Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control.

We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia.

At-Home Use of a Pregnancy-Specific Zone-MPC Closed-Loop System for Pregnancies Complicated by Type 1 Diabetes: A Single-Arm, Observational Multicenter Study.

Objective: There are no commercially available hybrid closed-loop insulin delivery systems customized to achieve pregnancy-specific glucose targets in the U.S. This study aimed to evaluate the feasibility and performance of at-home use of a zone model predictive controller-based closed-loop insulin delivery system customized for pregnancies complicated by type 1 diabetes (CLC-P).

Feasibility and Preliminary Safety of Smartphone-Based Automated Insulin Delivery in Adolescents and Children With Type 1 Diabetes.

Background: A smartphone-based automated insulin delivery (AID) controller device can facilitate use of interoperable components and acceptance in adolescents and children.

Methods: Pediatric participants (N = 20, 8F) with type 1 diabetes were enrolled in three sequential age-based cohorts: adolescents (12-<18 years, n = 8, 5F), school-age (8-<12 years, n = 7, 2F), and young children (2-<8 years, n = 5, 1F). Participants used the interoperable artificial pancreas system (iAPS) and zone model predictive control (MPC) on an unlocked smartphone for 48 hours, consumed unrestricted meals of their choice, and engaged in various unannounced exercises.

Outpatient Randomized Crossover Comparison of Zone Model Predictive Control Automated Insulin Delivery with Weekly Data Driven Adaptation Versus Sensor-Augmented Pump: Results from the International Diabetes Closed-Loop Trial 4.

Automated insulin delivery (AID) systems have proven effective in increasing time-in-range during both clinical trials and real-world use. Further improvements in outcomes for single-hormone (insulin only) AID may be limited by suboptimal insulin delivery settings. Adults (≥18 years of age) with type 1 diabetes were randomized to either sensor-augmented pump (SAP) (inclusive of predictive low-glucose suspend) or adaptive zone model predictive control AID for 13 weeks, then crossed over to the other arm.

Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes.

Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation.

Optimizing Behavioral Interventions to Regulate Gestational Weight Gain With Sequential Decision Policies Using Hybrid Model Predictive Control.

Excessive gestational weight gain is a significant public health concern that has been the recent focus of control systems-based interventions. (HMZ) is an intervention study that aims to develop and validate an individually-tailored and "intensively adaptive" intervention to manage weight gain for pregnant women with overweight or obesity using control engineering approaches. This paper presents how Hybrid Model Predictive Control (HMPC) can be used to assign intervention dosages and consequently generate a prescribed intervention with dosages unique to each individuals needs.

Feasibility of Closed-Loop Insulin Delivery with a Pregnancy-Specific Zone Model Predictive Control Algorithm.

Evaluating the feasibility of closed-loop insulin delivery with a zone model predictive control (zone-MPC) algorithm designed for pregnancy complicated by type 1 diabetes (T1D). Pregnant women with T1D from 14 to 32 weeks gestation already using continuous glucose monitor (CGM) augmented pump therapy were enrolled in a 2-day multicenter supervised outpatient study evaluating pregnancy-specific zone-MPC based closed-loop control (CLC) with the interoperable artificial pancreas system (iAPS) running on an unlocked smartphone. Meals and activities were unrestricted.

Outpatient Randomized Crossover Automated Insulin Delivery Versus Conventional Therapy with Induced Stress Challenges.

Automated insulin delivery (AID) systems have not been evaluated in the context of psychological and pharmacological stress in type 1 diabetes. Our objective was to determine glycemic control and insulin use with Zone Model Predictive Control (zone-MPC) AID system enhanced for states of persistent hyperglycemia versus sensor-augmented pump (SAP) during outpatient use, including in-clinic induced stress. Randomized, crossover, 2-week trial of zone-MPC AID versus SAP in 14 adults with type 1 diabetes.

Review of Automated Insulin Delivery Systems for Individuals with Type 1 Diabetes: Tailored Solutions for Subpopulations.

Automated insulin delivery (AID) systems have proven safe and effective in improving glycemic outcomes in individuals with type 1 diabetes (T1D). Clinical evaluation of this technology has progressed to large randomized, controlled outpatient studies and recent commercial approval of AID systems for children and adults. However, several challenges remain in improving these systems for different subpopulations (e.

Using Iterative Learning for Insulin Dosage Optimization in Multiple-Daily-Injections Therapy for People With Type 1 Diabetes.

Objective: In this work, we design iterative algorithms for the delivery of long-acting (basal) and rapid-acting (bolus) insulin, respectively, for people with type 1 diabetes (T1D) on multiple-daily-injections (MDIs) therapy using feedback from self-monitoring of blood glucose (SMBG) measurements.

