Background: Frequent blood glucose readings are the most cumbersome aspect of diabetes treatment for many patients. The noninvasive TensorTip Combo Glucometer (CoG) component employs dedicated mathematical algorithms to analyze the collected signal and to predict tissue glucose at the fingertip. This study presents the performance of the CoG (the invasive and the noninvasive components) during a standardized meal experiment.
Methods: Each of the 36 participants (18 females and males each, age: 49 ± 18 years, 14 healthy subjects, 6 type 1 and 16 type 2 patients) received a device for conducting calibration at home. Thereafter, they ingested a standardized meal. Blood glucose was assessed from capillary blood samples by means of the (non)invasive device, YSI Stat 2300 plus, Contour Next at time points -30, 0, 15, 30, 45, 60, 75, 90, 120, 150, and 180 minutes. Statistical analysis was performed by consensus error grid (CEG) and calculation of mean absolute relative difference (MARD) in comparison to YSI.
Results: For the noninvasive (NI) CoG technology, 100% of the data pairs were found in CEG zones A (96.6%) and B (3.4%); 100% were seen in zone A for the invasive component and Contour Next. MARD was calculated to be 4.2% for Contour Next, 9.2% for the invasive component, and 14.4% for the NI component.
Conclusions: After appropriate individual calibration of the NI technology, both the NI and the invasive CoG components reliably tracked tissue and blood glucose values, respectively. This may enable patients with diabetes to monitor their glucose levels frequently, reliably, and most of all pain-free.
Keywords: chaos theory; color sensor imaging; fingertip tissue; invasive device component; noninvasive glucose prediction.