Patients with highly unstable type 1 diabetes eligible for islet transplantation can be managed with a closed-loop insulin delivery system: A series of N-of-1 randomized controlled trials.

AIM: To compare the efficacy of the closed-loop Diabeloop for highly unstable diabetes (DBLHU) system with the open-loop predictive low glucose suspend (PLGS) system in patients with highly unstable type 1 diabetes (T1D) who experience acute metabolic events. METHODS: DBLHU-WP10 was an interventional, controlled, randomized, open-label study that comprised two cycles of N-of-1 trials (2-of-1 trials). Each trial consisted of two crossover 4-week periods of treatment with either DBLHU or PLGS in randomized order. The primary outcome was the percentage of time spent in the 70-180 mg/dL glucose range (time in range [TIR]). RESULTS: Five out of seven randomized patients completed the aggregated 2-of-1 trials. TIR was significantly higher with DBLHU (73.3% ± 1.7%) compared with PLGS (43.5% ± 1.7%; P < .0001). The percentage of time below 70 mg/dL was significantly lower with DBLHU (0.9% ± 0.4%) versus PLGS (3.7% ± 0.4%; P < .0001). DBLHU was also significantly superior to PLGS in reducing hyperglycaemic excursions and improving almost all other secondary outcomes, including glucose variability and satisfaction score. No adverse event could be related to the experimental treatment. CONCLUSIONS: DBLHU was superior to PLGS in improving the metabolic control of patients with highly unstable T1D who require an islet or pancreas transplant but who either have a contraindication or refuse to consent.

No more hypoglycaemia on days with physical activity and unrestricted diet when using a closed-loop system for 12 weeks: A post hoc secondary analysis of the multicentre, randomized controlled Diabeloop WP7 trial.

A post hoc analysis of the Diabeloop WP7 multicentre, randomized controlled trial was performed to investigate the efficacy of the Diabeloop Generation-1 (DBLG1) closed-loop system in controlling the hypoglycaemia induced by physical activity (PA) in real-life conditions. Glycaemic outcomes were compared between days with and without PA in 56 patients with type 1 diabetes (T1D) using DBLG1 for 12 weeks. After the patient announces a PA, DBLG1 reduces insulin delivery and, if necessary, calculates the amount of preventive carbohydrates (CHO). Daily time spent in the interstitial glucose range less than 70 mg/dL was not significantly different between days with and without PA (2.0% ± 1.5% vs. 2.2% ± 1.1%), regardless of the intensity or duration of the PA. Preventive CHO intake recommended by the system was significantly higher in days with PA (41.1 ± 35.5 vs. 21.8 ± 28.5 g/day; P < .0001), and insulin delivery was significantly lower (31.5 ± 10.5 vs. 34.0 ± 10.5 U/day; P < .0001). The time spent in hyperglycaemia and the glycaemic variation coefficient increased significantly on days with PA. In real-life conditions, the use of DBLG1 avoids PA-induced hypoglycaemia. Insulin adjustments and preventive CHO recommendation may explain this therapeutic benefit.

Efficacy of the Diabeloop closed-loop system to improve glycaemic control in patients with type 1 diabetes exposed to gastronomic dinners or to sustained physical exercise.

AIMS: To compare closed-loop (CL) and open-loop (OL) systems for glycaemic control in patients with type 1 diabetes (T1D) exposed to real-life challenging situations (gastronomic dinners or sustained physical exercise). METHODS: Thirty-eight adult patients with T1D were included in a three-armed randomized pilot trial (Diabeloop WP6.2 trial) comparing glucose control using a CL system with use of an OL device during two crossover 72-hour periods in one of the three following situations: large (gastronomic) dinners; sustained and repeated bouts of physical exercise (with uncontrolled food intake); or control (rest conditions). Outcomes included time in spent in the glucose ranges of 4.4-7.8 mmol/L and 3.9-10.0 mmol/L, and time in hypo- and hyperglycaemia. RESULTS: Time spent overnight in the tight range of 4.4 to 7.8 mmol/L was longer with CL (mean values: 63.2% vs 40.9% with OL; P ≤ .0001). Time spent during the day in the range of 3.9 to 10.0 mmol/L was also longer with CL (79.4% vs 64.1% with OL; P ≤ .0001). Participants using the CL system spent less time during the day with hyperglycaemic excursions (glucose >10.0 mmol/L) compared to those using an OL system (17.9% vs 31.9%; P ≤ .0001), and the proportions of time spent during the day with hyperglycaemic excursions of those using the CL system in the gastronomic dinner and physical exercise subgroups were of similar magnitude to those in the control subgroup (18.1 ± 6.3%, 17.2 ± 8.1% and 18.4 ± 12.5%, respectively). Finally, times spent in hypoglycaemia were short and not significantly different among the groups. CONCLUSIONS: The Diabeloop CL system is superior to OL devices in reducing hyperglycaemic excursions in patients with T1D exposed to gastronomic dinners, or exposed to physical exercise followed by uncontrolled food and carbohydrate intake.

