Day-and-Night Closed-Loop Glucose Control in Patients With Type 1 Diabetes Under Free-Living Conditions: Results of a Single-Arm 1-Month Experience Compared With a Previously Reported Feasibility Study of Evening and Night at Home

Use of wearables for monitoring cardiometabolic health: A systematic review

INTRODUCTION

Cardiometabolic disorders (CMD) such as hyperglycemia, obesity, hypertension, and dyslipidemia are the leading causes of mortality and significant public health concerns worldwide. With the advances in wireless technology, wearables have become popular for health promotion, but its impact on cardiometabolic health is not well understood.

PURPOSE

A systematic literature review aimed to describe the features of wearables used for monitoring cardiometabolic health and identify the impact of using wearables on those cardiometabolic health indicators.

METHODS

A systematic search of PubMed, CINAHL, Academic Search Complete, and Science and Technology Collection databases was performed using keywords related to CMD risk indicators and wearables. The wearables were limited to sensors for blood pressure (BP), heart rate (HR), electrocardiogram (ECG), glucose, and cholesterol.

INCLUDED STUDIES

1) were published from 2016 to March 2021 in English, 2) focused on wearables external to the body, and 3) examined wearable use by individuals in daily life (not by health care providers). Protocol, technical, and non-empirical studies were excluded.

RESULTS

Out of 53 studies, the types of wearables used were smartwatches (45.3%), patches (34.0%), chest straps (22.6%), wristbands (13.2%), and others (9.4%). HR (58.5%), glucose (28.3%), and ECG (26.4%) were the predominant indicators. No studies tracked BP or cholesterol. Additional features of wearables included physical activity, respiration, sleep, diet, and symptom monitoring. Twenty-two studies primarily focused on the use of wearables and reported direct impacts on cardiometabolic indicators; seven studies used wearables as part of a multi-modality approach and presented outcomes affected by a primary intervention but measured through CMD-sensor wearables; and 24 validated the precision and usability of CMD-sensor wearables.

CONCLUSION

The impact of wearables on cardiometabolic indicators varied across the studies, indicating the need for further research. However, this body of literature highlights the potential of wearables to promote cardiometabolic health.

Developing the UVA/Padova Type 1 Diabetes Simulator: Modeling, Validation, Refinements, and Utility

Arguably, diabetes mellitus is one of the best quantified human conditions. In the past 50 years, the metabolic monitoring technologies progressed from occasional assessment of average glycemia via HbA1c, through episodic blood glucose readings, to continuous glucose monitoring (CGM) producing data points every few minutes. The high-temporal resolution of CGM data enabled increasingly intensive treatments, from decision support assisting insulin injection or oral medication, to automated closed-loop control, known as the "artificial pancreas." Throughout this progress, mathematical models and computer simulation of the human metabolic system became indispensable for the technological progress of diabetes treatment, enabling every step, from assessment of insulin sensitivity via the now classic Minimal Model of Glucose Kinetics, to in silico trials replacing animal experiments, to automated insulin delivery algorithms. In this review, we follow these developments, beginning with the Minimal Model, which evolved through the years to become large and comprehensive and trigger a paradigm change in the design of diabetes optimization strategies: in 2007, we introduced a sophisticated model of glucose-insulin dynamics and a computer simulator equipped with a "population" of N = 300 in silico "subjects" with type 1 diabetes. In January 2008, in an unprecedented decision, the Food and Drug Administration (FDA) accepted this simulator as a substitute to animal trials for the pre-clinical testing of insulin treatment strategies. This opened the field for rapid and cost-effective development and pre-clinical testing of new treatment approaches, which continues today. Meanwhile, animal experiments for the purpose of designing new insulin treatment algorithms have been abandoned.

Human Digital Twin for Personalized Elderly Type 2 Diabetes Management

Managing Elderly type 2 diabetes (E-T2D) is challenging due to geriatric conditions (e.g., co-morbidity, multiple drug intake, etc.), and personalization becomes paramount for precision medicine. This paper presents a human digital twin (HDT) framework to manage E-T2D that exploits various patient-specific data and builds a suite of models exploiting the data for prediction and management to personalize diabetes treatment in E-T2D patients. These models include mathematical and deep-learning ones that capture different patient aspects. Consequently, the HDT virtualizes the patient from different viewpoints using an HDT that mimics the patient and has interfaces to update the virtual models simultaneously from measurements. Using these models the HDT obtains deeper insights about the patient. Further, an adaptive patient model fusing this information and a learning-based model predictive control (LB-MPC) algorithm are proposed. The geriatric conditions are captured as model parameters and constraints while solving the LB-MPC to personalize the insulin infusion for E-T2D management. The HDT is deployed on and illustrated with 15 patients using clinical trials and simulations. Our results show that HDT helps improve the time-in-range from 3–75% to 86–97% and reduces insulin infusion by 14–29%.

Automated insulin delivery systems: from early research to routine care of type 1 diabetes

Automated insulin delivery (AID) systems, so-called closed-loop systems or artificial pancreas, are based upon the concept of insulin supply driven by blood glucose levels and their variations according to body glucose needs, glucose intakes and insulin action. They include a continuous glucose monitoring device which provides a signal to a control algorithm tuning insulin delivery from an infusion pump. The control algorithm is the key of the system since it commands insulin administration in order to maintain blood glucose in a predefined target range and close to a near-normal glucose level. The last two decades have shown dramatic advances toward the use in free life of AID systems for routine care of type 1 diabetes through step-by-step demonstrations of feasibility, safety and efficacy in successive hospital, transitional and outpatient trials. Because of the constraints of pharmacokinetics and dynamics of subcutaneous insulin delivery, the currently available AID systems are all 'hybrid' or 'semi-automated' insulin delivery systems with a need of meal and exercise announcements in order to anticipate rapid glucose variations through pre-meal bolus or pre-exercise reduction of infusion rate. Nevertheless, these AID systems significantly improve time spent in a near-normal range with a reduction of the risk of hypoglycemia and the mental load of managing diabetes in everyday life, representing a milestone in insulin therapy. Expected progression toward fully automated, further miniaturized and integrated, possibly implantable on long-term and more physiological closed-loop systems paves the way for a functional cure of type 1 diabetes.

