Lois Jovanovič

Managing preexisting diabetes for pregnancy: Summary of evidence and consensus recommendations for care

This document presents consensus panel recommendations for the medical care of pregnant women with preexisting diabetes, including type 1 and type 2 diabetes. The intent is to help clinicians deal with the broad spectrum of problems that arise in management of diabetes before and during pregnancy, and to prepare diabetic women for treatment that may reduce complications in the years after pregnancy. A thorough discussion of the evidence supporting the recommendations is presented in the book, Management of Preexisting Diabetes and Pregnancy , authored by the consensus panel and published by the American Diabetes Association (ADA) in 2008 (1). A consensus statement on obstetrical and postpartum management will appear separately. The recommendations are diagnostic and therapeutic actions that are known or believed to favorably affect maternal and perinatal outcomes in pregnancies complicated by diabetes. The grading system adapted by the ADA was used to clarify and codify the evidence that forms the basis for the recommendations (2). Unfortunately there is a paucity of randomized controlled trials (RCTs) of the different aspects of management of diabetes and pregnancy. Therefore our recommendations are often based on trials conducted in nonpregnant diabetic women or nondiabetic pregnant women, as well as on peer-reviewed experience before and during pregnancy in women with preexisting diabetes (3–4). We also reviewed and adapted existing diabetes and pregnancy guidelines (5–10) and guidelines on diabetes complications and comorbidities (2,3,11–14). ### A. Organization of preconception and pregnancy care #### Recommendations

AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY - CLINICAL PRACTICE GUIDELINES FOR DEVELOPING A DIABETES MELLITUS COMPREHENSIVE CARE PLAN - 2015

The American Association of Clinical Endocrinologists/American College of Endocrinology Medical Guidelines for Clinical Practice are systematically developed statements to assist healthcare professionals in medical decision making for specific clinical conditions. Most of the content herein is based on literature reviews. In areas of uncertainty, professional judgment was applied. These guidelines are a working document that reflects the state of the field at the time of publication. Because rapid changes in this area are expected, periodic revisions are inevitable. We encourage medical professionals to use this information in conjunction with their best clinical judgment. The presented recommendations may not be appropriate in all situations. Any decision by practitioners to apply these guidelines must be made in light of local resources and individual patient circumstances. Abbreviations: A1C = hemoglobin A1c AACE = American Association of Clinical Endocrinologists ACCORD = Action to Control Cardiovascu...

Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events.

Background: Development of an artificial pancreas based on an automatic closed-loop algorithm that uses a subcutaneous insulin pump and continuous glucose sensor is a goal for biomedical engineering research. However, closing the loop for the artificial pancreas still presents many challenges, including model identification and design of a control algorithm that will keep the type 1 diabetes mellitus subject in normoglycemia for the longest duration and under maximal safety considerations. Method: An artificial pancreatic β-cell based on zone model predictive control (zone-MPC) that is tuned automatically has been evaluated on the University of Virginia/University of Padova Food and Drug Administration-accepted metabolic simulator. Zone-MPC is applied when a fixed set point is not defined and the control variable objective can be expressed as a zone. Because euglycemia is usually defined as a range, zone-MPC is a natural control strategy for the artificial pancreatic β-cell. Clinical data usually include discrete information about insulin delivery and meals, which can be used to generate personalized models. It is argued that mapping clinical insulin administration and meal history through two different second-order transfer functions improves the identification accuracy of these models. Moreover, using mapped insulin as an additional state in zone-MPC enriches information about past control moves, thereby reducing the probability of overdosing. In this study, zone-MPC is tested in three different modes using unannounced and announced meals at their nominal value and with 40% uncertainty. Ten adult in silico subjects were evaluated following a scenario of mixed meals with 75, 75, and 50 grams of carbohydrates (CHOs) consumed at 7 am, 1 pm, and 8 pm, respectively. Zone-MPC results are compared to those of the “optimal” open-loop preadjusted treatment. Results: Zone-MPC succeeds in maintaining glycemic responses closer to euglycemia compared to the “optimal” open-loop treatment in te three different modes with and without meal announcement. In the face of meal uncertainty, announced zone-MPC presented only marginally improved results over unannounced zone-MPC. When considering user error in CHO estimation and the need to interact with the system, unannounced zone-MPC is an appealing alternative. Conclusions: Zone-MPC reduces the variability of control moves over fixed set point control without the need to detune the controller. This strategy gives zone-MPC the ability to act quickly when needed and reduce unnecessary control moves in the euglycemic range.

