Evaluating the automated blood glucose pattern detection and case-retrieval modules of the 4 Diabetes Support System

Artificial intelligence and diabetes technology: A review

Artificial intelligence (AI) is widely discussed in the popular literature and is portrayed as impacting many aspects of human life, both in and out of the workplace. The potential for revolutionizing healthcare is significant because of the availability of increasingly powerful computational platforms and methods, along with increasingly informative sources of patient data, both in and out of clinical settings. This review aims to provide a realistic assessment of the potential for AI in understanding and managing diabetes, accounting for the state of the art in the methodology and medical devices that collect data, process data, and act accordingly. Acknowledging that many conflicting definitions of AI have been put forth, this article attempts to characterize the main elements of the field as they relate to diabetes, identifying the main perspectives and methods that can (i) affect basic understanding of the disease, (ii) affect understanding of risk factors (genetic, clinical, and behavioral) of diabetes development, (iii) improve diagnosis, (iv) improve understanding of the arc of disease (progression and personal/societal impact), and finally (v) improve treatment.

Artificial Intelligence in Decision Support Systems for Type 1 Diabetes

Type 1 diabetes (T1D) is a chronic health condition resulting from pancreatic beta cell dysfunction and insulin depletion. While automated insulin delivery systems are now available, many people choose to manage insulin delivery manually through insulin pumps or through multiple daily injections. Frequent insulin titrations are needed to adequately manage glucose, however, provider adjustments are typically made every several months. Recent automated decision support systems incorporate artificial intelligence algorithms to deliver personalized recommendations regarding insulin doses and daily behaviors. This paper presents a comprehensive review of computational and artificial intelligence-based decision support systems to manage T1D. Articles were obtained from PubMed, IEEE Xplore, and ScienceDirect databases. No time period restrictions were imposed on the search. After removing off-topic articles and duplicates, 562 articles were left to review. Of those articles, we identified 61 articles for comprehensive review based on algorithm evaluation using real-world human data, in silico trials, or clinical studies. We grouped decision support systems into general categories of (1) those which recommend adjustments to insulin and (2) those which predict and help avoid hypoglycemia. We review the artificial intelligence methods used for each type of decision support system, and discuss the performance and potential applications of these systems.

The Promise and Perils of Wearable Physiological Sensors for Diabetes Management

Development of truly useful wearable physiologic monitoring devices for use in diabetes management is still in its infancy. From wearable activity monitors such as fitness trackers and smart watches to contact lenses measuring glucose levels in tears, we are just at the threshold of their coming use in medicine. Ultimately, such devices could help to improve the performance of sense-and-respond insulin pumps, illuminate the impact of physical activity on blood glucose levels, and improve patient safety. This is a summary of our experience attempting to use such devices to enhance continuous glucose monitoring-augmented insulin pump therapy. We discuss the current status and present difficulties with available devices, and review the potential for future use.

Artificial Intelligence Methodologies and Their Application to Diabetes

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study

BACKGROUND

The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks.

METHODS

Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia.

RESULTS

Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0-8.2) in week 1 versus 2.0 (0.5-6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study.

CONCLUSION

The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.

Advanced Insulin Bolus Advisor Based on Run-To-Run Control and Case-Based Reasoning

This paper presents an advanced insulin bolus advisor for people with diabetes on multiple daily injections or insulin pump therapy. The proposed system, which runs on a smartphone, keeps the simplicity of a standard bolus calculator while enhancing its performance by providing more adaptability and flexibility. This is achieved by means of applying a retrospective optimization of the insulin bolus therapy using a novel combination of run-to-run (R2R) that uses intermittent continuous glucose monitoring data, and case-based reasoning (CBR). The validity of the proposed approach has been proven by in-silico studies using the FDA-accepted UVa-Padova type 1 diabetes simulator. Tests under more realistic in-silico scenarios are achieved by updating the simulator to emulate intrasubject insulin sensitivity variations and uncertainty in the capillarity measurements and carbohydrate intake. The CBR(R2R) algorithm performed well in simulations by significantly reducing the mean blood glucose, increasing the time in euglycemia and completely eliminating hypoglycaemia. Finally, compared to an R2R stand-alone version of the algorithm, the CBR(R2R) algorithm performed better in both adults and adolescent populations, proving the benefit of the utilization of CBR. In particular, the mean blood glucose improved from 166 ± 39 to 150 ± 16 in the adult populations (p = 0.03) and from 167 ± 25 to 162 ± 23 in the adolescent population (p = 0.06). In addition, CBR(R2R) was able to completely eliminate hypoglycaemia, while the R2R alone was not able to do it in the adolescent population.