Minimal and Maximal Models to Quantitate Glucose Metabolism: Tools to Measure, to Simulate and to Run in Silico Clinical Trials

Several models have been proposed to describe the glucose system at whole-body, organ/tissue and cellular level, designed to measure non-accessible parameters (minimal models), to simulate system behavior and run in silico clinical trials (maximal models). Here, we will review the authors' work, by putting it into a concise historical background. We will discuss first the parametric portrait provided by the oral minimal models-building on the classical intravenous glucose tolerance test minimal models-to measure otherwise non-accessible key parameters like insulin sensitivity and beta-cell responsivity from a physiological oral test, the mixed meal or the oral glucose tolerance tests, and what can be gained by adding a tracer to the oral glucose dose. These models were used in various pathophysiological studies, which we will briefly review. A deeper understanding of insulin sensitivity can be gained by measuring insulin action in the skeletal muscle. This requires the use of isotopic tracers: both the classical multiple-tracer dilution and the positron emission tomography techniques are discussed, which quantitate the effect of insulin on the individual steps of glucose metabolism, that is, bidirectional transport plasma-interstitium, and phosphorylation. Finally, we will present a cellular model of insulin secretion that, using a multiscale modeling approach, highlights the relations between minimal model indices and subcellular secretory events. In terms of maximal models, we will move from a parametric to a flux portrait of the system by discussing the triple tracer meal protocol implemented with the tracer-to-tracee clamp technique. This allows to arrive at quasi-model independent measurement of glucose rate of appearance (Ra), endogenous glucose production (EGP), and glucose rate of disappearance (Rd). Both the fast absorbing simple carbs and the slow absorbing complex carbs are discussed. This rich data base has allowed us to build the UVA/Padova Type 1 diabetes and the Padova Type 2 diabetes large scale simulators. In particular, the UVA/Padova Type 1 simulator proved to be a very useful tool to safely and effectively test in silico closed-loop control algorithms for an artificial pancreas (AP). This was the first and unique simulator of the glucose system accepted by the U.S. Food and Drug Administration as a substitute to animal trials for in silico testing AP algorithms. Recent uses of the simulator have looked at glucose sensors for non-adjunctive use and new insulin molecules.

Recent Advances in Intelligent Wearable Medical Devices Integrating Biosensing and Drug Delivery

The primary roles of precision medicine are to perform real-time examination, administer on-demand medication, and apply instruments continuously. However, most current therapeutic systems implement these processes separately, leading to treatment interruption and limited recovery in patients. Personalized healthcare and smart medical treatment have greatly promoted research on and development of biosensing and drug-delivery integrated systems, with intelligent wearable medical devices (IWMDs) as typical systems, which have received increasing attention because of their non-invasive and customizable nature. Here, the latest progress in research on IWMDs is reviewed, including their mechanisms of integrating biosensing and on-demand drug delivery. The current challenges and future development directions of IWMDs are also discussed.

Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures

BACKGROUND

Personal insulin pumps have shown to be effective in improving the quality of therapy for people with type 1 diabetes (T1D). However, the safety of this technology is limited by the possible infusion site failures, which are linked with hyperglycemia and ketoacidosis. Thanks to the large availability of collected data provided by modern therapeutic technologies, machine learning algorithms have the potential to provide new way to identify failures early and avert adverse events.

METHODS

A clinical dataset (N = 20) is used to evaluate a novel method for detecting real-time infusion site failures using unsupervised anomaly detection algorithms, previously proposed and developed on in-silico data. An adapted feature engineering procedure is introduced to make the method able to operate in the absence of a closed-loop (CL) system and meal announcements.

RESULTS

In the optimal configuration, we obtained a performance of 0.75 Sensitivity (15 out of 20 total failures detected) and 0.08 FP/day, outperforming previously proposed literature algorithms. The algorithm was able to anticipate the replacement of the malfunctioning infusion sets by ~2 h on average.

CONCLUSIONS

On the considered dataset, the proposed algorithm showed the potential to improve the safety of patients treated with sensor-augmented pump systems.

Automated insulin dosing systems: Advances after a century of insulin

The daily complexities of insulin therapy and glucose variability in type 1 diabetes still pose significant challenges, despite advancements in modern insulin analogues. Minimising hypoglycaemia and optimising time spent within target glucose range are recommended to reduce the risk of diabetes-related complications and distress. Access to structured education and adjuvant diabetes technologies, such as insulin pumps and glucose sensors, are recommended by National Institute for Health and Care Excellence (NICE) to enable people with type 1 diabetes achieve their glycaemic goals. One hundred years after the discovery of insulin, automated insulin dosing (AID, a.k.a. closed loop or artificial pancreas) systems are a reality with a number of systems available and being used in usual clinical practice. Evidence from randomised clinical trials and real-world prospective studies support efficacy, effectiveness and safety of AID systems. Qualitative evaluations reveal treatment satisfaction and positive effects on quality of life. Current insulin-only AID systems still require carbohydrate and activity announcement (hybrid closed loop) due to the inherent pharmacokinetic limitations of rapid-acting insulin analogies. Ultra-rapid acting insulin and adjunctive use of other therapies (e.g. glucagon, pramlitide) are being evaluated to achieve full closed loop. Open-source AID (OS-AID) systems have been developed by the diabetes community, driven by a desire for safety and to accelerate technological advancement. In addition to effectiveness and safety, real-world prospective studies suggest that OS-AID systems fulfil unmet needs of commercially approved systems. The development, ongoing challenges and expectations of AID are outlined in this review.