Real-Time hypoglycemia prediction suite using continuous glucose monitoring: a safety net for the artificial pancreas.

OBJECTIVE The purpose of this study was to develop an advanced algorithm that detects pending hypoglycemia and then suspends basal insulin delivery. This approach can provide a solution to the problem of nocturnal hypoglycemia, a major concern of patients with diabetes. RESEARCH DESIGN AND METHODS This real-time hypoglycemia prediction algorithm (HPA) combines five individual algorithms, all based on continuous glucose monitoring 1-min data. A predictive alarm is issued by a voting algorithm when a hypoglycemic event is predicted to occur in the next 35 min. The HPA system was developed using data derived from 21 Navigator studies that assessed Navigator function over 24 h in children with type 1 diabetes. We confirmed the function of the HPA using a separate dataset from 22 admissions of type 1 diabetic subjects. During these admissions, hypoglycemia was induced by gradual increases in the basal insulin infusion rate up to 180% from the subjects own baseline infusion rate. RESULTS Using a prediction horizon of 35 min, a glucose threshold of 80 mg/dl, and a voting threshold of three of five algorithms to predict hypoglycemia (defined as a FreeStyle plasma glucose readings <60 mg/dl), the HPA predicted 91% of the hypoglycemic events. When four of five algorithms were required to be positive, then 82% of the events were predicted. CONCLUSIONS The HPA will enable automated insulin-pump suspension in response to a pending event that has been detected prior to severe immediate complications.

Safety Constraints in an Artificial Pancreatic β Cell: An Implementation of Model Predictive Control with Insulin on Board

Background: Type 1 diabetes mellitus (T1DM) is characterized by the destruction of pancreatic β cells, resulting in the inability to produce sufficient insulin to maintain normoglycemia. As a result, people with T1DM depend on exogenous insulin that is given either by multiple daily injections or by an insulin pump to control their blood glucose. A challenging task is to design the next step in T1DM therapy: a fully automated insulin delivery system consisting of an artificial pancreatic β cell that shall provide both safe and effective therapy. The core of such a system is a control algorithm that calculates the insulin dose based on automated glucose measurements. Methods: A model predictive control (MPC) algorithm was designed to control glycemia by controlling exogenous insulin delivery. The MPC algorithm contained a dynamic safety constraint, insulin on board (IOB), which incorporated the clinical values of correction factor and insulin-to-carbohydrate ratio along with estimated insulin action decay curves as part of the optimal control solution. Results: The results emphasized the ability of the IOB constraint to significantly improve the glucose/insulin control trajectories in the presence of aggressive control actions. The simulation results indicated that 50% of the simulations conducted without the IOB constraint resulted in hypoglycemic events, compared to 10% of the simulations that included the IOB constraint. Conclusions: Achieving both efficacy and safety in an artificial pancreatic β cell calls for an IOB safety constraint that is able to override aggressive control moves (large insulin doses), thereby minimizing the risk of hypoglycemia.

Quest for the Artificial Pancreas: Combining Technology with Treatment

The various components of the artificial pancreas puzzle are being put into place. Features such as communication, control, modeling, and learning are being realized presently. Steps have been set in motion to carry the conceptual design through simulation to clinical implementation. The challenging pieces still to be addressed include stress and exercise; as integral parts of the ultimate goal, effort has begun to shift toward overcoming the remaining hurdles to the full artificial pancreas. The artificial pancreas is close to becoming a reality, driven by technology, and the expectation that lives will be improved.

Clinical Evaluation of a Personalized Artificial Pancreas

OBJECTIVE An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration. RESEARCH DESIGN AND METHODS We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, nsubjects = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or “brain,” can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates. RESULTS Initial glucose values ranged from 84–251 mg/dL. Blood glucose was kept in the near-normal range (80–180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively. CONCLUSIONS These encouraging short-term results reveal the ability of a control algorithm tailored to an individual’s glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.