ProAct study: new features of insulin pumps improve diabetes management and glycemic control in patients after transition of continuous subcutaneous insulin infusion systems

BACKGROUND

Continuous subcutaneous insulin infusion (CSII) patients experience switches of pump systems on a regular basis. We investigated the impact of transition from older pumps to the Accu-Chek(®) Combo system (Roche Diagnostics Deutschland GmbH, Mannheim, Germany) on a patient's glycemic control and diabetes management.

PATIENTS AND METHODS

In total, 299 patients (172 female, 127 male; mean±SD age, 39.4±15.2 years; CSII duration, 7.0±5.2 years) were enrolled by 61 European sites into this uncontrolled prospective trial. Glycemic control, safety, and diabetes management parameters were measured at baseline and after 3 and 6 months. Changes from baseline were analyzed.

RESULTS

After transition to the new insulin pump, mean±SD hemoglobin A1c (HbA1c) values decreased from 7.8±1.1% (baseline) to 7.7±1.1% (end point). The proportion of patients with HbA1c <7.0% was slightly higher at the end of the study (29.6%) than at baseline (25.2%), whereas the proportion of patients with HbA1c >8.0% decreased (baseline, 36.2%; end point, 32.7%; P<0.05). The number of hypoglycemic episodes (blood glucose<70 mg/dL) improved slightly during the study (baseline, 40.4±34.0 events/quarter; end point, 39.2±33.9 events/quarter). Glycemic control improved significantly in the group with an initial HbA1c >8.0% (-0.46%; P<0.001) and remained solidly stable in the group with an initial HbA1c <7% (+0.04%; not significant). Short-term (<3 years) pump users (n=48) had a larger HbA1c decrease (-0.40%) than long-term (≥3 years) users (n=251) (-0.07%; P<0.05). The number of blood glucose measurements increased (3.7±1.9/day vs. 4.4±1.8/day; P<0.05), whereas the number of insulin boluses decreased (5.1±1.9/day vs. 4.6±1.5/day; P<0.05) during the study.

CONCLUSIONS

Transition from older pump systems to the Accu-Chek Combo system in a large patient population resulted in stable glycemic control with significant improvements in HbA1c in patients with unsatisfactory baseline HbA1c and shorter pump use. Increased frequency of self-monitoring of blood glucose and decrease of bolus frequency could suggest a more confident diabetes management and a reduced need for correction boluses.

Use of Automated Bolus Calculators for Diabetes Management

Fewer than 30 % of patients with diabetes who are on insulin therapy achieve target glycated haemoglobin (HbA1C) levels. Automated bolus calculators (ABCs) are now almost universally used for patients on insulin pump therapy to calculate pre-meal insulin doses. Use of ABCs in glucose monitors and smart phone applications have the potential to improve glucose control in a larger population of individuals with diabetes on insulin therapy by overcoming the fear of hypoglycaemia and assisting those with low numeracy skills.

A consensus perceived glycemic variability metric

OBJECTIVE

Glycemic variability (GV) is an important component of overall glycemic control for patients with diabetes mellitus. Physicians are able to recognize excessive GV from continuous glucose monitoring (CGM) plots; however, there is currently no universally agreed upon GV metric. The objective of this study was to develop a consensus perceived glycemic variability (CPGV) metric that could be routinely applied to CGM data to assess diabetes mellitus control.

METHODS

Twelve physicians actively managing patients with type 1 diabetes mellitus rated a total of 250 24 h CGM plots as exhibiting low, borderline, high, or extremely high GV. Ratings were averaged to obtain a consensus and then input into two machine learning algorithms: multilayer perceptrons (MPs) and support vector machines for regression (SVR). In silica experiments were run using each algorithm with different combinations of 12 descriptive input features. Ten-fold cross validation was used to evaluate the performance of each model.

RESULTS

The SVR models approximated the physician consensus ratings of unseen CGM plots better than the MP models. When judged by the root mean square error, the best SVR model performed comparably to individual physicians at matching consensus ratings. When applied to 262 different CGM plots as a screen for excessive GV, this model had accuracy, sensitivity, and specificity of 90.1%, 97.0%, and 74.1%, respectively. It significantly outperformed mean amplitude of glycemic excursion, standard deviation, distance traveled, and excursion frequency.

