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Garg SK, Liljenquist D, Bode B, Christiansen MP, Bailey TS, Brazg RL, Denham DS, Chang AR, Akturk HK, Dehennis A, Tweden KS, Kaufman FR. Evaluation of Accuracy and Safety of the Next-Generation Up to 180-Day Long-Term Implantable Eversense Continuous Glucose Monitoring System: The PROMISE Study. Diabetes Technol Ther 2022; 24:84-92. [PMID: 34515521 PMCID: PMC8817689 DOI: 10.1089/dia.2021.0182] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Background: Use of continuous glucose monitoring (CGM) systems is being rapidly adopted as standard of care for insulin-requiring patients with diabetes. The PROMISE study (NCT03808376) evaluated the accuracy and safety of the next-generation implantable Eversense CGM system for up to 180 days. Methods: This was a prospective multicenter study involving 181 subjects with diabetes at 8 USA sites. All subjects were inserted with a primary sensor. Ninety-six subjects had a second sensor, either an identical sensor or a modified sensor (sacrificial boronic acid [SBA]), inserted in their other arm (53 and 43 subjects, respectively). Accuracy was evaluated by comparing CGM to YSI 2300 glucose analyzer (Yellow Springs Instrument [YSI]) values during 10 clinic visits (day 1-180). Confirmed event detection rates, calibration stability, sensor survival, and serious adverse events (SAEs) were evaluated. Results: For primary sensors, the percent CGM readings within 20%/20% of YSI values was 92.9%; overall mean absolute relative difference (MARD) was 9.1%. The confirmed alert detection rate at 70 mg/dL was 93% and at 180 mg/dL was 99%. The median percentage of time for one calibration per day was 56%. Sixty-five percent of the primary sensors survived to 180 days. For the SBA sensors, the percent CGM readings within 20%/20% of YSI values was 93.9%; overall MARD was 8.5%. The confirmed alert detection rate at 70 mg/dL was 94% and at 180 mg/dL was 99%. The median percentage of time for one calibration per day was 63%. Ninety percent of the SBA sensors survived to 180 days. No device- or insertion/removal procedure-related SAEs were reported. Conclusion: These data show the next-generation Eversense CGM system had sustained accuracy and safety up to 180 days, with an improved calibration scheme and survival, using the primary or SBA sensors.
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Affiliation(s)
- Satish K. Garg
- University of Colorado, Aurora, Colorado, USA
- Address correspondence to: Satish K. Garg, MD, Editor-in-Chief, Diabetes Tech. & Therap., Professor of Medicine and Pediatrics, Garg Endowed Chairs & Director Adult Program, Barbara Davis Center for Diabetes, University of Colorado Denver, 1775 Aurora Court, Room M20-1323 Aurora, CO 80045, USA
| | | | - Bruce Bode
- Atlanta Diabetes Associates, Atlanta, Georgia, USA
| | | | | | | | | | | | | | | | - Katherine S. Tweden
- Senseonics, Inc., Germantown, Maryland, USA
- Address correspondence to: Katherine S. Tweden, PhD, Senseonics, Inc., 20451 Seneca Meadows Pkwy, Germantown, MD 20876, USA
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Abstract
Eversense is a long-term implantable continuous glucose monitoring (CGM) system. Difficulty in locating and removing implanted sensors is one of the issues limiting its use. We propose using near-infrared (NIR) light to locate implanted glucose sensors. NIR light was used to locate implanted glucose sensors in 30 patients (age 18-62 years) with type 1 diabetes who use Eversense sensors for CGM. Duration of sensor implantation was 10-40 weeks (median 12 weeks). Out of 30 patients with type 1 diabetes, the NIR light located the implanted sensor in 24 patients (success rate 80%) within 5 s. Sensor implantation at the skin with tattoos and excessive freckles were two main reasons for failure to locate sensors using NIR. We report an innovative method to locate implanted glucose sensors in seconds, which would reduce the time significantly to remove the sensor.
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Affiliation(s)
- Halis Kaan Akturk
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado
| | - Scott Brackett
- Barbara Davis Center for Diabetes, University of Colorado, Aurora, Colorado
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