In 2024, detailed outcomes of the pivotal study for the, at the time, novel continuous glucose monitoring (CGM) solution Accu-Chek SmartGuide (Roche Diabetes Care GmbH, Mannheim, Germany) were published. 1 The CGM system received a CE mark in the same year for marketing in the European Economic Area. However, the development of a CGM system does not end with the initial marketed product and warrants continued research on improving the hardware and software, eg, better algorithms, in the course of post-market surveillance activities. Optimizing the performance of CGM systems by improving their algorithms is critical for enhancing accuracy, reliability, and clinical utility. Actually, improved algorithms may have the ability to yield more accurate CGM readings, 2 minimize false alarms, and increase the proportion of time in range/time in tight range (TIR/TITR) readings, thereby supporting more precise insulin dosing and timely intervention.
More and more published clinical opinions and/or consensus statements emphasize that transparency in algorithmic methods, calibration alignment, and standardized evaluation procedures are essential to ensure reliable interpretation and global comparability of CGM technologies.3,4 Taken together, algorithm optimization is fundamental to maximizing the clinical impact of CGM systems, improving user safety, reducing complications, and advancing integration into comprehensive diabetes care strategies.
Continuous glucose monitoring raw signals from the CGM solution’s pivotal study 1 were reanalyzed with an improved algorithm. The new algorithm retains the manual calibration scheme, and features more efficient noise reduction, resulting in improved CGM accuracy. The detailed results of this reanalysis are presented in Table 1 similar to the earlier results (Table 2 in Mader et al 1 ). The retrospective application to our existing data set allowed for direct comparison and validation of the improvement. The CGM system’s accuracy was markedly improved, eg, the primary endpoint (20/20 agreement rate [AR]) improved from 90.5% to 92.0% in the in-clinic data set, and mean absolute relative difference (MARD) across the CGM measuring range improved from 9.2% to 8.6%. When evaluated for a future factory-calibrated configuration requiring no manual calibrations, this identical signal processing yielded a 20/20 AR of 89.8% and MARD of 9.7% across the measuring range. Consensus error grid analysis indicated similar results to those of the original algorithm with 99.7% in zones A and B and the remaining 0.3% in zone C for the factory-calibrated configuration and 99.8% of results in zones A and B and the remaining 0.2% in zone C for the manually calibrated configuration. Sensor survival at 14 days improved slightly from 82.4% to 83.8%, and data recovery rate markedly rose from 92.7% ± 3.7% to 98.2% ± 3.7%.
Table 1.
Accuracy Results for In-Clinic Sessions for the Factory-Calibrated and Manually Calibrated CGM Solution’s Configurations.
| Comparator BG range (mg/dL) | N (%) | 15/15
a
AR (%) |
20/20
a
AR (%) |
30/30
a
AR (%) |
40/40
a
AR (%) |
MARD (%) |
MAD (mg/dL) |
Bias (%) |
|---|---|---|---|---|---|---|---|---|
| In-clinic (factory-calibrated configuration) | ||||||||
| 40-400 | 16 480 (100%) | 82.7 | 89.8 | 96.3 | 98.4 | 9.7 | – | −2.9 |
| <54 | 99 (1%) | 80.8 | 89.9 | 97.0 | 100.0 | – | 9.1 | +17.7 |
| <70 | 1186 (7%) | 87.7 | 94.9 | 99.1 | 99.8 | – | 7.8 | +6.7 |
| 70-180 | 10 067 (61%) | 80.4 | 88.0 | 95.8 | 98.3 | 10.1 | – | −3.7 |
| >180 | 5227 (32%) | 85.9 | 92.1 | 96.7 | 98.4 | 8.4 | – | −3.7 |
| In-clinic (manually calibrated configuration) | ||||||||
| 40-400 | 16 482 (100%) | 85.5 | 92.0 | 97.6 | 99.0 | 8.6 | – | −1.2 |
| <54 | 102 (1%) | 79.4 | 90.2 | 97.1 | 99.0 | – | 9.0 | +16.7 |
| <70 | 1196 (7%) | 87.6 | 94.9 | 99.2 | 99.7 | – | 7.9 | +8.1 |
| 70-180 | 10 087 (61%) | 83.9 | 91.0 | 97.3 | 98.9 | 8.8 | – | −2.0 |
| >180 | 5199 (32%) | 88.2 | 93.3 | 97.9 | 99.0 | 7.3 | – | −1.7 |
For BG values <100 mg/dL, absolute differences between BG and CGM values were calculated; for BG values ≥100 mg/dL, relative differences were calculated.