Unannounced Meal Detection for Artificial Pancreas Systems Using Extended Isolation Forest.

This study aims at developing an unannounced meal detection method for artificial pancreas, based on a recent extension of Isolation Forest. The proposed method makes use of features accounting for individual Continuous Glucose Monitoring (CGM) profiles and benefits from a two-threshold decision rule detection. The advantage of using Extended Isolation Forest (EIF) instead of the standard one is supported by experiments on data from virtual diabetic patients, showing good detection accuracy with acceptable detection delays.

Increasing the safety of unannounced meal detection for artificial pancreas closed-loop with patient's hourly meal schedule.

The daily challenge for people with type 1 diabetes is maintaining glycaemia in the "normal" range after meals, by injecting themselves the correct amount of insulin. Artificial pancreas systems were developed to adjust insulin delivery based on real-time monitoring of glycaemia and meal patient's report. Meal reporting is a heavy burden for patients as it requires carbohydrate estimation several times per day. To improve patient's quality of life and treatment, several methods aim at detecting unannounced meals. While untreated meals lead to hyperglycaemia and in the long-term to comorbidities, treating falsely detected meals can cause hypoglycaemia and coma. In this paper, we propose to customise the meal detection to the patient's hourly meal probability in order to limit false detection of unannounced meals.

Closed-loop insulin delivery in adults with type 1 diabetes in real-life conditions: a 12-week multicentre, open-label randomised controlled crossover trial.

BACKGROUND: Closed-loop insulin delivery systems are expected to become a standard treatment for patients with type 1 diabetes. We aimed to assess whether the Diabeloop Generation 1 (DBLG1) hybrid closed-loop artificial pancreas system improved glucose control compared with sensor-assisted pump therapy. METHODS: In this multicentre, open-label, randomised, crossover trial, we recruited adults (aged ≥18 years) with at least a 2 year history of type 1 diabetes, who had been treated with external insulin pump therapy for at least 6 months, had glycated haemoglobin (HbA1c) of 10% or less (86 mmol/mol), and preserved hypoglycaemia awareness. After a 2-week run-in period, patients were randomly assigned (1:1) with a web-based system in randomly permuted blocks of two, to receive insulin via the hybrid closed-loop system (DBLG1; using a machine-learning-based algorithm) or sensor-assisted pump therapy over 12 weeks of free living, followed by an 8-week washout period and then the other intervention for 12 weeks. The primary outcome was the proportion of time that the sensor glucose concentration was within the target range (3·9-10·0 mmol/L) during the 12 week study period. Efficacy analyses were done in the modified intention-to-treat population, which included all randomly assigned patients who completed both 12 week treatment periods. Safety analyses were done in all patients who were exposed to either of the two treatments at least once during the study. This trial is registered with ClinicalTrials.gov, number NCT02987556. FINDINGS: Between March 3, 2017, and June 19, 2017, 71 patients were screened, and 68 eligible patients were randomly assigned to the DBLG1 group (n=33) or the sensor-assisted pump therapy group (n=35), of whom five dropped out in the washout period (n=1 pregnancy; n=4 withdrew consent). 63 patients completed both 12 week treatment periods and were included in the modified intention-to-treat analysis. The proportion of time that the glucose concentration was within the target range was significantly higher in the DBLG1 group (68·5% [SD 9·4] than the sensor-assisted pump group (59·4% [10·2]; mean difference 9·2% [95% CI 6·4 to 11·9]; p<0·0001). Five severe hypoglycaemic episodes occurred in the DBLG1 group and three episodes occurred in the sensor-assisted pump therapy group, which were associated with hardware malfunctions or human error. INTERPRETATION: The DBLG1 system improves glucose control compared with sensor-assisted insulin pumps. This finding supports the use of closed-loop technology combined with appropriate health care organisation in adults with type 1 diabetes. FUNDING: French Innovation Fund, Diabeloop.

Customization of home closed-loop insulin delivery in adult patients with type 1 diabetes, assisted with structured remote monitoring: the pilot WP7 Diabeloop study.

AIMS: Improvement in closed-loop insulin delivery systems could result from customization of settings to individual needs and remote monitoring. This pilot home study evaluated the efficacy and relevance of this approach. METHODS: A bicentric clinical trial was conducted for 3 weeks, using an MPC-based algorithm (Diabeloop Artificial Pancreas system) featuring five settings designed to modulate the reactivity of regulation. Remote monitoring was ensured by expert nurses with a web platform generating automatic Secured Information Messages (SIMs) and with a structured procedure. Endpoints were glucose metrics and description of impact of monitoring on regulation parameters. RESULTS: Eight patients with type 1 diabetes (six men, age 41.8 ± 11.4 years, HbA1c 7.7 ± 1.0%) were included. Time spent in the 70-180 mg/dl range was 70.2% [67.5; 76.9]. Time in hypoglycemia < 70 mg/dl was 2.9% [2.1; 3.4]. Eleven SIMs led to phone intervention. Original default settings were modified in all patients by the intervention of the nurses. CONCLUSION: This pilot trial suggests that the Diabeloop closed-loop system could be efficient regarding metabolic outcomes, whereas its telemedical monitoring feature could contribute to enhanced efficacy and safety. This study is registered at ClinicalTrials.gov with trial registration number NCT02987556.