Time in Range for Closed-Loop Systems versus Standard of Care during Physical Exercise in People with Type 1 Diabetes: A Systematic Review and Meta-Analysis

The aim of this systematic review and meta-analysis was to compare time in range (TIR) (70–180 mg/dL (3.9–10.0 mmol/L)) between fully closed-loop systems (CLS) and standard of care (including hybrid systems) during physical exercise in people with type 1 diabetes (T1D). A systematic literature search was conducted in EMBASE, PubMed, Cochrane Central Register of Controlled Trials, and ISI Web of Science from January 1950 until January 2020. Randomized controlled trials including studies with different CLS were compared against standard of care in people with T1D. The meta-analysis was performed using the random effects model and restricted maximum likelihood estimation method. Six randomized controlled trials involving 153 participants with T1D of all age groups were included. Due to crossover test designs, studies were included repeatedly (a–d) if CLS or physical exercise interventions were different. Applying this methodology increased the comparisons to a total number of 266 participants. TIR was higher with an absolute mean difference (AMD) of 6.18%, 95% CI: 1.99 to 10.38% in favor of CLS. In a subgroup analysis, the AMD was 9.46%, 95% CI: 2.48% to 16.45% in children and adolescents while the AMD for adults was 1.07% 95% CI: −0.81% to 2.96% in favor of CLS. In this systematic review and meta-analysis CLS moderately improved TIR in comparison to standard of care during physical exercise in people with T1D. This effect was particularly pronounced for children and adolescents showing that the use of CLS improved TIR significantly compared to standard of care.

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

BACKGROUND

In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al.

METHODS

Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed.

RESULTS

Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05).

CONCLUSIONS

The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.

A dissolving and glucose-responsive insulin-releasing microneedle patch for type 1 diabetes therapy

A dissolving microneedle patch for responsive insulin release and type 1 diabetes therapy.

Measuring glucose at the site of insulin delivery with a redox-mediated sensor

Automated insulin delivery systems for people with type 1 diabetes rely on an accurate subcutaneous glucose sensor and an infusion cannula that delivers insulin in response to measured glucose. Integrating the sensor with the infusion cannula would provide substantial benefit by reducing the number of devices inserted into subcutaneous tissue. We describe the sensor chemistry and a calibration algorithm to minimize impact of insulin delivery artifacts in a new glucose sensing cannula. Seven people with type 1 diabetes undergoing automated insulin delivery used two sensing cannulae whereby one delivered a rapidly-acting insulin analog and the other delivered a control phosphate buffered saline (PBS) solution with no insulin. While there was a small artifact in both conditions that increased for larger volumes, there was no difference between the artifacts in the sensing cannula delivering insulin compared with the sensing cannula delivering PBS as determined by integrating the area-under-the-curve of the sensor values following delivery of larger amounts of fluid (P = 0.7). The time for the sensor to recover from the artifact was found to be longer for larger fluid amounts compared with smaller fluid amounts (10.3 ± 8.5 min vs. 41.2 ± 78.3 s, P < 0.05). Using a smart-sampling Kalman filtering smoothing algorithm improved sensor accuracy. When using an all-point calibration on all sensors, the smart-sampling Kalman filter reduced the mean absolute relative difference from 10.9% to 9.5% and resulted in 96.7% of the data points falling within the A and B regions of the Clarke error grid. Despite a small artifact, which is likely due to dilution by fluid delivery, it is possible to continuously measure glucose in a cannula that simultaneously delivers insulin.

Respective Contributions of Glycemic Variability and Mean Daily Glucose as Predictors of Hypoglycemia in Type 1 Diabetes: Are They Equivalent?

OBJECTIVE

To evaluate the respective contributions of short-term glycemic variability and mean daily glucose (MDG) concentration to the risk of hypoglycemia in type 1 diabetes.

RESEARCH DESIGN AND METHODS

People with type 1 diabetes (n = 100) investigated at the University Hospital of Montpellier (France) underwent continuous glucose monitoring (CGM) on two consecutive days, providing a total of 200 24-h glycemic profiles. The following parameters were computed: MDG concentration, within-day glycemic variability (coefficient of variation for glucose [%CV]), and risk of hypoglycemia (presented as the percentage of time spent below three glycemic thresholds: 3.9, 3.45, and 3.0 mmol/L).

RESULTS

MDG was significantly higher, and %CV significantly lower (both P < 0.001), when comparing the 24-h glycemic profiles according to whether no time or a certain duration of time was spent below the thresholds. Univariate regression analyses showed that MDG and %CV were the two explanatory variables that entered the model with the outcome variable (time spent below the thresholds). The classification and regression tree procedure indicated that the predominant predictor for hypoglycemia was %CV when the threshold was 3.0 mmol/L. In people with mean glucose ≤7.8 mmol/L, the time spent below 3.0 mmol/L was shortest (P < 0.001) when %CV was below 34%.

CONCLUSIONS

In type 1 diabetes, short-term glycemic variability relative to mean glucose (i.e., %CV) explains more hypoglycemia than does mean glucose alone when the glucose threshold is 3.0 mmol/L. Minimizing the risk of hypoglycemia requires a %CV below 34%.

Randomized Controlled Trial of Mobile Closed-Loop Control

OBJECTIVE

Assess the efficacy of inControl AP, a mobile closed-loop control (CLC) system.

RESEARCH DESIGN AND METHODS

This protocol, NCT02985866, is a 3-month parallel-group, multicenter, randomized unblinded trial designed to compare mobile CLC with sensor-augmented pump (SAP) therapy. Eligibility criteria were type 1 diabetes for at least 1 year, use of insulin pumps for at least 6 months, age ≥14 years, and baseline HbA1c <10.5% (91 mmol/mol). The study was designed to assess two coprimary outcomes: superiority of CLC over SAP in continuous glucose monitor (CGM)-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L.

