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Analytical Evaluation of PPG Blood Glucose Monitoring System - researcher clinical trial

PPG 혈당 모니터링 시스템의 분석적 평가 - 연구자 임상

  • 박철구 ((주)소프트웨어융합연구소) ;
  • 최상기 ((주)소프트웨어융합연구소) ;
  • 조성근 ((주)소프트웨어융합연구소) ;
  • 김권민 ((주)소프트웨어융합연구소)
  • Received : 2023.11.16
  • Accepted : 2023.12.20
  • Published : 2023.12.28

Abstract

This study is a performance evaluation of a blood sugar monitoring system that combines a PPG sensor, which is an evaluation device for blood glucose monitoring, and a DNN algorithm when monitoring capillary blood glucose. The study is a researcher-led clinical trial conducted on participants from September 2023 to November 2023. PPG-BGMS compared predicted blood sugar levels for evaluation using 1-minute heart rate and heart rate variability information and the DNN prediction algorithm with capillary blood glucose levels measured with a blood glucose meter of the standard personal blood sugar management system. Of the 100 participants, 50 had type 2 diabetes (T2DM), and the average age was 67 years (range, 28 to 89 years). It was found that 100% of the predicted blood sugar level of PPG-BGMS was distributed in the A+B area of the Clarke error grid and Parker(Consensus) error grid. The MARD value of PPG-BGMS predicted blood glucose is 5.3 ± 4.0%. Consequentially, the non-blood-based PPG-BGMS was found to be non-inferior to the instantaneous blood sugar level of the clinical standard blood-based personal blood glucose measurement system.

본 연구는 모세관 혈당의 혈당값을 대조군으로 연구 참가자의 혈액 포도당을 모니티링할 때 PPG 센서와 DNN 예측알고리즘이 융합된 혈당모니터링 시스템(PPG-BGMS)의 성능을 평가하는 것이다. 연구는 2023년 9월부터 2023년 11월까지 참가자를 대상으로 실시된 연구자 임상시험이다. PPG-BGMS는 1분간의 심박수, 심박변이도 정보와 DNN 예측알고리즘을 활용한 예측된 혈당수치와 개인용혈당관리시스템의 혈당측정기로 측정한 모세관혈당 수치와 비교했다. 총 100명의 참가자 중 제2형 당뇨(T2DM) 유병인은 50명이며, 평균연령은 67세(28세~89세)이다. PPG-BGMS의 예측혈당의 100%가 Clarke 오류그리드 및 Parker(Consensus) 오류그리드의 A+B 영역에 분포하는 것으로 나타났다. PPG-BGMS 예측 혈당의 MARD 값은 5.3 ± 4.0 %이다. 결과에 의하면 비채혈식 PPG-BGMS는 임상표준의 채혈식 개인용 혈당측정시스템의 순간 혈당수치와 비교하여 열등하지 않는 것으로 분석되었다.

Keywords

References

  1. Y. Bao et al. ... Chinese Diabetes Society (2019). Chinese clinical guidelines for continuous glucose monitoring (2018 edition). Diabetes/metabolism research and reviews, 35(6), e3152. DOI : 10.1002/dmrr.3152
  2. D. Bruttomesso et al ...of the Italian Diabetes Society(SID). (2019). The use of real time continuous glucose monitoring or flash glucose monitoring in the management of diabetes: A consensus view of Italian diabetes experts using the Delphi method. Nutrition, metabolism, and cardiovascular diseases : NMCD, 29(5), 421-431. DOI : 10.1016/j.numecd.2019.01.018
  3. American Diabetes Association. (2009). Diagnosis and classification of diabetes mellitus. Diabetes Care. Jan;32 Suppl 1(Suppl 1):S62-7. DOI : 10.2337/dc09-S062.
  4. S. Hassani Zadeh, P. Boffetta & M. Hosseinzadeh. (2020). Dietary patterns and risk of gestational diabetes mellitus: A systematic review and meta-analysis of cohort studies. Clinical nutrition ESPEN, 36, 1-9. DOI : 10.1016/j.clnesp.2020.02.009
  5. M. C. Petersen & G. I. Shulman. (2018). Mechanisms of Insulin Action and Insulin Resistance. Physiological reviews, 98(4), 2133-2223. DOI : 10.1152/physrev.00063.2017
  6. National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). (April 2023). Insulin Resistance & Prediabetes. https://www.niddk.nih.gov/health-information/diabetes/overview/what-isdiabetes/prediabetes-insulin-resistance.
  7. G. Wilcox. (2005). Insulin and insulin resistance. The Clinical biochemist. Reviews, 26(2), 19-39.
  8. A. K. Singh, R. Gupta, A. Ghosh & A. Misra. (2020). Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations. Diabetes & metabolic syndrome, 14(4), 303-310. DOI : 10.1016/j.dsx.2020.04.004
  9. W. L. Clarke. (2005). The original Clarke Error Grid Analysis (EGA). Diabetes technology & therapeutics, 7(5), 776-779. DOI : 10.1089/dia.2005.7.776
  10. W. L. Clarke, D. Cox, L. A. Gonder-Frederick, W. Carter & S. L. Pohl. (1987). Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes care, 10(5), 622-628. DOI : 10.2337/diacare.10.5.622
  11. S. Sengupta, A. Handoo, I. Haq, K. Dahiya, S. Mehta & M. Kaushik. (2022). Clarke Error Grid Analysis for Performance Evaluation of Glucometers in a Tertiary Care Referral Hospital. Indian journal of clinical biochemistry : IJCB, 37(2), 199-205. DOI : 10.1007/s12291-021-00971-4
  12. A. Pfutzner, D.C. Klonoff, S. Pardo & J. L. Parkes. (2013). Technical Aspects of the Parkes Error Grid. Journal of Diabetes Science and Technology, 7, 1275 - 1281.
  13. J. Zhou, S. Zhang, L. Li, Y. Wang, W. Lu, C. Sheng, Y. Li, Y. Bao & W. Jia. (2018). Performance of a new real-time continuous glucose monitoring system: A multicenter pilot study. Journal of diabetes investigation, 9(2), 286-293. DOI : 10.1111/jdi.12699
  14. Blood Glucose Monitoring System Surveillance Program. (2023). www.diabetestechnology.org/seg/
  15. H. Mondal & S. Mondal. (2020). Clarke Error Grid Analysis on Graph Paper and Microsoft Excel. Journal of diabetes science and technology, 14(2), 499. DOI : 10.1177/1932296819890875