Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Neonatal Intensive Care Unit <s> Background: Control of hyperglycemia in adult medical and surgical intensive care units (ICUs) has been shown to dramatically decrease morbidity and mortality. Algorithms to achieve glycemic control in the ICU setting are evolving. We have evaluated the use of a discrete proportional-integral-derivative (PID) algorithm to control hyperglycemia in pediatric ICU (PICU) patients both with and without diabetes. Methods: Six PICU patients [four with diabetic ketoacidosis (DKA) and two with glucocorticoid-induced hyperglycemia] with glucose values >150 mg/dL were enrolled. Their hyperglycemia was managed with a PID algorithm that provided recommendations for both changes in the intravenous insulin infusion rate and the time to obtain the next discrete glucose value. Glucose targets were adjusted based on clinical circumstances. Results: Patients (mean age 9.2 years; range 1.8–14 years) utilized the algorithm for a total of 454.4 h. Mean time to the initial glucose target was 8.7 h (range 1.3–15.1 h) in five pat... <s> BIB001 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Neonatal Intensive Care Unit <s> OBJECTIVE: Studies showing improved outcomes with tight glycemic control in the intensive care unit (ICU) have resulted in a substantial number of new insulin delivery algorithms being proposed. The present study highlights mechanisms used in the better-known approaches, examines what might be critical differences among them, and uses systems theory to characterize the conditions under which each can be expected to perform best. METHODS: Algorithm dose (DeltaI/DeltaG) and step (response to a persistent elevation in glucose) response curves were calculated for written instruction algorithms, developed at the Providence Heart and Vascular Institute (Portland [P] protocol), the University of Washington (UW), and Yale University (Y), together with similar curves for the Glucommander (GM) and proportional integral derivative (PID) computer algorithms. From the simulated curves, different mechanisms used to adjust insulin delivery were identified. RESULTS: All algorithms increased insulin delivery in response to persistent hyperglycemia, but the mechanism used altered the algorithm's sensitivity to glucose, or gain, in the GM, UW, and Y protocols, while leaving it unchanged for the P protocol and PID algorithm. CONCLUSIONS: The increase in insulin delivery in response to persistent hyperglycemia observed with all the algorithms can be expected to bring subjects who respond to insulin to targeted glucose ranges. However, because the PID and P protocols did not alter the insulin delivery response curves, these algorithms can be expected to take longer to achieve target glucose levels in individuals who are insulin resistant and/or are exposed to increased carbohydrate loads (e.g., glucose infusions). By contrast, the GM, UW, and Y algorithms can be expected to adapt to the insulin resistance such that the time to achieve target levels is unchanged if the time for insulin to act does not change. If the insulin resistance is accompanied by a longer time for insulin to act, the UW, Y, and GM algorithms may increase the risk of hypoglycemia. Under these conditions, the longer time required for the PID and P protocols to achieve a target glucose level may be a reasonable trade-off for no increase in the risk of hypoglycemia. <s> BIB002
The last project developed by the authors early in 2016 and still under way is Neokid. Neokid originated from the need to tightly control blood glucose in preterm newborns admitted at the Neonatal Intensive Care Unit (NICU) of the University Hospital in Padova. Since preterm newborns are fragile patients, heel pricks for taking blood samples may not be as frequent as required, and the probability that hypo-or hyper-glycemia episodes will go undetected is much higher than in adults. To overcome the problem, the NICU started investigating the effectiveness of using CGM sensors to support the best practices for calculating glucose infusion rates based on the literature BIB001 and compare their outcomes with the manual therapy previously adopted. The primary goal of Neokid is therefore to enforce remote monitoring of blood glucose values on those patients and automate the computation of the infusion rates, suggesting in real time the most appropriate values to the NICU staff. Moreover, all the neonates at the NICU are monitored and their data are displayed on a central monitoring station alerting the staff whenever an alteration of any vital parameter is noticed. This is crucial in an eventful setting, such as the NICU, where centralized monitoring facilitates the simultaneous analysis of multiple parameters and is essential for properly driving clinical decisions. Unfortunately, there is still no commercial equipment available addressing the acquisition of CGM data and making them available on the ward control panel. Before Neokid, CGM was accomplished through separate glucometers lying on cradle drawers and their readings could only be acquired when some member of the staff visited each cradle. Thus, Neokid also helps in integrating CGM data for all patients on a single PC display, tracking any clinical alteration or maneuver. Besides the plain acquisition of CGM values, Neokid also allows for annotating events such as rest, intraventricular hemorrage, acidosis, pneumothorax or pain and to correlate their occurrences with CGM values during data analysis. For the Neokid project, the chosen CGM device is still the Dexcom G4 Platinum with Share. Thanks to the release by Dexcom, Inc. of the protocol for communicating via Bluetooth Low Energy with the receiver, the authors developed a telemonitoring application running on a smartphone. This application replicates in real time the information sent by the Dexcom G4 Platinum with Share and relays these data to the remote monitoring server using a 3G/UMTS/WiFi connection. The server includes a web application, similar to those already discussed in the previous sections, which makes data available to nurses and doctors on a PC located in the ward room of the NICU. An additional component has also been developed to accomplish the calculations of the glucose infusion rates adopting an algorithm published in the literature BIB002 . The functionality of the main components developed for the Neokid system is shown in Figure 6 . The main screen of the smartphone app with the last acquired glycemia and calibration values is shown in part (a), while (b) shows the detailed CGM track for a single patient with two events. Finally, (c) illustrates the panel with the suggested glucose infusion rates. The Neokid project is an interesting example of the advantages that may be achieved when software design issues enforce modularity and reusability. First, we were able to reuse the whole web application already implemented for the AP@home project with very limited changes. Moreover, even though the web application was mainly meant to be accessed in the ward, thanks to its generic availability on the Internet, doctors could make use of it also from their offices, and sometimes even from home. Finally, knowledge of the protocol for interacting with the CGM devices is essential, and, when it is disclosed by the manufacturers, custom applications may be successfully implemented in very short time periods.