RESULTS

Between November 2017 and May 2018, 127 participants were randomly assigned 1:1 to CLC (n = 65) versus SAP (n = 62); 125 participants completed the study. CGM time below 3.9 mmol/L was 5.0% at baseline and 2.4% during follow-up in the CLC group vs. 4.7% and 4.0%, respectively, in the SAP group (mean difference -1.7% [95% CI -2.4, -1.0]; P < 0.0001 for superiority). CGM time above 10 mmol/L was 40% at baseline and 34% during follow-up in the CLC group vs. 43% and 39%, respectively, in the SAP group (mean difference -3.0% [95% CI -6.1, 0.1]; P < 0.0001 for noninferiority). One severe hypoglycemic event occurred in the CLC group, which was unrelated to the study device.

CONCLUSIONS

In meeting its coprimary end points, superiority of CLC over SAP in CGM-measured time below 3.9 mmol/L and noninferiority in CGM-measured time above 10 mmol/L, the study has demonstrated that mobile CLC is feasible and could offer certain usability advantages over embedded systems, provided the connectivity between system components is stable.

Evening and overnight closed-loop control versus 24/7 continuous closed-loop control for type 1 diabetes: a randomised crossover trial

Background

Automated closed-loop control (CLC), known as the "artificial pancreas" is emerging as a treatment option for Type 1 Diabetes (T1D), generally superior to sensor-augmented insulin pump (SAP) treatment. It is postulated that evening-night (E-N) CLC may account for most of the benefits of 24-7 CLC; however, a direct comparison has not been done.

Methods

In this trial (NCT02679287), adults with T1D were randomised 1:1 to two groups, which followed different sequences of four 8-week sessions, resulting in two crossover designs comparing SAP vs E-N CLC and E-N CLC vs 24-7 CLC, respectively. Eligibility: T1D for at least 1 year, using an insulin pump for at least six months, ages 18 years or older. Primary hypothesis: E-N CLC compared to SAP will decrease percent time <70mg/dL (3.9mmol/L) measured by continuous glucose monitoring (CGM) without deterioration in HbA1c. Secondary Hypotheses: 24-7 CLC compared to SAP will increase CGM-measured time in target range (TIR, 70-180mg/dL; 3.9-10mmol/L) and will reduce glucose variability during the day.

Findings

Ninety-three participants were randomised and 80 were included in the analysis, ages 18-69 years; HbA1c levels 5.4-10.6%; 66% female. Compared to SAP, E-N CLC reduced overall time <70mg/dL from 4.0% to 2.2% () resulting in an absolute difference of 1.8% (95%CI: 1.2-2.4%), p<0.0001. This was accompanied by overall reduction in HbA1c from 7.4% at baseline to 7.1% at the end of study, resulting in an absolute difference of 0.3% (95% CI: 0.1-0.4%), p<0.0001. There were 5 severe hypoglycaemia adverse events attributed to user-directed boluses without malfunction of the investigational device, and no diabetic ketoacidosis events.

Interpretation

In type 1 diabetes, evening-night closed-loop control was superior to sensor-augmented pump therapy, achieving most of the glycaemic benefits of 24-7 closed-loop.

In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

Objective: Safety data on Do-It-Yourself Artificial Pancreas Systems are missing. The most widespread in Europe is the AndroidAPS implementation of the OpenAPS algorithm. We used the UVA/Padova Type 1 Diabetes Simulator to in silico test safety and efficacy of this algorithm in different scenarios. Methods: We tested five configurations of the AndroidAPS algorithm differing in aggressiveness and patient's interaction with the system. All configurations were tested with insulin sensitivity variation of ±30%. The most promising configurations were tested in real-life scenarios: over- and underestimated bolus by 50%, bolus delivered 15 min before meal, and late bolus delivered 15 min after meal. Continuous Glucose Monitoring (CGM) time in ranges (TIRs) metrics were used to assess the glycemic control. Results: In silico testing showed that open-source closed-loop system AndroidAPS works effectively and safely. The best results were reached if AndroidAPS algorithm worked with microboluses and when half of calculated bolus was issued (mean glycemia 131 mg/dL, SD 27 mg/dL, TIR 91%, time between 54 and 70 mg/dL <1%, and low blood glucose index even <1). The meal bolus over- and underestimation as well as late bolus did not affect the TIR and, importantly, the time between 54 and 70 mg/dL. Conclusion: In silico testing proved that AndroidAPS implementation of the OpenAPS algorithm is safe and effective, and it showed a great potential to be tested in prospective home setting study.

HbA1c variability in adults with type 1 diabetes on continuous subcutaneous insulin infusion (CSII) therapy compared to multiple daily injection (MDI) treatment

OBJECTIVE

To determine if continuous subcutaneous insulin infusion (CSII) therapy is associated with lower glycated haemoglobin (HbA1c) variability (long-term glycaemic variability; GV) relative to multiple daily injection (MDI) treatment in adults with type 1 diabetes mellitus (T1DM).

DESIGN

Retrospective audit.

SETTING AND PARTICIPANTS

Clinic records from 506 adults with T1DM from two tertiary Australian hospitals.

OUTCOME MEASURES

Long-term GV was assessed by HbA1c SD and coefficient of variation (CV) in adults on established MDI or CSII therapy, and in a subset changing from MDI to CSII.

