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. 2022 Jun 6;7(2):e27284.
doi: 10.2196/27284.

Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: Prospective Evaluation in Clinical Practice

David Scheinker  1   2   3   4 , Angela Gu  3 , Joshua Grossman  3 , Andrew Ward  3 , Oseas Ayerdi  3 , Daniel Miller  3 , Jeannine Leverenz  2 , Korey Hood  1   4 , Ming Yeh Lee  2 , David M Maahs  1   4   5 , Priya Prahalad  1   4
Affiliations

Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: Prospective Evaluation in Clinical Practice

David Scheinker et al. JMIR Diabetes. .

Abstract

Background: The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators.

Objective: This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool.

Methods: We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations.

Results: Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL).

Conclusions: TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.

Keywords: continuous glucose monitor; diabetes; personalized medicine; population health; telehealth.

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Conflict of interest statement

Conflicts of Interest: DM has research support from the National Institutes of Health, JDRF, National Science Foundation, and the Helmsley Charitable Trust, and his institution has research support from Medtronic, Dexcom, Insulet, Big Foot Biomedical, Tandem, and Roche. DM consulted Abbott, Helmsley Charitable Trust, Sanofi, Novo Nordisk, Eli Lilly, and Insulet. KH has research support from Dexcom, Inc, for investigator-initiated research and consultant fees from the Lilly Innovation Center; Lifescan Diabetes Institute; Insulet, Inc; and Roche Diagnostics. AW receives compensation from HealthPals, Inc. in the form of salary. AW was supported by the US Department of Defense through a National Defense Science and Engineering Graduate (NDSEG) Fellowship.

Figures

Figure 1
Figure 1
Initial and revised timely interventions for diabetes excellence (TIDE) interface. TBR: time below range; TIR: time in range.
Figure 2
Figure 2
Workflow illustrated with examples of weekly and monthly cadence of data collection, data transfer, and provider review. CDCES: certified diabetes care and education specialists; CGM: continuous glucose monitor; EMR: electronic medical record; TIDE: timely interventions for diabetes excellence.
Figure 3
Figure 3
Frequency of generic and personalized flags in external cohorts.
See this image and copyright information in PMC

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