Leveraging technology for the treatment of type 1 diabetes in pregnancy: A review of past, current, and future therapeutic tools

Expert Guidance on Off-Label Use of Hybrid Closed-Loop Therapy in Pregnancies Complicated by Diabetes

Automated insulin delivery (AID) systems have established benefits in terms of glycemic control, health outcomes, and quality of life and are strongly recommended for people with type 1 diabetes outside of pregnancy. While evidence for use of investigational AID systems during pregnancy is promising, data and guidance are still needed regarding use of commercially available systems during pregnancy. Unfortunately, none of the hybrid closed-loop (HCL) systems that are currently available in the United States have glucose targets that are as aggressive as pregnancy glycemic targets, none have a pregnancy-specific algorithm, and none are approved for use during pregnancy. As such, any use of these systems during pregnancy is considered off-label in the United States and would be "assisted" by provider/user techniques. Despite these limitations, many women conceive while using clinically available HCL systems and may be hesitant to cease use during pregnancy. Achievement of strict pregnancy glycemic targets can be difficult, and it is conceivable that selective off-label use of clinically available HCL systems in some women could lead to improved glycemia. We herein offer expert guidance based on clinical experience and available case reports on how to identify appropriate candidates for HCL therapy in pregnancy, how to counsel pregnant women with diabetes on the potential risks and benefits of HCL therapy during pregnancy, and how to manage commercially available systems off-label throughout gestation in an assisted HCL approach.

Barriers to Uptake of Insulin Technologies and Novel Solutions

Diabetes-related technology has undergone great advancement in recent years. These technological devices are more commonly utilized in the type 1 diabetes population, which requires insulin as the primary treatment modality. Available devices include insulin pumps, continuous glucose monitors, and hybrid systems referred to as automated insulin delivery systems or hybrid closed-loop systems, which combine those two devices along with software algorithms to achieve advanced therapeutic capabilities, including automatic modulation of insulin delivery based on sensor-derived glucose levels to minimize abnormal glucose trends. Use of diabetes technology is associated with significant positive health and psychosocial outcomes, yet utilization rates are generally lacking across both adult and pediatric type 1 diabetes populations in the United States and other countries. There are consistent themes in existing barriers to technology uptake reported by individuals with type 1 diabetes or parents of children with type 1 diabetes, including physical burdens associated with wearing the devices, concerns in navigating the technology and the devices’ abilities to meet user expectations, high cost, inadequate resources within the healthcare team to support device use, disparities in technology access, and psychosocial barriers. It is important to understand the common barriers to uptake of not only the automated insulin delivery systems but also their component devices (insulin pumps and continuous glucose monitors) to fully support individuals in utilizing these devices and optimizing health benefits. The purpose of this article is to summarize the current automated insulin delivery devices that are available for use in management of type 1 diabetes, review common barriers to uptake of those systems and their component devices, and provide expert opinion on existing and future solutions to identified barriers.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.