RESULTS

Adults (n=506, (164 CSII), 50% women, mean±SD age 38.0±15.3 years, 17.0±13.7 years diabetes, mean HbA1c 7.8%±1.2% (62±13 mmol/mol) on CSII, 8.0%±1.5% (64±16 mmol/mol) on MDI) were followed for 4.1±3.6 years. CSII use was associated with lower GV (HbA1c SD: CSII vs MDI 0.5%±0.41% (6±6 mmol/mol) vs 0.7%±0.7% (9±8 mmol/mol)) and CV: CSII vs MDI 6.7%±4.6% (10±10 mmol/mol) vs 9.3%±7.3% (14±13 mmol/mol), both p<0.001. Fifty-six adults (73% female, age 36±13 years, 16±13 years diabetes, HbA1c 7.8%±0.8% (62±9 mmol/mol)) transitioned from MDI to CSII. Mean HbA1c fell by 0.4%. GV from 1 year post-CSII commencement decreased significantly, HbA1c SD pre-CSII versus post-CSII 0.7%±0.5% (8±5 mmol/mol) vs 0.4%±0.4% (5±4 mmol/mol); p<0.001, and HbA1c CV 9.2%±5.5% (13±8 mmol/mol) vs 6.1%±3.9% (9±5 mmol/mol); p<0.001.

CONCLUSIONS

In clinical practice with T1DM adults relative to MDI, CSII therapy is associated with lower HbA1c GV.

Hypoglycemia Prevention via Personalized Glucose-Insulin Models Identified in Free-Living Conditions

BACKGROUND

The objective of this research is to show the effectiveness of individualized hypoglycemia predictive alerts (IHPAs) based on patient-tailored glucose-insulin models (PTMs) for different subjects. Interpatient variability calls for PTMs that have been identified from data collected in free-living conditions during a one-month trial.

METHODS

A new impulse-response (IR) identification technique has been applied to free-living data in order to identify PTMs that are able to predict the future glucose trends and prevent hypoglycemia events. Impulse response has been applied to seven patients with type 1 diabetes (T1D) of the University of Amsterdam Medical Centre. Individualized hypoglycemia predictive alert has been designed for each patient thanks to the good prediction capabilities of PTMs.

RESULTS

The PTMs performance is evaluated in terms of index of fitting (FIT), coefficient of determination, and Pearson's correlation coefficient with a population FIT of 63.74%. The IHPAs are evaluated on seven patients with T1D with the aim of predicting in advance (between 45 and 10 minutes) the unavoidable hypoglycemia events; these systems show better performance in terms of sensitivity, precision, and accuracy with respect to previously published results.

CONCLUSION

The proposed work shows the successful results obtained applying the IR to an entire set of patients, participants of a one-month trial. Individualized hypoglycemia predictive alerts are evaluated in terms of hypoglycemia prevention: the use of a PTM allows to detect 84.67% of the hypoglycemia events occurred during a one-month trial on average with less than 0.4% of false alarms. The promising prediction capabilities of PTMs can be a key ingredient for new generations of individualized model predictive control for artificial pancreas.

Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes

BACKGROUND

Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise.

METHODS

Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm.

RESULTS

The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model (P < .001) and the standard MSO (P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity.

CONCLUSIONS

Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control

Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.

Diabetes Technology: Monitoring, Analytics, and Optimal Control

Over the past 50 years, the diabetes technology field progressed remarkably through self-monitoring of blood glucose (SMBG), continuous subcutaneous insulin infusion (CSII), risk and variability analysis, mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). This review follows these developments, beginning with an overview of the functioning of the human metabolic system in health and in diabetes and of its detailed quantitative network modeling. The review continues with a brief account of the first AP studies that used intravenous glucose monitoring and insulin infusion, and with notes about CSII and CGM-the technologies that made possible the development of contemporary AP systems. In conclusion, engineering lessons learned from AP research, and the clinical need for AP systems to prove their safety and efficacy in large-scale clinical trials, are outlined.

The International Diabetes Closed-Loop Study: Testing Artificial Pancreas Component Interoperability

BACKGROUND

Use of artificial pancreas (AP) requires seamless interaction of device components, such as continuous glucose monitor (CGM), insulin pump, and control algorithm. Mobile AP configurations also include a smartphone as computational hub and gateway to cloud applications (e.g., remote monitoring and data review and analysis). This International Diabetes Closed-Loop study was designed to demonstrate and evaluate the operation of the inControl AP using different CGMs and pump modalities without changes to the user interface, user experience, and underlying controller.

METHODS

Forty-three patients with type 1 diabetes (T1D) were enrolled at 10 clinical centers (7 United States, 3 Europe) and 41 were included in the analyses (39% female, >95% non-Hispanic white, median T1D duration 16 years, median HbA1c 7.4%). Two CGMs and two insulin pumps were tested by different study participants/sites using the same system hub (a smartphone) during 2 weeks of in-home use.

RESULTS

The major difference between the system components was the stability of their wireless connections with the smartphone. The two sensors achieved similar rates of connectivity as measured by percentage time in closed loop (75% and 75%); however, the two pumps had markedly different closed-loop adherence (66% vs. 87%). When connected, all system configurations achieved similar glycemic outcomes on AP control (73% [mean] time in range: 70-180 mg/dL, and 1.7% [median] time <70 mg/dL).

CONCLUSIONS

CGMs and insulin pumps can be interchangeable in the same Mobile AP system, as long as these devices achieve certain levels of reliability and wireless connection stability.

The road from intermittently scanned glucose monitoring to hybrid closed-loop systems: Part A. Keys to success: subject profiles, choice of systems, education

Managing type 1 diabetes (T1DM) is challenging and requires intensive glucose monitoring and titration of insulin in order to reduce the risk of complications. The use of continuous glucose monitoring (CGM) systems, either flash or intermittently scanned glucose monitoring (isCGM) or real-time (RT) CGM, has positively affected the management of type 1 diabetes with the potential to lower HbA1c, enhance time spent in range, reduce frequency and time spent in hypoglycemia and hyperglycemia, lower glycemic variability, and improve quality of life. In recent years, both CGM and pump technology have advanced, with improved functional features and integration, including low glucose suspend (LGS), predictive low glucose suspend (PLGS), and hybrid closed-loop (HCL) systems. In this review, we highlight the benefits and limitations of use of isCGM/RT-CGM for open-loop control and recent progress in closed-loop control systems. We also discuss different subject profiles for the different systems, and focus on educational aspects that are key to successful use of the systems.

