Lal, Rayhan

Background: Given the faster onset and shorter duration of action of ultra-rapid insulin lispro-aabc (URLi, Lyumjev™ insulin), compared with insulin lispro (a rapid-acting insulin [RAI]), the present study investigated the safety and glycemic outcomes of MiniMed™ 780G advanced hybrid closed-loop (AHCL) use with URLi in youths and adults with T1D. Methods: This 19-site, single-arm, clinical trial enrolled children and adolescents (aged 7–17 years) and adults (aged 18–80 years) with T1D who underwent a run-in period (∼2 weeks) using open loop or automated basal only with an RAI (aspart or lispro), followed by a 3-month study period using URLi with AHCL (AHCL + URLi). End-of-study glycemic metrics and A1c were compared to predefined thresholds and to data from baseline/run-in. Use of recommended optimal settings (ROS, 100 mg/dL glucose target with an active insulin time [AIT] of 2 h) was also assessed. Results: In an intention-to-treat population ( N = 101 pediatric and N = 110 adult participants), percentage of time in range (%TIR) during the last ∼6 weeks of the study period was significantly greater than the predefined thresholds for both the pediatric and adult participants. A1c was unchanged in the pediatric cohort but significantly reduced in the adult cohort (from 7.4 ± 0.9% to 6.9 ± 0.6% [ P < 0.001]). Compared with run-in, study period %TIR and percentage of time in tight target range (%TITR) were significantly increased (pediatric mean %TIRΔ [17.4%] and %TITRΔ [16.0%], respectively, and adult mean %TIRΔ [10.6%] and %TITRΔ [11.6%], respectively; P < 0.001 for all). Additionally, % time above range for both groups was significantly reduced. Use of ROS allowed more participants to achieve recommended glycemic targets. There were no severe hypoglycemia or diabetic ketoacidosis events. Most caregivers agreed that mealtime versus premeal URLi delivery simplified pediatric meal management. Conclusions: Similar to previous studies investigating automated URLi delivery, URLi use in the MiniMed™ 780G system was safe, and glycemic outcomes were favorable for pediatric and adult participants.

Abstract Objective This work aims to guide clinicians practicing endocrinology in the use of telehealth (synchronous patient-clinician visits conducted over video or telephone) for outpatient care. Participants The Endocrine Society convened a 9-member panel of US endocrinologists with expertise in telehealth clinical care, telehealth operations, patient-centered care, health care delivery research, and/or evidence-based medicine. Evidence The panel conducted a literature search to identify studies published since 2000 about telehealth in endocrinology. One member extracted a list of factors affecting the quality of endocrine care via telehealth from the extant literature. The panel grouped these factors into 5 domains: clinical, patient, patient-clinician relationship, clinician, and health care setting and technology. Consensus Process For each domain, 2 or 3 members drew on existing literature and their expert opinions to draft a section examining the effect of the domain’s component factors on the appropriateness of telehealth use within endocrine practice. Appropriateness was evaluated in the context of the 6 Institute of Medicine aims for health care quality: patient-centeredness, equity, safety, effectiveness, timeliness, and efficiency. The panel held monthly virtual meetings to discuss and revise each domain. Two members wrote the remaining sections and integrated them with the domains to create the full policy perspective, which was reviewed and revised by all members. Conclusions Telehealth has become a common care modality within endocrinology. This policy perspective summarizes the factors determining telehealth appropriateness in various patient care scenarios. Strategies to increase the quality of telehealth care are offered. More research is needed to develop a robust evidence base for future guideline development.

Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.