Automatic identification of physical activity types and sedentary behaviors from triaxial accelerometer: laboratory-based calibrations are not enough.

"Objective" methods to monitor physical activity and sedentary patterns in free-living conditions are necessary to further our understanding of their impacts on health. In recent years, many software solutions capable of automatically identifying activity types from portable accelerometry data have been developed, with promising results in controlled conditions, but virtually no reports on field tests. An automatic classification algorithm initially developed using laboratory-acquired data (59 subjects engaging in a set of 24 standardized activities) to discriminate between 8 activity classes (lying, slouching, sitting, standing, walking, running, and cycling) was applied to data collected in the field. Twenty volunteers equipped with a hip-worn triaxial accelerometer performed at their own pace an activity set that included, among others, activities such as walking the streets, running, cycling, and taking the bus. Performances of the laboratory-calibrated classification algorithm were compared with those of an alternative version of the same model including field-collected data in the learning set. Despite good results in laboratory conditions, the performances of the laboratory-calibrated algorithm (assessed by confusion matrices) decreased for several activities when applied to free-living data. Recalibrating the algorithm with data closer to real-life conditions and from an independent group of subjects proved useful, especially for the detection of sedentary behaviors while in transports, thereby improving the detection of overall sitting (sensitivity: laboratory model = 24.9%; recalibrated model = 95.7%). Automatic identification methods should be developed using data acquired in free-living conditions rather than data from standardized laboratory activity sets only, and their limits carefully tested before they are used in field studies.

Preliminary evaluation of a new semi-closed-loop insulin therapy system over the prandial period in adult patients with type 1 diabetes: the WP6.0 Diabeloop study.

There is room for improvement in the algorithms used in closed-loop insulin therapy during the prandial period. This pilot study evaluated the efficacy and safety of the Diabeloop algorithm (model predictive control type) during the postprandial period. This 2-center clinical trial compared interstitial glucose levels over two 5-hour periods (with/without the algorithm) following a calibrated lunch. On the control day, the amount of insulin delivered by the pump was determined according to the patient's usual parameters. On the test day, 50% or 75% of the theoretical bolus required was delivered, while the algorithm, informed of carbohydrate intake, proposed changes to insulin delivery every 15 minutes using modeling to forecast glucose levels. The primary endpoint was percentage of time spent at near normoglycemia (70-180 mg/dl). Twelve patients with type 1 diabetes (9 men, age 35.6 ± 12.7 years, HbA1c 7.3 ± 0.8%) were included. The percentage of time spent in the target range was 84.5 ± 20.8 (test day) versus 69.2 ± 33.9% (control day, P = .11). The percentage of time spent in hypoglycemia < 70 mg/dl was 0.2 ± 0.8 (test) versus 4.4 ± 8.2% (control, P = .18). Interstitial glucose at the end of the test (5 hours) was 127.5 ± 40.1 (test) versus 146 ± 53.5 mg/dl (control, P = .25). The insulin doses did not differ, and no differences were observed between the 50% and 75% boluses. In a semi-closed-loop configuration with manual priming boluses (25% or 50% reduction), the Diabeloop v1 algorithm was as successful as the manual method in determining the prandial bolus, without any exposure to excessive hypoglycemic risk.

Improving activity recognition using temporal coherence.

Assessment of daily physical activity using data from wearable sensors has recently become a prominent research area in the biomedical engineering field and a substantial application for pattern recognition. In this paper, we present an accelerometer-based activity recognition scheme on the basis of a hierarchical structured classifier. A first step consists of distinguishing static activities from dynamic ones in order to extract relevant features for each activity type. Next, a separate classifier is applied to detect more specific activities of the same type. On top of our activity recognition system, we introduce a novel approach to take into account the temporal coherence of activities. Inter-activity transition information is modeled by a directed graph Markov chain. Confidence measures in activity classes are then evaluated from conventional classifier's outputs and coupled with the graph to reinforce activity estimation. Accurate results and significant improvement of activity detection are obtained when applying our system for the recognition of 9 activities for 48 subjects.

Estimation of physical activity monitored during the day-to-day life by an autonomous wearable device (SVELTE project).

Physical activity (PA) and the energy expenditure it generates (PAEE) are increasingly shown to have impacts on everybody's health (e.g. development of chronic diseases) and to be key factors in maintaining the physical autonomy of elderlies. The SVELTE project objective was to develop an autonomous actimeter, easily wearable and with several days of autonomy, which could record a subject's physical activity during his/her daily life and estimate the associated energy expenditure. A few prototypes and dedicated algorithms were developed based on laboratory experiments. The identification of physical activity patterns algorithm shows good performances (79% of correct identification), based on a trial in semi-free-living conditions. The assessment of the PAEE computation algorithm is under validation based on a clinical trial.