First Look at Control-IQ: A New-Generation Automated Insulin Delivery System

OBJECTIVE

To pilot test a new closed-loop control technology to validate it for a further large clinical trial.

RESEARCH DESIGN AND METHODS

The t:slim X2 insulin pump with Control-IQ Technology (Tandem Diabetes Care) includes a Dexcom G6 sensor and a closed-loop algorithm implemented in the pump that 1) automates insulin correction boluses, 2) has a dedicated hypoglycemia safety system, and 3) gradually intensifies control overnight, aiming for blood glucose levels of approximately 100-120 mg/dL every morning.

RESULTS

Five patients with type 1 diabetes (mean age 52.8 years, 2/3 male/female, mean A1C 6.5%) used Control-IQ in an outpatient setting (hotel) for approximately 37 h. During the closed loop, mean glucose was 129 mg/dL (135/121 mg/dL during the day/night), time within target range 70-180 mg/dL was 87% (82%/94% during the day/night), and time <60 mg/dL was 1.1% (2.0%/0.0% during the day/night).

CONCLUSIONS

Following this pilot trial, Control-IQ was deployed in several studies, including the large-scale National Institutes of Health International Diabetes Closed-Loop (iDCL) Trial.

Automated closed-loop control of diabetes: the artificial pancreas

The incidence of Diabetes Mellitus is on the rise worldwide, which exerts enormous health toll on the population and enormous pressure on the healthcare systems. Now, almost hundred years after the discovery of insulin in 1921, the optimization problem of diabetes is well formulated as maintenance of strict glycemic control without increasing the risk for hypoglycemia. External insulin administration is mandatory for people with type 1 diabetes; various medications, as well as basal and prandial insulin, are included in the daily treatment of type 2 diabetes. This review follows the development of the Diabetes Technology field which, since the 1970s, progressed remarkably through continuous subcutaneous insulin infusion (CSII), mathematical models and computer simulation of the human metabolic system, real-time continuous glucose monitoring (CGM), and control algorithms driving closed-loop control systems known as the "artificial pancreas" (AP). All of these developments included significant engineering advances and substantial bioelectronics progress in the sensing of blood glucose levels, insulin delivery, and control design. The key technologies that enabled contemporary AP systems are CSII and CGM, both of which became available and sufficiently portable in the beginning of this century. This powered the quest for wearable home-use AP, which is now under way with prototypes tested in outpatient studies during the past 6 years. Pivotal trials of new AP technologies are ongoing, and the first hybrid closed-loop system has been approved by the FDA for clinical use. Thus, the closed-loop AP is well on its way to become the digital-age treatment of diabetes.

β Cell Replacement Therapy: The Next 10 Years

β cell replacement with either pancreas or islet transplantation has progressed immensely over the last decades with current 1- and 5-year insulin independence rates of approximately 85% and 50%, respectively. Recent advances are largely attributed to improvements in immunosuppressive regimen, donor selection, and surgical technique. However, both strategies are compromised by a scarce donor source. Xenotransplantation offers a potential solution by providing a theoretically unlimited supply of islets, but clinical application has been limited by concerns for a potent immune response against xenogeneic tissue. β cell clusters derived from embryonic or induced pluripotent stem cells represent another promising unlimited source of insulin producing cells, but clinical application is pending further advances in the function of the β cell like clusters. Exciting developments and rapid progress in all areas of β cell replacement prompted a lively debate by members of the young investigator committee of the International Pancreas and Islet Transplant Association at the 15th International Pancreas and Islet Transplant Association Congress in Melbourne and at the 26th international congress of The Transplant Society in Hong Kong. This international group of young investigators debated which modality of β cell replacement would predominate the landscape in 10 years, and their arguments are summarized here.

Adaptive fuzzy integral sliding mode control of blood glucose level in patients with type 1 diabetes: In silico studies

Currently, artificial pancreas is an alternative treatment instead of insulin therapy for patients with type 1 diabetes mellitus. Closed-loop control of blood glucose level (BGL) is one of the difficult tasks in biomedical engineering field due to nonlinear time-varying dynamics of insulin-glucose relation that is combined with time delays and model uncertainties. In this paper, we propose a novel adaptive fuzzy integral sliding mode control scheme for BGL regulation. System dynamics is identified online using fuzzy logic systems. The presented method is evaluated in silico studies by nine different virtual patients in three different groups for two continuous days. Simulation results demonstrate effective performance of the proposed control scheme of BGL regulation in presence of simultaneous meal and physical exercise disturbances. Comparison of the proposed control method with proportional-integral-derivative (PID) control and model predictive control (MPC) shows a superiority of the adaptive fuzzy integral sliding mode control with regard to two conventional methods of BGL regulation (PID and MPC) and sliding mode control.

Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas

There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.

Efficacy and safety of the artificial pancreas in the paediatric population with type 1 diabetes

BACKGROUND

Type 1 diabetes (DM1) is one of the most common chronic diseases in childhood and requires life-long insulin therapy and continuous health care support. An artificial pancreas (AP) or closed-loop system (CLS) have been developed with the aim of improving metabolic control without increasing the risk of hypoglycaemia in patients with DM1. As the impact of APs have been studied mainly in adults, the aim of this review is to evaluate the efficacy and safety of the AP in the paediatric population with DM1.

