The hypoglycemia prediction algorithm (HPA) was developed using data derived from 21 Navigator studies, which assessed Navigator function over 24 h in children with type 1 diabetes, aged 3–18 years, conducted in clinical research centers (CRCs) (¹¹). Then the HPA functionality was confirmed using a separate dataset from 22 CRC admissions of type 1 diabetic subjects with a mean age of 20 years (range 6–38). In this study, hypoglycemia was induced by gradual increases in the basal insulin infusion rate by a mean of 180%, 18 of 22 subjects (82%) reached a glucose value of ≤60 mg/dl (¹²,¹³). Promising results were reported by Buckingham et al. (¹²), where when two different algorithms were used 60% of the pending hypoglycemic events were predicted and prevented.

CGM data were introduced to the HPA as if it were a real-time measurement; the different algorithms analyzed the data and an alarm was produced if a quorum was reached by the voting algorithm. Three hypoglycemia thresholds of 70, 80, and 90 mg/dl were evaluated, each with three different prediction horizons (V) of 35, 45, and 55 min and with three voting thresholds of 3, 4, and 5.

The five hypoglycemic prediction alarms were run for all proposed parameter combinations on 18 sets of data from 18 admissions. It should be noted that the reported results are based solely on the predictive part of the method.

As can be seen from Fig. 2, hypoglycemia was reached at around noon (defined as glucose ≤70 mg/dl). This event has been predicted by the different algorithms 55, 45, and 35 min ahead of the event, and an alarm could have been issued at this time depending on the quorum threshold (e.g., 55-min warning time if two different algorithms were to issue a positive vote twice in a 10 min window). If the number of positive alarms required was three or four, a warning time would have been 40 and 35 min, respectively, sufficient time for a suspension of the pump to have prevented the event.

Table 1 shows the results from running the individual and combined algorithms against the historical pump shut-off data. The numbered columns (e.g., 1, 2, 3, 4, and 5) indicate the voting threshold for the case. The data also show that the prediction rate declines as the voting threshold increases. This can be seen in the range of prediction times obtained by varying the settings of the tuning parameters, namely, hypoglycemia prediction time, hypoglycemia threshold value, and the voting threshold. As an example, 91% of the events were predicted 35 min prior to the event using a voting threshold of three, where voting of four of five predicted 82% of the events 35–55 min ahead with glucose threshold of 80 mg/dl. However, in theory, a higher success rate could be obtained by allowing any one of five to issue an alarm. This balance between aggressiveness of the HPA and effectiveness is an important factor since too many false alarms are a detriment to safety systems will result in disconnecting of the system by the user, rendering the system useless. HPA tuning knobs (prediction horizon, alarm threshold, and voting threshold, as seen in Table 1) allows the algorithm to be adjusted to meet the subject preferences as far as defining the hypoglycemia threshold, time to alarm prior to the event, and aggressiveness of the algorithm. These settings could vary between day and night and be tuned to meet individual insulin sensitivity, allowing the user to enhance specificity that would prevent false alarms resulting in pump suspension, where during the day a more aggressive tuning can be set that can alert the user to take corrective action prior to the need to suspend the pump. The use of a CGM in clinical decision making is the first step toward the artificial pancreas. The prevention of nocturnal hypoglycemia based on glucose predictions as well as missed-meal alarms will reduce glucose variability and clinical complications resulting from extreme blood glucose concentrations. CGM technology, together with telemedicine applications such as E911 (²²), can provide remote glucose monitoring and triangulation as well as pump suspension. This technology will help in improving the well-being of people with diabetes and peace of mind to families of children with diabetes.

When a safety algorithm is suggested, the false-positive rate is equally as important as the true-positive rate. The evaluation of the true positive has been addressed based on retrospective analysis of clinical data. The specificity of the algorithm was further evaluated prospectively using the UVa/Padova FDA-accepted (²³) Metabolic Simulator and is currently under clinical evaluation that will be reported in a subsequent publication. The frequency of false-positive alarms of the HPA has been assessed by running the simulator under standard conditions with a meal at 5:00 P.M., starting the hypoglycemia prediction algorithms at 9:00 P.M. and running it until 7:00 A.M. the following morning before a breakfast meal. Furthermore, the HPA was evaluated overnight using twice the usual basal insulin in order to induce hypoglycemia. The prospective analysis of the algorithm with tuning of 80 mg/dl, 45 min, and two for hypoglycemia threshold, prediction horizon and voting threshold, respectively, is provided below. This simulation supported the clinical results with only four in silico subjects crossing the 60 mg/dl blood glucose threshold out of 100 in silico adult subjects, where the glucose nadir is marginally <60 mg/dl. As can be seen from Fig. 3, the HPA predicted a pending hypoglycemic event when glucose concentration was ∼104 mg/dl and suspended the pump for 90 min. This resulted in prevention of the event where, in successive simulation without the use of the algorithm, the glucose dropped to extremely low values. The false-positive rate of the algorithm with the same tuning was 9% based on a population of 100 different in silico subjects and noisy sensor. However, only three out of the nine cases where a false alarm was issued resulted in a glucose elevation of >20 mg/dl from the baseline that resulted in hyperglycemic event. It should be noted that this needs to be further evaluated by extensive clinical trials to better assess the algorithm.

The use of a sophisticated voting algorithm allows an extra degree of safety prior to issuing an alarm. In addition, voting enables the use of five different individual algorithms to predict pending hypoglycemia and not to rely on one algorithm that may or may not be the most suitable to address the variability among type 1 diabetic subjects. Furthermore, the use of a suite of algorithms allows a more robust system that can cope with glucose variability and the different glucose drop patterns that may affect rate of change and the ability to detect a pending event by a single algorithm.

The use of the HPA would allow for triggering of a warning alarm and/or suspension of an insulin pump, which should decrease the risk of severe hypoglycemia. Based on clinical evaluation, insulin delivery will most likely need to be suspended 30–50 min before a projected hypoglycemic event in order to prevent most hypoglycemic events (¹²). On one hand, longer prediction time may provide greater ability to prevent hypoglycemia events; on the other hand, it may be impractical to suspend a pump too far from an event that may not happen due to human factors (e.g., a planned meal). The Hypoglycemia Prediction Algorithm (HPA) tuning allows flexibility in the aggressiveness of the alarm and can be set to meet user preferences. Furthermore, this technology can be easily implemented in current CGM systems and as a safety net to further artificial pancreas development.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

We acknowledge the Juvenile Diabetes Research Foundation (grants 22-2006-1115, 22-2006-1108, and 22-2007-479) and the Otis Williams Fund at the Santa Barbara Foundation for their financial support.

H.P.C. has received supplies/devices for research purposes from Abbott Diabetes Care. B.A.B. was given honorarium from Abbott Diabetes Care for a lecture in February 2009. No other potential conflicts of interest relevant to this article were reported.