Glucose monitoring intelligent tracking system for remote glycemic assessment in diabetic dogs: a novel approach

Abstract

Optimizing glucose control is one of the primary goals of diabetes management. This study assessed the feasibility and accuracy of a remote real-time continuous glucose monitoring system (RT-CGMS) integrated with intelligent tracking in diabetic dogs. Seven Beagle dogs were monitored using interstitial sensors across different configurations: adhesive only, adhesive with protective garments, and garments combined with an innovative glucose monitoring approach for remote transmission. Sensor wear time was slightly longer with garments (8.2 ± 6.7 vs. 5.8 ± 3.1 days; P > 0.05). Valid data acquisition was significantly higher in the remote-monitoring group [95 (84, 96)] compared to Group 1 [67 (47, 78)] and Group 2 [76 (64, 80), P < 0.001 for both]. A strong correlation was found between RT-CGMS and PBGM measurements (r = 0.904). Calibration improved accuracy at glucose levels ≥ 5.5 mmol/L, reducing MARD from 28.5 to 14.5% and increasing Bland-Altman agreement from 48 to 67%. However, MAD slightly increased in the < 5.5 mmol/L range (2.2 to 2.7 mmol/L). Frequent hyperglycemia, high variability, and glucose excursions were observed. In conclusion, RT-CGMS with intelligent tracking improved data continuity and accuracy in diabetic dogs. Future research should focus on improving the system's sensitivity under hypoglycemic conditions and exploring its broader applications, including its role in enhancing in-hospital glucose management, utilizing big data to facilitate online diagnostics and offline follow-up care, providing guidance for daily glucose stabilization, enabling personalized veterinary services, and offering subscription-based health reports for pet owners.

Keywords: Analytical accuracy; Blood glucose fluctuations; Diabetic dog models; Intelligent tracking technology; Real-time continuous glucose monitoring (RT-CGMS).

Fig. 1

Automatic Glucose Management System for Diabetic Dogs. Composition and deployment of the continuous glucose monitoring system in a diabetic canine model. (a) Placement of the FreeStyle Libre sensor and Osense™ Air device on the dorsal mid-shoulder region following site preparation. (b) Sensor fixation enhanced with custom-designed protective garments made from durable, tear-resistant materials to improve wearability. (c) Schematic of the iVT-GMS, illustrating real-time data transmission from the NFC-enabled Osense™ Air device via Bluetooth to a smart gateway, with optional PoE-based wired network integration for multi-platform glucose data visualization. iVT-GMS, Intelligent Vitals Tracking–Glucose Management System; NFC, near-field communication; PoE, power over Ethernet

Fig. 2

Correlation between interstitial glucose measured by the remote RT-CGMS and blood glucose measured by PBGM in diabetic dogs (P < 0.001). r, correlation coefficient; RT-CGMS, real-time continuous glucose monitoring; PBGM, portable blood glucose meters

Fig. 3

Bland–Altman plots comparing interstitial glucose values measured by remote RT-CGMS (before and after calibration) with reference blood glucose values obtained using PBGM (Accu-Chek). (a) Differences prior to calibration. (b) Differences following calibration. Each point represents a paired glucose measurement (n = 46). Different colors correspond to different RT-CGMS sensors used during the study. The central solid black line indicates the mean bias, while the dashed black lines represent ISO 15197:2013 accuracy limits: ±0.83 mmol/L for glucose concentrations below 5.5 mmol/L and ± 15% for concentrations at or above 5.5 mmol/L. Percentages represent the proportion of samples falling within these limits (central % value). PBGM, portable blood glucose meters; RT-CGMS, real-time continuous glucose monitoring

Fig. 4

Parkes Consensus EGA illustrating the clinical risk distribution of glucose values measured by remote RT-CGMS compared to PBGM in diabetic dogs. Reference glucose values (PBGM) are plotted on the x-axis, and corresponding interstitial glucose values from RT-CGMS are plotted on the y-axis. The different zones represent the magnitude of clinical risk: no effect on clinical action (Zone A); altered clinical action with little or no effect on the clinical outcome (Zone B); altered clinical action likely to affect the clinical outcome (Zone C); altered clinical action that could pose a significant medical risk (Zone D); and altered clinical action that could have dangerous consequences (Zone E). EGA, error grid analysis; PBGM, portable blood glucose meters; RT-CGMS, real-time continuous glucose monitoring system

Fig. 5

CGM report displaying blood glucose trends before and after calibration, accessible via a computer or smartphone. Green lines represent glucose values before calibration; blue lines represent values after calibration; red dots indicate PBGM reference values used for calibration. CGM, continuous glucose monitoring; PBGM, portable blood glucose meters