CONCLUSIONS

This new CPGV metric could be used as a routine measure of overall glucose control to supplement glycosylated hemoglobin in clinical practice.

Model-driven diabetes care: study protocol for a randomized controlled trial

Background

People with type 1 diabetes who use electronic self-help tools register a large amount of information about their disease on their participating devices; however, this information is rarely utilized beyond the immediate investigation. We have developed a diabetes diary for mobile phones and a statistics-based feedback module, which we have named Diastat, to give data-driven feedback to the patient based on their own data.

Method

In this study, up to 40 participants will be given a smartphone on which is loaded a diabetes self-help application (app), the Few Touch Application (FTA). Participants will be randomized into two groups to be given access to Diastat 4 or 12 weeks, respectively after receiving the smartphone, and will use the FTA with Diastat for 8 weeks after this point. The primary endpoint is the frequency of high and low blood-glucose measurements.

Discussion

The study will investigate the effect of data-driven feedback to patients. Our hypothesis is that this will improve glycemic control and reduce variability. The endpoints are robust indicators that can be assembled with minimal effort by the patient beyond normal routine.

Trial registration

Clinicaltrials.gov: NCT01774149

Automated glycemic pattern analysis: overcoming diabetes clinical inertia

The OneTouch® Verio™ IQ Meter with PatternAlert™ Technology has been approved by the U.S. Food and Drug Administration as the first self-glucose monitor that can automatically determine glycemic patterns [high and low pre-meal blood glucose (BG)] for health care providers (HCPs) and patients. In this issue of Journal of Diabetes Science and Technology, Katz and coauthors demonstrate that this device was more accurate and quicker in detecting abnormal glucose patterns than the review by HCPs of 30-day handwritten BG logs and that its interpretations were positively accepted by the HCPs. Continued development of automated pattern analysis and decision-support software to overcome the "data-overload" associated with intensive glucose monitoring and diabetes management will reduce clinical inertia and could dramatically improve diabetes outcomes.

Characterizing blood glucose variability using new metrics with continuous glucose monitoring data

OBJECTIVE

Glycemic variability contributes to oxidative stress, which has been linked to the pathogenesis of the long-term complications of diabetes. Currently, the best metric for assessing glycemic variability is mean amplitude of glycemic excursion (MAGE); however, MAGE is not in routine clinical use. A glycemic variability metric in routine clinical use could potentially be an important measure of overall glucose control and a predictor of diabetes complication risk not detected by glycosylated hemoglobin (A1C) levels. This study aimed to develop and evaluate new automated metrics of glycemic variability that could be routinely applied to continuous glucose monitoring (CGM) data to assess and enhance glucose control.

METHOD

Individual 24 h CGM tracings from our clinical diabetes research database were scored for MAGE and two additional metrics designed to compensate for aspects of variability not captured by MAGE: (1) number of daily glucose fluctuations >75 mg/dl that leave the normal range (70-175 mg/dl), or excursion frequency, and (2) total daily fluctuation, or distance traveled. These scores were used to train machine learning algorithms to recognize excessive variability based on physician ratings of daily CGM charts, producing a third metric of glycemic variability: perceived variability. Finger stick A1C (average) and serum 1,5-anhydroglucitol (postprandial) levels were used as clinical markers of overall glucose control for comparison.

RESULTS

Mean amplitude of glycemic excursion, excursion frequency, and distance traveled did not adequately quantify the glycemic variability visualized by physicians who evaluated the daily CGM plots. A naive Bayes classifier was developed that characterizes CGM tracings based on physician interpretations of tracings. Preliminary results suggest that the number of excessively variable days, as determined by this naive Bayes classifier, may be an effective way to automatically assess glycemic variability of CGM data. This metric more closely reflects 90-day changes in serum 1,5-anhydroglucitol levels than does MAGE.

CONCLUSION

We have developed a new automated metric to assess overall glycemic variability in people with diabetes using CGM, which could easily be incorporated into commercially available CGM software. Additional work to validate and refine this metric is underway. Future studies are planned to correlate the metric with both urinary 8-iso-prostaglandin F2 alpha excretion and serum 1,5-anhydroglucitol levels to see how well it identifies patients with high glycemic variability and increased markers of oxidative stress to assess risk for long-term complications of diabetes.