Modular Artificial β-Cell System: A Prototype for Clinical Research:

Background: The quest toward an artificial β-cell has been accelerating, propelled by recent technological advances in subcutaneous glucose sensors and insulin pumps. The development and clinical testing of algorithms involves several challenges: communication and data transfer between a sensor and a pump via computer, a human interface presenting real-time information to the physician, safety issues when an automated system is used to administer insulin, and an architecture that supports different sensors, pumps, and control algorithms. These challenges were addressed in the development of a modular artificial β-cell system for clinical research. Methods: The developmental environment of MATLAB® (The MathWorks, Inc., Natick, MA) allowed the flexible implementation of communication protocols for different sensors and pumps. The system has a plug-and-play option for the control algorithm and a human interface that presents and logs the data, enforces protocol safety rules, and facilitates physician oversight. Results: A novel platform for use in clinical research trials was realized as a bridge toward a portable unit. This prototype encapsulates communication between the control algorithm, the pump, and the sensors. Its intuitive human interface presents all the relevant patient information to the physician and allows events to be electronically logged. It facilitates subject safety by way of integrated interlocks, checklists, and alarms. Conclusion: The modular design of the system allows for the robust testing of various sensors and pumps as well as feedback control, meal detection, predictive hypoglycemia alarms, and device-related algorithms to detect sensor or pump failure.

Glucose Estimation and Prediction through Meal Responses Using Ambulatory Subject Data for Advisory Mode Model Predictive Control

Background: A primary challenge for closed-loop glucose control in type 1 diabetes mellitus (T1DM) is the development of a control strategy that will be applicable during all daily activities, including meals, stress, and exercise. A model-based control algorithm requires a mathematical model that has the simplicity for online glucose prediction, yet retains the complexity necessary to cope with variations in insulin sensitivities and carbohydrate ingestion. Methods: A modified Bergman minimal model was linearized for Kalman filter (KF) state estimation on data from T1DM subjects, and multiple methods of parameter augmentation were developed for online adaptation. In addition, model deterioration for glucose prediction was assessed to determine an appropriate prediction horizon for model predictive control (MPC). Furthermore, MPC strategies were validated using advisory mode simulations. Results: Twenty days of continuous glucose data, which included 97 meals, were evaluated for three subjects. A constant parameter minimal model was used to predict glucose levels for normal days with meal announcement and with a maximum prediction horizon of approximately 45 minutes. In order to attain this prediction horizon in the absence of meal announcement, parameter adaptation was necessary to capture the glucose disturbance. Evaluation of advisory mode MPC permitted effective tuning for a moderately aggressive controller that responded well to meal disturbances. Conclusions: Estimation and prediction of glucose were accomplished using a KF based on a modified Bergman model. For a model with no meal announcement, parameter adaptation provided the means for closed-loop implementation. This state estimation and model validation scheme established the necessary framework for advisory mode MPC.

Modeling the Effects of Subcutaneous Insulin Administration and Carbohydrate Consumption on Blood Glucose

Background: Estimation of the magnitude and duration of effects of carbohydrate (CHO) and subcutaneously administered insulin on blood glucose (BG) is required for improved BG regulation in people with type 1 diabetes mellitus (T1DM). The goal of this study was to quantify these effects in people with T1DM using a novel protocol. Methods: The protocol duration was 8 hours: A 1–3 U subcutaneous (SC) insulin bolus was administered and a 25-g CHO meal was consumed, with these inputs separated by 3–5 hours. The DexCom SEVEN® PLUS continuous glucose monitor was used to obtain SC glucose measurements every 5 minutes and YSI 2300 Stat Plus was used to obtain intravenous glucose measurements every 15 minutes. Results: The protocol was tested on 11 subjects at Sansum Diabetes Research Institute. The intersubject parameter coefficient of variation for the best identification method was 170%. The mean percentages of output variation explained by the bolus insulin and meal models were 68 and 69%, respectively, with root mean square error of 14 and 10 mg/dl, respectively. Relationships between the model parameters and clinical parameters were observed. Conclusion: Separation of insulin boluses and meals in time allowed unique identification of model parameters. The wide intersubject variation in parameters supports the notion that glucose-insulin models and thus insulin delivery algorithms for people with T1DM should be personalized. This experimental protocol could be used to refine estimates of the correction factor and the insulin-to-carbohydrate ratio used by people with T1DM.