MAIN BODY

The real advantage of a CLS compared to last-generation sensor-augmented pumps is the gradual modulation of basal insulin infusion in response to glycaemic variations (towards both hyperglycaemia and hypoglycaemia), which has the aim of improving the proportion of time spent in the target glucose range and reducing the mean glucose level without increasing the risk of hypoglycaemia. Some recent studies demonstrated that also in children and adolescents an AP is able to reduce the frequency of hypoglycaemic events, an important limiting factor in reaching good metabolic control, particularly overnight. However, the advantages of the AP in reducing hyperglycaemia, increasing the time spent in the target glycaemic range and thus reducing glycated haemoglobin are less clear and require more clinical trials in the paediatric population, in particular in younger children.

CONCLUSIONS

Although the first results from bi-hormonal CLS are promising, long-term, head-to-head studies will have to prove their superiority over insulin-only approaches. More technological progress, the availability of more fast-acting insulin, further developments of algorithms that could improve glycaemic control after meals and physical activity are the most important challenges in reaching an optimal metabolic control with the use of the AP in children and adolescents.

Artificial Pancreas as an Effective and Safe Alternative in Patients with Type 1 Diabetes Mellitus: A Systematic Review and Meta-Analysis

INTRODUCTION

Insulin injection is the main treatment in patients with type 1 diabetes mellitus (T1DM). Even though continuous glucose monitoring has significantly improved the conditions of these patients, limitations still exist. To further enhance glucose control in patients with T1DM, an artificial pancreas has been developed. We aimed to systematically compare artificial pancreas with its control group during a 24-h basis in patients with T1DM.

METHODS

Electronic databases were carefully searched for English publications comparing artificial pancreas with its control group. Overall daytime and nighttime glucose parameters were considered as the endpoints. Data were evaluated by means of weighted mean differences (WMDs) and 95% confidence intervals (CIs) generated by RevMan 5.3 software.

RESULTS

A total number of 354 patients were included. Artificial pancreas significantly maintained a better mean concentration of glucose (WMD - 1.03, 95% CI - 1.32 to - 0.75; P = 0.00001). Time spent in the hypoglycemic phase was also significantly lower (WMD - 1.23, 95% CI - 1.56 to - 0.91; P = 0.00001). Daily insulin requirement also significantly favored artificial pancreas (WMD - 3.43, 95% CI - 4.27 to - 2.59; P = 0.00001). Time spent outside the euglycemic phase and hyperglycemia phase (glucose > 10.0 mmol/L) also significantly favored artificial pancreas. Also, the numbers of hypoglycemic events were not significantly different.

CONCLUSION

Artificial pancreas might be considered an effective and safe alternative to be used during a 24-h basis in patients with T1DM.

Predictive hyperglycemia and hypoglycemia minimization: In-home double-blind randomized controlled evaluation in children and young adolescents

OBJECTIVE

The primary objective of this trial was to evaluate the feasibility, safety, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system vs predictive low glucose suspension (PLGS) alone in optimizing overnight glucose control in children 6 to 14 years old.

RESEARCH DESIGN AND METHODS

Twenty-eight participants 6 to 14 years old with T1D duration ≥1 year with daily insulin therapy ≥12 months and on insulin pump therapy for ≥6 months were randomized per night into PHHM mode or PLGS-only mode for 42 nights. The primary outcome was percentage of time in sensor-measured range 70 to 180 mg/dL in the overnight period.

RESULTS

The addition of automated insulin delivery with PHHM increased time in target range (70-180 mg/dL) from 66 ± 11% during PLGS nights to 76 ± 9% during PHHM nights (P<.001), without increasing hypoglycemia as measured by time below various thresholds. Average morning blood glucose improved from 176 ± 28 mg/dL following PLGS nights to 154 ± 19 mg/dL following PHHM nights (P<.001).

CONCLUSIONS

The PHHM system was effective in optimizing overnight glycemic control, significantly increasing time in range, lowering mean glucose, and decreasing glycemic variability compared to PLGS alone in children 6 to 14 years old.

Dual-hormone artificial pancreas: benefits and limitations compared with single-hormone systems

Technological advances have made the artificial pancreas a reality. This has the potential to improve the lives of individuals with Type 1 diabetes by reducing the risk of hypoglycaemia, achieving better overall glucose control, and enhancing quality of life. Both single-hormone (insulin-only) and dual-hormone (insulin and glucagon) systems have been developed; however, a focused review of the relative benefits of each artificial pancreas system is needed. We reviewed studies that directly compared single- and dual-hormone systems to evaluate the efficacy of each system for preventing hypoglycaemia and maintaining glycaemic control, as well as their utility in specific situations including during exercise, overnight and during the prandial period. We observed additional benefits with the dual-hormone artificial pancreas for reducing the risk of hypoglycaemic events overall and during exercise over the study duration. The single-hormone artificial pancreas was sufficient for maintenance of euglycaemia in the overnight period and for preventing late-onset post-exercise hypoglycaemia. Future comparative studies of longer duration are required to determine whether one system is superior for improving mean glucose control, eliminating severe hypoglycaemia, or improving quality of life.

A randomized controlled trial-based algorithm for insulin-pump therapy in hyperglycemic patients early after kidney transplantation

Treating hyperglycemia in previously non-diabetic individuals with exogenous insulin immediately after kidney transplantation reduced the odds of developing Posttransplantation Diabetes Mellitus (PTDM) in our previous proof-of-concept clinical trial. We hypothesized that insulin-pump therapy with maximal insulin dosage during the afternoon would improve glycemic control compared to basal insulin and standard-of-care. In a multi-center, randomized, controlled trial testing insulin isophane for PTDM prevention, we added a third study arm applying continuous subcutaneous insulin lispro infusion (CSII) treatment. CSII was initiated in 24 patients aged 55±12 years, without diabetes history, receiving tacrolimus. The mean daily insulin lispro dose was 9.2±5.2 IU. 2.3±1.1% of the total insulin dose were administered between 00:00 and 6:00, 19.5±11.6% between 6:00 and 12:00, 62.3±15.6% between 12:00 and 18:00 and 15.9±9.1% between 18:00 and 24:00. Additional bolus injections were necessary in five patients. Mild hypoglycemia (52–60 mg/dL) occurred in two patients. During the first post-operative week glucose control in CSII patients was overall superior compared to standard-of-care as well as once-daily insulin isophane for fasting and post-supper glucose. We present an algorithm for CSII treatment in kidney transplant recipients, demonstrating similar safety and superior short-term efficacy compared to standard-of-care and once-daily insulin isophane.

