Evaluation of the Utility of a Glycemic Pattern Identification System

Abstract

For insulin-dependent diabetes patients and their care providers, the ability to quickly and efficiently identify deleterious patterns in blood glucose (BG) data can have a significant impact on improving diabetes management outcomes and quality of life.1,2 Standard tools for aiding in identifying patterns typically involve (1) paper log books or (2) software on a desktop computer, BG meter, or other handheld device. In the case of software, data logs may be accompanied by weekly averages or point charts, or more complex analytical tools in the form of modal days, percentage of readings in and out of a target range, or averages by diurnal time period. 2,3These methods have several shortcomings, including (1) the limited characterization of the utility of the information provided and (2) the significant time and mental workload required to extract distinct patterns or other actionable information. [3][4][5] With the increasing prevalence of BG meters with advanced processing and display functionality, and technologies that transmit BG data to smartphone or cloud based systems in real time, there is an opportunity to develop an intelligent system that can overcome the above shortcomings.There is support for the management of diurnal BG patterns with behavioral or treatment related interventions. 2,3,6,7 We are evaluating a novel system named Vigilant™ (investigational use software device from InSpark Technologies, Inc, Charlottesville, VA) that is designed to message users about diurnal patterns in upcoming daily time periods when the patient is testing, and to optimize the frequency of pattern messaging to within a desirable range. Patterns of hyperglycemia and hypoglycemia are identified if the product of the following exceeds a predetermined threshold: (1) the percentage of BG readings in a diurnal time period exceeding a clinical threshold and (2) the probability that the identified diurnal BG pattern is deviant relative to the rest of the patient's BG levels. This method, hereinafter referred to as "method 1," has been described in detail in a prior publication. We evaluated the utility of pattern identification with a novel pattern identification system named Vigilant™ and compared it to standard pattern identification methods in diabetes.Method: To characterize the importance of an identified pattern we evaluated the relative risk of future hypoglycemic and hyperglycemic events in diurnal periods following identification of a pattern in a data set of 536 patients with diabetes. We evaluated events 2 days, 7 days, 30 days, and 61-90 days from pattern identification, across diabetes types and cohorts of glycemic control, and also compared the system to 6 pattern identification methods consisting of deleterious event counts and percentages over 5-, 14-, and 30-day windows.Results: Episodes of hypoglycemia, hyperglycemia, severe hypoglycemia, and severe hyperglycemia were 120%, 46%, 123%, and 76% more likely after pattern identification, respectively, compared to periods when no pattern was identified. The syst...

The role of glycemic control in the development of late complications of diabetes mellitus

“…Так, в приложении имеется функция «Мои тенденции», которая позволяет определить тренды уровня глюкозы в крови и уведомляет пациентов о возможных причинах. Благодаря этому пациенты лучше понимают свое состояние и могут принимать более осознанные решения о питании, физических нагрузках и других аспектах образа жизни [22,23]. Приложение позволяет получать предупреждения, когда уровень глюкозы в крови достигает критически высоких или низких значений, и устанавливать напоминания.…”

Artificial intelligence in diabetes management: Advancements, opportunities, and challenges

“…These studies utilized various types of input data, including electroencephalogram, electrocardiogram, self-monitoring BG (SMBG), and electronic health records (EHRs). 74 , 75 , 76 , 77 , 78 For detecting hypoglycemia, Kodama et al. summarized that the pooled estimates of sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were 0.79, 0.80, 8.05, and 0.18, respectively.…”

Glycemic control as an important tool in the prevention of cardiovascular aging in patients with diabetes mellitus

“…Многие пациенты ограничиваются только определением уровня глюкозы крови по глюкометру, так как не понимают, что визуальный структурированный анализ данных является необходимым инструментом для достижения адекватного гликемического контроля и профилактики осложнений СД. Кроме того, нежелание вести дневник самоконтроля часто объясняется пациентами нехваткой времени и неумением проводить интерпретацию полученных сведений [40,41].…”