Background: A noninvasive, wearable continuous glucose monitor would be a major advancement in diabetes therapy. This trial investigated a novel noninvasive glucose monitor which analyzes spectral variations in radio frequency/microwave signals reflected from the wrist. Methods: A single-arm, open-label, experimental study compared glucose values from a prototype investigational device with laboratory glucose measurements from venous blood samples (Super GL Glucose Analyzer, Dr. Müller Gerätebau GmbH) at varying levels of glycemia. The study included 29 male participants with type 1 diabetes (age range = 19-56 years). The study comprised three stages with the following aims: (1) demonstrate initial proof-of-principle, (2) test an improved device design, and (3) test performance on two consecutive days without device recalibration. The co-primary endpoints in all trial stages were median and mean absolute relative difference (ARD) calculated across all data points. Results: In stage 1, the median and mean ARDs were 30% and 46%, respectively. Stage 2 produced marked performance improvements with a median and mean ARD of 22% and 28%, respectively. Stage 3 showed that, without recalibration, the device performed as well as the initial prototype (stage 1) with a median and mean ARD of 35% and 44%, respectively. Conclusion: This proof-of-concept study shows that a novel noninvasive continuous glucose monitor was capable of detecting glucose levels. Furthermore, the ARD results are comparable to first models of commercially available minimally invasive products without the need to insert a needle. The prototype has been further developed and is being tested in subsequent studies. Trial registration number: NCT05023798.
Background and Aims: To determine accuracy, safety and specificity of a novel non-invasive wrist-worn continuous BGM device which analyses resonance shifts in the microwave spectrum using AI. We present results from an ongoing study in patients with T1D and T2D from an expanded dataset (cohorts 1-3). Methods: In this open, pilot, adaptive design study, subjects (N=5/cohort) attended 4 test occasions (n=2/session), each ≤7 days apart. Devices automatically collected data every 60 secs for 500 msec over 3 hours/session, with plasma glucose measured every 5 mins. A global AI model was evaluated by MARD using venous blood glucose. Interim results from every 5 completed patients informed the next device iteration with 10 iterations possible across the study. Results: Data from each cohort was used to train a neural net algorithm to predict a new trial. Each cohort improved overall MARD prediction accuracy. All analyses followed a leave-one-trial-out methodology where the data for the omitted trial was predicted for each analysis cycle. Using the first cohort, the MARD was 21%, adding the second reduced MARD to 15% and with all 3 cohorts the predictive MARD was 13%. Improvement in the SEG plots showed data falling into low risk SEG categories. Conclusions: We have shown that by giving the neural network an expanded dataset, the MARD decreased to nearer commercially available minimally-invasive BGMs by 38% from a predictive MARD of 21% to 13%. Disclosure M.S.Chaudhry: None. G.J.Dunseath: None. J.Ryan: None. L.Barlow: None. I.C.Carrillo masso: Employee; Afon Technology Ltd. J.H.Crane: None. M.R.A.Qureshi: Employee; Afon Technology Ltd. S.C.Bain: Advisory Panel; Novo Nordisk, Sanofi, Lilly, Boehringer Ingelheim Inc., AstraZeneca. S.D.Luzio: None. C.M.Handy: None. B.Love: Consultant; Afon Technologies LTD. N.M.M.Silva: None. L.M.Ferreira: None. K.Wareham: None.
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