platform to monitor infusion performance, immediately alert patients when ISF occurs, and help users infuse confidently and safely. Diatech’s novel algorithm leverages tissue counter pressure (TCP) and CGM data to offer a superior method for ISF detection. The company’s long-term vision is that by detecting ISF in real time, SmartFusion will improve patients’ glycemic control, prevent complications of hyperglycemia, and reduce excess medical costs. Diatech has already collected preliminary data to show that TCP waveforms can feasibly be leveraged to differentiate healthy and malfunctioned infusions. The goal of this Phase I SBIR proposal is to collect increasingly complex and representative preclinical data to train and optimize the TCP-CGM algorithm and demonstrate proof-of-concept that it can accurately detect ISF. Diatech will pursue this goal through the following aims: 1) collect and characterize labeled in vivo TCP and x-ray imaging data of typical and malfunctioned infusions, 2) collect and characterize labeled in vivo TCP and BG/CGM data of failure modes consistent with ISF in immobilized diabetic swine, and 3) collect 3-day TCP and BG/CGM data from ambulatory diabetic swine with failure modes consistent with ISF. Successful completion of the project will result in a novel TCP-CGM algorithm to accurately detect ISF in closed- loop systems. The algorithm will be further optimized through collection and integration of clinical data in Phase II. SmartFusion will ultimately be integrated into insulin pumps and diabetes management platforms to provide real-time ISF detection and personalized recommendations for ISF prevention. Further, by enabling new avenues for control of closed-loop systems, SmartFusion can offer a significant leap forward for implementation of artificial pancreas technology, leading to improved health and quality of life for patients living with T1D.

项目总结/文摘