Estimating the cost-effectiveness of intermittently scanned continuous glucose monitoring in adults with type 1 diabetes in England.

OBJECTIVE: We previously showed that intermittently scanned continuous glucose monitoring (isCGM) reduces HbA1c at 24 weeks compared with self-monitoring of blood glucose with finger pricking (SMBG) in adults with type 1 diabetes and high HbA1c levels (58-97 mmol/mol [7.5%-11%]). We aim to assess the economic impact of isCGM compared with SMBG. METHODS: Participant-level baseline and follow-up health status (EQ-5D-5L) and within-trial healthcare resource-use data were collected. Quality-adjusted life-years (QALYs) were derived at 24 weeks, adjusting for baseline EQ-5D-5L. Participant-level costs were generated. Using the IQVIA CORE Diabetes Model, economic analysis was performed from the National Health Service perspective over a lifetime horizon, discounted at 3.5%. RESULTS: Within-trial EQ-5D-5L showed non-significant adjusted incremental QALY gain of 0.006 (95% CI: -0.007 to 0.019) for isCGM compared with SMBG and an adjusted cost increase of £548 (95% CI: 381-714) per participant. The lifetime projected incremental cost (95% CI) of isCGM was £1954 (-5108 to 8904) with an incremental QALY (95% CI) gain of 0.436 (0.195-0.652) resulting in an incremental cost-per-QALY of £4477. In all subgroups, isCGM had an incremental cost-per-QALY better than £20,000 compared with SMBG; for people with baseline HbA1c >75 mmol/mol (9.0%), it was cost-saving. Sensitivity analysis suggested that isCGM remains cost-effective if its effectiveness lasts for at least 7 years. CONCLUSION: While isCGM is associated with increased short-term costs, compared with SMBG, its benefits in lowering HbA1c will lead to sufficient long-term health-gains and cost-savings to justify costs, so long as the effect lasts into the medium term.

Impact of COVID-19 lockdown on flash and real-time glucose sensor users with type 1 diabetes in England.

AIMS: People with type 1 diabetes (T1D) face the daily task of implementing self-management strategies to achieve their glycaemic goals. The UK COVID-19 lockdown has had an impact on day-to-day behaviour, which may affect diabetes self-management and outcomes. We assessed whether sensor-based outcomes pre- and during lockdown periods were different in a cohort of glucose sensor users with T1D. METHODS: Data were collected from Freestyle Libre (FSL) or Dexcom G6 sensor users who remotely shared their data with the diabetes clinic web platform. Sensor metrics according to international consensus were analysed and compared between pre-lockdown period and 2 and 3 weeks into lockdown (periods 1 and 2). RESULTS: Two hundred and sixty-nine T1D patients (baseline HbA1c 57 ± 14 mmol/mol) were identified as FSL (n = 190) or Dexcom G6 (n = 79) users. In patients with sensor use > 70% (N = 223), compared to pre-lockdown period percentage TIR 3.9-10 mM (TIR) significantly increased during period 1 (59.6 ± 18.2 vs. 57.5 ± 17.2%, p = 0.002) and period 2 (59.3 ± 18.3 vs. 57.5 ± 17.2%, p = 0.035). The proportion of patients achieving TIR ≥ 70% increased from 23.3% pre-lockdown to 27.8% in period 1 and 30.5% in period 2. A higher proportion also achieved the recommended time below and above range, and coefficient of variation in periods 1 and 2. Dexcom G6 users had significantly lower % time below range (< 3.9 mM) compared to FSL users during both lockdown periods (period 1: Dexcom G6 vs. FSL: 1.8% vs. 4%; period 2: 1.4% vs. 4%, p < 0.005 for both periods). CONCLUSION: Sensor-based glycaemic outcomes in people with T1D in the current cohort improved during COVID-19 lockdown, which may be associated with positive changes in self-management strategies. Further work is required to evaluate long-term sustainability and support.

Evaluating Glucose Control With a Novel Composite Continuous Glucose Monitoring Index.