The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

BACKGROUND

A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described.

METHOD

Intraday variability of insulin sensitivity (S_I) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily S_I. Thus, time-varying distributions of both subject's basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of "dawn" phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added.

RESULTS

One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data.

CONCLUSIONS

The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Use of Mobile Devices to Measure Outcomes in Clinical Research, 2010-2016: A Systematic Literature Review

Background

The use of mobile devices in clinical research has advanced substantially in recent years due to the rapid pace of technology development. With an overall aim of informing the future use of mobile devices in interventional clinical research to measure primary outcomes, we conducted a systematic review of the use of and clinical outcomes measured by mobile devices (mobile outcomes) in observational and interventional clinical research.

Method

We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles on clinical research published between January 2010 and May 2016 in which mobile devices were used to measure study outcomes. We screened each publication for specific inclusion and exclusion criteria. We then identified and qualitatively summarized the use of mobile outcome assessments in clinical research, including the type and design of the study, therapeutic focus, type of mobile device(s) used, and specific mobile outcomes reported.

Results

The search retrieved 2,530 potential articles of interest. After screening, 88 publications remained. Twenty-five percent of the publications (n = 22) described mobile outcomes used in interventional research, and the rest (n = 66) described observational clinical research. Thirteen therapeutic areas were represented. Five categories of mobile devices were identified: (1) inertial sensors, (2) biosensors, (3) pressure sensors and walkways, (4) medication adherence monitors, and (5) location monitors; inertial sensors/accelerometers were most common (reported in 86% of the publications). Among the variety of mobile outcomes, various assessments of physical activity were most common (reported in 74% of the publications). Other mobile outcomes included assessments of sleep, mobility, and pill adherence, as well as biomarkers assessed using a mobile device, including cardiac measures, glucose, gastric reflux, respiratory measures, and intensity of head-related injury.

Conclusion

Mobile devices are being widely used in clinical research to assess outcomes, although their use in interventional research to assess therapeutic effectiveness is limited. For mobile devices to be used more frequently in pivotal interventional research – such as trials informing regulatory decision-making – more focus should be placed on: (1) consolidating the evidence supporting the clinical meaningfulness of specific mobile outcomes, and (2) standardizing the use of mobile devices in clinical research to measure specific mobile outcomes (e.g., data capture frequencies, placement of device). To that aim, this manuscript offers a broad overview of the various mobile outcome assessments currently used in observational and interventional research, and categorizes and consolidates this information for researchers interested in using mobile devices to assess outcomes in interventional research.

In silico assessment of biomedical products: The conundrum of rare but not so rare events in two case studies

In silico clinical trials, defined as "The use of individualized computer simulation in the development or regulatory evaluation of a medicinal product, medical device, or medical intervention," have been proposed as a possible strategy to reduce the regulatory costs of innovation and the time to market for biomedical products. We review some of the the literature on this topic, focusing in particular on those applications where the current practice is recognized as inadequate, as for example, the detection of unexpected severe adverse events too rare to be detected in a clinical trial, but still likely enough to be of concern. We then describe with more details two case studies, two successful applications of in silico clinical trial approaches, one relative to the University of Virginia/Padova simulator that the Food and Drug Administration has accepted as possible replacement for animal testing in the preclinical assessment of artificial pancreas technologies, and the second, an investigation of the probability of cardiac lead fracture, where a Bayesian network was used to combine in vivo and in silico observations, suggesting a whole new strategy of in silico-augmented clinical trials, to be used to increase the numerosity where recruitment is impossible, or to explore patients' phenotypes that are unlikely to appear in the trial cohort, but are still frequent enough to be of concern.

Manual Closed-Loop Insulin Delivery Using a Saddle Point Model Predictive Control Algorithm: Results of a Crossover Randomized Overnight Study

BACKGROUND

The purpose was to assess the efficacy of a new closed-loop algorithm (Saddle Point Model Predictive Control, SP-MPC) in achieving nocturnal normoglycemia while reducing the risk of hypoglycemia in patients with type 1 diabetes.

METHOD

In this randomized crossover study, 10 adult patients (mean hemoglobin A1c 7.35 ± 1.04%) were assigned to be treated overnight by open loop using sensor-augmented pump therapy (open-loop SAP) or manual closed-loop delivery. During closed loop, insulin doses were calculated using the SP-MPC algorithm and administered as manual boluses every 15 minutes from 9:00 pm to 8:00 am. Patients consumed a self-selected meal (65-125 g of carbohydrates) at 7:00 pm accompanied by their usual prandial bolus. Blood glucose was measured every 30 minutes. The primary endpoints were the time spent in target (70-145 mg/dl) and time spent below 70 mg/dl from 11:00 pm to 8:00 am.

RESULTS

Time spent in target did not differ between closed-loop and open-loop SAP. The number of hypoglycemic events (<70 mg/dl) was reduced 2.8-fold in closed loop (n = 5, median = 0/patient/hour; interquartile range: 0-0.11) as compared to open-loop SAP (n = 14, median = 0.22/patient/hour, 0.02-0.22) ( P = .02). The area under the curve for sensor glucose values >145 mg/dl was significantly lower during closed-loop than during open-loop SAP ( P = .03) as well as HBGI ( P = .02).

CONCLUSIONS

This pilot study suggests that the use of the SP-MPC algorithm may improve mean overnight glucose control and reduce the number of hypoglycemic events as compared to SAP therapy.