Methods: Iterative learning control (ILC) updates basal therapy consisting of one long-acting insulin injection per day, while run-to-run (R2R) adapts meal bolus therapy via the update of the mealtime-specific insulin-to-carbohydrate ratio (CR). Updates are due weekly and are based upon sparse SMBG measurements.

Randomized Crossover Comparison of Automated Insulin Delivery Versus Conventional Therapy Using an Unlocked Smartphone with Scheduled Pasta and Rice Meal Challenges in the Outpatient Setting.

Automated Insulin Delivery (AID) hybrid closed-loop systems have not been well studied in the context of prescribed meals. We evaluated performance of our interoperable artificial pancreas system (iAPS) in the at-home setting, running on an unlocked smartphone, with scheduled meal challenges in a randomized crossover trial. Ten adults with type 1 diabetes completed 2 weeks of AID-based control and 2 weeks of conventional therapy in random order where they consumed regular pasta or extra-long grain white rice as part of a complete dinner meal on six different occasions in both arms (each meal thrice in random order).

The Effect of Two Types of Pasta Versus White Rice on Postprandial Blood Glucose Levels in Adults with Type 1 Diabetes: A Randomized Crossover Trial.

Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). We performed a randomized crossover study in 12 adults with T1D (age 58.

Design and Clinical Evaluation of the Interoperable Artificial Pancreas System (iAPS) Smartphone App: Interoperable Components with Modular Design for Progressive Artificial Pancreas Research and Development.

Background: There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone.

Methods: After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study.

Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.

Background: As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected.

Design and Clinical Evaluation of a Novel Low-Glucose Prediction Algorithm with Mini-Dose Stable Glucagon Delivery in Post-Bariatric Hypoglycemia.

Background: Postbariatric hypoglycemia (PBH) is a complication of bariatric surgery with limited therapeutic options. We developed an event-based system to predict and detect hypoglycemia based on continuous glucose monitor (CGM) data and recommend delivery of minidose liquid glucagon.

Methods: We performed an iterative development clinical study employing a novel glucagon delivery system: a Dexcom CGM connected to a Windows tablet running a hypoglycemia prediction algorithm and an Omnipod pump filled with an investigational stable liquid glucagon formulation.

Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial.

Objective: As artificial pancreas (AP) becomes standard of care, consideration of extended use of insulin infusion sets (IIS) and continuous glucose monitors (CGMs) becomes vital. We conducted an outpatient randomized crossover study to test the safety and efficacy of a zone model predictive control (zone-MPC)-based AP system versus sensor augmented pump (SAP) therapy in which IIS and CGM failures were provoked via extended wear to 7 and 21 days, respectively.

Research Design And Methods: A smartphone-based AP system was used by 19 adults (median age 23 years [IQR 10], mean 8.

A genome-wide approach to screen for genetic variants in broilers (Gallus gallus) with divergent feed conversion ratio.

Feed conversion ratio (FCR) is an economically important trait in broilers and feed accounts for a significant proportion of the costs involved in broiler production. To explore the contribution of functional variants to FCR trait, we analyzed coding and non-coding single-nucleotide variants (SNVs) across the genome by exome sequencing in seven pairs of full-sibs broilers with divergent FCR and with a sequence coverage at an average depth of fourfold. We identified 192,119 high-quality SNVs, including 30,380 coding SNVs (cSNVs) in the experimental population.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature.

Erratum to: "A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention" [Translational Behavioral Medicine, Volume 4, Issue 3, pp 275-289].

[This corrects the article DOI: 10.1007/s13142-014-0282-z.].

Optimized Treatment of Fibromyalgia Using System Identification and Hybrid Model Predictive Control.

The term is used in behavioral health to describe individually-tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions.

A control systems engineering approach for adaptive behavioral interventions: illustration with a fibromyalgia intervention.

The term adaptive intervention has been used in behavioral medicine to describe operationalized and individually tailored strategies for prevention and treatment of chronic, relapsing disorders. Control systems engineering offers an attractive means for designing and implementing adaptive behavioral interventions that feature intensive measurement and frequent decision-making over time. This is illustrated in this paper for the case of a low-dose naltrexone treatment intervention for fibromyalgia.

Identification of Affine Linear Parameter Varying Models for Adaptive Interventions in Fibromyalgia Treatment.

There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances.

Optimal Input Signal Design for Data-Centric Estimation Methods.

Data-centric estimation methods such as Model-on-Demand and Direct Weight Optimization form attractive techniques for estimating unknown functions from noisy data. These methods rely on generating a local function approximation from a database of regressors at the current operating point with the process repeated at each new operating point. This paper examines the design of optimal input signals formulated to produce informative data to be used by local modeling procedures.