OBJECTIVE: The objective was to describe a novel composite continuous glucose monitoring index (COGI) and to evaluate its utility, in adults with type 1 diabetes, during hybrid closed-loop (HCL) therapy and multiple daily injections (MDI) therapy combined with real-time continuous glucose monitoring (CGM). METHODS: COGI consists of three key components of glucose control as assessed by CGM: Time in range (TIR), time below range (TBR), and glucose variability (GV) (weighted by 50%, 35% and 15%). COGI ranges from 0 to 100, where 1% increase of time <3.9 mmol/L (<70 mg/dl) is equivalent to 4.7% reduction of TIR between 3.9-10 mmol/L (70-180 mg/dl), and 0.5 mmol/L (9 mg/dl) increase in standard deviation is equivalent to 3% reduction in TIR. RESULTS: Continuous subcutaneous insulin infusion (CSII) users with HbA1c >7.5-10%, had significantly higher COGI during 12 weeks of HCL compared to sensor-augmented pump therapy, mean (SD), 60.3 (8.6) versus 69.5 (6.9), P < .001. Similarly, in CSII users with HbA1c <7.5%, HCL improved COGI from 59.9 (11.2) to 74.8 (6.6), P < .001. In MDI users with HbA1c >7.5% to 9.9%, use of real-time CGM led to improved COGI, 49.8 (14.2) versus 58.2 (9.1), P < .0001. In MDI users with impaired awareness of hypoglycemia, use of real-time CGM led to improved COGI, 53.4 (12.2) versus 66.7 (11.1), P < .001. CONCLUSIONS: COGI summarizes three key aspects of CGM data into a concise metric that could be utilized to evaluate the quality of glucose control and to demonstrate the incremental benefit of a wide range of treatment modalities.

Closing the Loop in Adults, Children and Adolescents With Suboptimally Controlled Type 1 Diabetes Under Free Living Conditions: A Psychosocial Substudy.

OBJECTIVE: The objective was to explore psychosocial experiences of closed loop technology for adults, children, and adolescents with type 1 diabetes and their parents taking part in two multicenter, free-living, randomized crossover home studies. METHODS: Participants using insulin pump therapy were randomized to either 12 weeks of automated closed-loop glucose control, then 12 weeks of sensor augmented insulin pump therapy (open loop), or vice versa. Closed loop was used for 24 hours by adults and overnight only by children and adolescents. Participants completed the Diabetes Technology Questionnaire (DTQ) periodically and shared their views in semistructured interviews. This analysis characterizes the impact of the technology, positive and negative aspects of living with the device, alongside participants' expectations, hopes, and anxieties. RESULTS: Participants were 32 adults, age 38.6 ± 9.6 years, 55% male, and 26 children, mean age 12 years (range 6-18 years), 54% male. DTQ results indicated moderately favorable impact of, and satisfaction with, both open and closed loop interventions, but little evidence of a comparative advantage of either. Key positive themes included perceived improved blood glucose control, improved general well-being, particularly on waking, improved sleep, reduced burden of diabetes, and visibility of data. Key negative themes included having to carry around the equipment and dislike of the pump and second cannula (ie, sensor) inserted. CONCLUSIONS: Overall, participants reported a positive experience of the closed loop technology. Results are consistent with previous research with size of equipment continuing to be a problem. Progress is being made in the usability of the closed-loop system.

Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery.

BACKGROUND: Closed-loop insulin delivery is an emerging treatment for type 1 diabetes (T1D) evaluated clinically and using computer simulations during pre-clinical testing. Efforts to make closed-loop systems available to people with type 2 diabetes (T2D) calls for the development of a new type of simulators to accommodate differences between T1D and T2D. Presented here is the development of a model of posthepatic endogenous insulin concentration, a component omitted in T1D simulators but key for simulating T2D physiology. METHODS: We evaluated six competing models to describe the time course of endogenous insulin concentration as a function of the plasma glucose concentration and time. The models were fitted to data collected in insulin-naive subjects with T2D who underwent two 24-h visits and were treated, in a random order, by either closed-loop insulin delivery or glucose-lowering oral agents. The model parameters were estimated using a Bayesian approach, as implemented in the WinBUGS software. Model selection criteria were used to identify the best model describing our clinical data. RESULTS: The selected model successfully described endogenous insulin concentration over 24 h in both study periods and provided plausible parameter estimates. Model-derived results were in concordance with a clinical finding which revealed increased posthepatic endogenous insulin concentration during the control study period (P < 0.05). The modelling results indicated that the excess amount of insulin can be attributed to the glucose-independent effect as the glucose-dependent effect was similar between visits (P > 0.05). CONCLUSIONS: A model to describe endogenous insulin concentration in T2D including components of posthepatic glucose-dependent and glucose-independent insulin secretion was identified and validated. The model is suitable to be incorporated in a simulation environment for evaluating closed-loop insulin delivery in T2D.