In conclusion, the reported algorithmic improvement represents a meaningful step toward optimizing the Accu-Chek SmartGuide CGM solution’s performance. Enhanced CGM performance has important clinical implications, as more precise glucose measurements enable better-informed therapeutic decisions and may improve glycemic control. We believe this update will be of interest to readers of the original work 1 and to the broader diabetes technology community.
Julia K. Mader, MD
Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Austria
Delia Waldenmaier, PhD
Institut für Diabetes-Technologie Ulm GmbH, Germany
Wiebke Mueller-Hoffmann, PhD
Roche Diabetes Care GmbH, Mannheim, Germany
Katrin Mueller, PhD
Roche Diabetes Care GmbH, Mannheim, Germany
Michael Angstmann, PhD
Roche Diabetes Care GmbH, Mannheim, Germany
Gerhard Vogt, PhD
Roche Diabetes Care GmbH, Mannheim, Germany
Cosima C. Rieger, PhD
Roche Diabetes Care GmbH, Mannheim, Germany
Manuel Eichenlaub, PhD
Institut für Diabetes-Technologie Ulm GmbH, Germany
Thomas Forst, MD
hVIVO Germany GmbH, Mannheim, Germany
Guido Freckmann, MD
Institut für Diabetes-Technologie Ulm GmbH, Germany
Acknowledgments
Accu-Chek and Accu-Chek SmartGuide are trademarks of Roche.
Footnotes
Abbreviations: CGM, continuous glucose monitoring; TIR, time in range; TITR, time in tight range.
ORCID iDs: Julia K. Mader 
https://orcid.org/0000-0001-7854-4233
Delia Waldenmaier
https://orcid.org/0000-0003-3280-2369
Wiebke Mueller-Hoffmann
https://orcid.org/0000-0001-5795-046X
Manuel Eichenlaub
https://orcid.org/0000-0003-2150-3160
Guido Freckmann
https://orcid.org/0000-0002-0406-9529
Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by Roche Diagnostics International Ltd, Switzerland.
The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: JKM is a member of the advisory boards of Abbott Diabetes Care, Becton-Dickinson, Biomea Fusion, Dexcom, Eli Lilly, Embecta, Insulet, Medtronic, Novo Nordisk A/S, Pharmasens, Roche Diabetes Care, Sanofi-Aventis, and Tandem. She has received speaker honoraria from A. Menarini Diagnostics, Abbott Diabetes Care, Buzud, Dexcom, Embecta, Eli Lilly, Novo Nordisk A/S, Roche Diabetes Care, Sanofi, Sinocare, Viatris, Vitalaire, and Ypsomed. She is also a shareholder of decide Clinical Software GmbH and elyte Diagnostics and serves as CMO of elyte Diagnostics. GF is general manager and medical director of the IfDT (Institut für Diabetes-Technologie Ulm GmbH, Ulm, Germany), which carries out clinical studies, eg, with medical devices for diabetes therapy, on its own initiative and on behalf of various companies. GF/IfDT have received speakers’ honoraria or consulting fees in the last 3 years from Abbott, Ascensia, Bionime, Boydsense, Dexcom, Glucoset, Insulet, I-Sens, Lilly, Menarini, Perfood, Pharmasens, Roche, Sinocare, Vertex, and Ypsomed. ME and DW are employees of IfDT. WM-H, KM, MA, GV, and CR are employees of Roche Diabetes Care GmbH, Mannheim, Germany. TF is Chief Medical Officer at hVIVO Germany. TF received speaker’s honoraria or consulting fees in the last 3 years from Amarin, AstraZeneca, Bayer, Böhringer Ingelheim, Cipla, Daiichi-Sankyo, derCampus, Diabetes-Akademie Bad Mergentheim, Eli Lilly, Fortbildungskolleg, MSD, Novo Nordisk, Roche Diagnostics, Sanofi, Santis, and Sciarc.
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