Cesar C. Palerm

Closed-Loop Insulin Delivery Using a Subcutaneous Glucose Sensor and Intraperitoneal Insulin Delivery: Feasibility study testing a new model for the artificial pancreas

OBJECTIVE Attempts to build an artificial pancreas by using subcutaneous insulin delivery from a portable pump guided by an subcutaneous glucose sensor have encountered delays and variability of insulin absorption. We tested closed-loop intraperitoneal insulin infusion from an implanted pump driven by an subcutaneous glucose sensor via a proportional-integral-derivative (PID) algorithm. RESEARCH DESIGN AND METHODS Two-day closed-loop therapy (except for a 15-min premeal manual bolus) was compared with a 1-day control phase with intraperitoneal open-loop insulin delivery, according to randomized order, in a hospital setting in eight type 1 diabetic patients treated by implanted pumps. The percentage of time spent with blood glucose in the 4.4–6.6 mmol/l range was the primary end point. RESULTS During the closed-loop phases, the mean ± SEM percentage of time spent with blood glucose in the 4.4–6.6 mmol/l range was significantly higher (39.1 ± 4.5 vs. 27.7 ± 6.2%, P = 0.05), and overall dispersion of blood glucose values was reduced among patients. Better closed-loop glucose control came from the time periods excluding the two early postprandial hours with a higher percentage of time in the 4.4–6.6 mmol/l range (46.3 ± 5.3 vs. 28.6 ± 7.4, P = 0.025) and lower mean blood glucose levels (6.9 ± 0.3 vs. 7.9 ± 0.6 mmol/l, P = 0.036). Time spent with blood glucose <3.3 mmol/l was low and similar for both investigational phases. CONCLUSIONS Our results demonstrate the feasibility of intraperitoneal insulin delivery for an artificial β-cell and support the need for further study. Moreover, according to a semiautomated mode, the features of the premeal bolus in terms of timing and amount warrant further research.

Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring—A Study on Hypoglycemic Clamp Data

Motivation: The fear of hypoglycemia remains an important limiting factor in the ability of an individual with type 1 diabetes to tightly regulate glycemia. Continuous glucose monitors provide important feedback to improve glycemic control, but there remains a need for these devices to better alarm of possible impending hypoglycemia, particularly overnight or other periods when the individual is engaged in activities that take their focus away from glucose monitoring. Methods: We have previously proposed an algorithm, based on the use of real-time glucose sensor signals and optimal estimation theory (Kalman filtering), to predict hypoglycemia; the algorithm was validated in simulation-based studies. In this article we further refine and validate the prediction algorithm based on the analysis of clinical hypoglycemic clamp data from 13 subjects. The sensitivity and specificity of the predictions are calculated with respect to reference blood glucose values obtained at the same sampling rate of the sensor. Results: For a 30-minute prediction horizon and alarm threshold of 70 mg/dl, the sensitivity and specificity were 90 and 79%, respectively, indicating that a 21% false alarm rate must be tolerated to predict 90% of the hypoglycemic events 30 minutes ahead of time. Shorter prediction horizons yield a significant improvement in sensitivity and specificity. Discussion: Sensitivity and specificity data as a function of prediction horizon and alarm threshold enable an individual to adjust the alarm to best meet their needs. Such decisions can be made depending on the subjects risk for hypoglycemia, for example.

Reduced Hypoglycemia and Increased Time in Target Using Closed-Loop Insulin Delivery During Nights With or Without Antecedent Afternoon Exercise in Type 1 Diabetes

OBJECTIVE Afternoon exercise increases the risk of nocturnal hypoglycemia (NH) in subjects with type 1 diabetes. We hypothesized that automated feedback-controlled closed-loop (CL) insulin delivery would be superior to open-loop (OL) control in preventing NH and maintaining a higher proportion of blood glucose levels within the target blood glucose range on nights with and without antecedent afternoon exercise. RESEARCH DESIGN AND METHODS Subjects completed two 48-h inpatient study periods in random order: usual OL control and CL control using a proportional-integrative-derivative plus insulin feedback algorithm. Each admission included a sedentary day and an exercise day, with a standardized protocol of 60 min of brisk treadmill walking to 65–70% maximum heart rate at 3:00 p.m. RESULTS Among 12 subjects (age 12–26 years, A1C 7.4 ± 0.6%), antecedent exercise increased the frequency of NH (reference blood glucose <60 mg/dL) during OL control from six to eight events. In contrast, there was only one NH event each on nights with and without antecedent exercise during CL control (P = 0.04 vs. OL nights). Overnight, the percentage of glucose values in target range was increased with CL control (P < 0.0001). Insulin delivery was lower between 10:00 p.m. and 2:00 a.m. on nights after exercise on CL versus OL, P = 0.008. CONCLUSIONS CL insulin delivery provides an effective means to reduce the risk of NH while increasing the percentage of time spent in target range, regardless of activity level in the mid-afternoon. These data suggest that CL control could be of benefit to patients with type 1 diabetes even if it is limited to the overnight period.

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.

Prandial Insulin Dosing Using Run-to-Run Control: Application of clinical data and medical expertise to define a suitable performance metric

OBJECTIVE—We propose a novel algorithm to adjust prandial insulin dose using sparse blood glucose measurements. The dose is adjusted on the basis of a performance measure for the same meal on the previous day. We determine the best performance measure and tune the algorithm to match the recommendations of experienced physicians. RESEARCH DESIGN AND METHODS—Eleven subjects with type 1 diabetes, using continuous subcutaneous insulin infusion, were recruited (seven women and four men, aged 21–65 years with A1C of 7.1 ± 1.3%). Basal insulin infusion rates were optimized. Target carbohydrate content for the lunch meal was calculated on the basis of a weight-maintenance diet. Over a period of 2–4 days, subjects were asked to measure their blood glucose according to the algorithms protocol. Starting with their usual insulin-to-carbohydrate ratio, the insulin bolus dose was titrated downward until postprandial glucose levels were high (180–250 mg/dl [10–14 mmol/l]). Subsequently, physicians made insulin bolus recommendations to normalize postprandial glucose concentrations. Graphical methods were then used to determine the most appropriate performance measure for the algorithm to match the physicians decisions. For the best performance measure, the gain of the controller was determined to be the best match to the dose recommendations of the physicians. RESULTS—The correlation between the clinically determined dose adjustments and those of the algorithm is R2 = 0.95, P < 1e − 18. CONCLUSIONS—We have shown how engineering methods can be melded with medical expertise to develop and refine a dosing algorithm. This algorithm has the potential of drastically simplifying the determination of correct insulin-to-carbohydrate ratios.

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.

In Silico Evaluation Platform for Artificial Pancreatic β-Cell Development—A Dynamic Simulator for Closed-Loop Control with Hardware-in-the-Loop

BACKGROUND A critical step in algorithm development for an artificial beta-cell is extensive in silico testing. Computer simulations usually involve only the controller software, leaving untested the hardware elements, including the critical communication interface between the controller and the glucose sensor and insulin pump. METHODS An in silico simulation platform has been developed that uses all of the components of the clinical system. At the core is a comprehensive in silico population model that covers the variability of principal metabolic parameters observed in vivo, to replace the human subject, with the ability to use historical clinical data. A continuous glucose monitor, in this case either the Abbott Diabetes Care (Alameda, CA) FreeStyle Navigator or the DexCom (San Diego, CA) STS7, is supplied with a glucose signal provided by the simulator. The Insulet (Bedford, MA) OmniPod insulin pump is also interfaced with the simulator to provide insulin delivery data. These hardware elements are an integral part of the system under testing, which also includes the algorithm components. RESULTS The system is unique in that it uses the same hardware components for simulations as are required in clinical trials, allowing for full-system level verification and validation. With a detailed mathematical model, a suite of patients can be simulated to reflect various conditions. Because all hardware is used, their related limitations are automatically included. CONCLUSIONS A complete artificial beta-cell evaluation platform was realized with the flexibility to interface various algorithms and patient models, allowing for the systematic analysis of monitoring and control algorithms. The system facilitates a variety of tests and challenges to the software and the component devices, streamlining preclinical validation trials.

Real-Time Continuous Glucose Monitoring in an Intensive Care Unit: Better Accuracy in Patients with Septic Shock

OBJECTIVE This study assessed the accuracy of real-time continuous glucose monitoring system (RTCGMS) devices in an intensive care unit (ICU) to determine whether the septic status of the patient has any influence on the accuracy of the RTCGMS. SUBJECTS AND METHODS In total, 41 patients on insulin therapy were included. Patients were monitored for 72 h using RTCGMS. Arterial blood glucose (ABG) samples were obtained following the protocol established in the ICU. The results were evaluated using paired values (excluding those used for calibration) with the performance assessed using numerical accuracy. Nonparametric tests were used to determine statistically significant differences in accuracy. RESULTS In total, 956 ABG/RTCGMS pairs were analyzed. The overall median relative absolute difference (RAD) was 13.5%, and the International Organization for Standardization (ISO) criteria were 68.1%. The median RADs reported for patients with septic shock, with sepsis, and without sepsis were 11.2%, 14.3%, and 16.3%, respectively (P<0.05). Measurements meeting the ISO criteria were 74.5%, 65.6%, and 63.7% for patients with septic shock, with sepsis, and without sepsis, respectively (P<0.05). CONCLUSIONS The results showed that the septic status of patients influenced the accuracy of the RTCGMS in the ICU. Accuracy was significantly better in patients with septic shock in comparison with the other patient cohorts.

Automated regulation of hemodynamic variables

Experimental studies of two control methodologies for regulating multiple variables in critical care patients are described. The control strategies for the regulation of mean arterial pressure and cardiac output use vasoactive and inotropic drugs. Corresponding experimental results from the evaluation of the controllers with canines are presented.

Clinical update on optimal prandial insulin dosing using a refined run-to-run control algorithm.

Background: This article provides a clinical update using a novel run-to-run algorithm to optimize prandial insulin dosing based on sparse glucose measurements from the previous days meals. The objective was to use a refined run-to-run algorithm to calculate prandial insulin-to-carbohydrate ratios (I:CHO) for meals of variable carbohydrate content in subjects with type 1 diabetes (T1DM). Method: The open-labeled, nonrandomized study took place over a 6-week period in a nonprofit research center. Nine subjects with T1DM using continuous subcutaneous insulin infusion participated. Basal insulin rates were optimized using continuous glucose monitoring, with a target fasting blood glucose of 90 mg/dl. Subjects monitored blood glucose concentration at the beginning of the meal and at 60 and 120 minutes after the start of the meal. They were instructed to start meals with blood glucose levels between 70 and 130 mg/dl. Subjects were contacted daily to collect data for the previous 24-hour period and to give them the physician-approved, algorithm-derived I:CHO ratios for the next 24 hours. Subjects calculated the amount of the insulin bolus for each meal based on the corresponding I:CHO and their estimate of the meals carbohydrate content. One- and 2-hour postprandial glucose concentrations served as the main outcome measures. Results: The mean 1-hour postprandial blood glucose level was 104 ± 19 mg/dl. The 2-hour postprandial levels (96.5 ± 18 mg/dl) approached the preprandial levels (90.1 ± 13 mg/dl). Conclusions: Run-to-run algorithms are able to improve postprandial blood glucose levels in subjects with T1DM.