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Tytuł pozycji:

A Mathematical Formula to Determine the Minimum Continuous Glucose Monitoring Duration to Assess Time-in-ranges: Sensitivity Analysis Over the Parameters.

Tytuł:
A Mathematical Formula to Determine the Minimum Continuous Glucose Monitoring Duration to Assess Time-in-ranges: Sensitivity Analysis Over the Parameters.
Autorzy:
Camerlingo N
Vettoretti M
Sparacino G
Facchinetti A
Mader JK
Choudhary P
Del Favero S
Źródło:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 1435-1438.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
Język:
English
Imprint Name(s):
Original Publication: [Piscataway, NJ] : [IEEE], [2007]-
MeSH Terms:
Blood Glucose Self-Monitoring*
Diabetes Mellitus, Type 1*
Blood Glucose ; Glycemic Control ; Humans ; Time Factors
Substance Nomenclature:
0 (Blood Glucose)
Entry Date(s):
Date Created: 20211211 Date Completed: 20211229 Latest Revision: 20211229
Update Code:
20240105
DOI:
10.1109/EMBC46164.2021.9630689
PMID:
34891555
Czasopismo naukowe
In diabetes management, the fraction of time spent with glucose concentration within the physiological range of [70-180] mg/dL, namely time in range (TIR) is often computed by clinicians to assess glycemic control using a continuous glucose monitoring sensor. However, a sufficiently long monitoring period is required to reliably estimate this index. A mathematical equation derived by our group provides the minimum trial duration granting a desired uncertainty around the estimated TIR. The equation involves two parameters, p r and α, related to the population under analysis, which should be set based on the clinician's experience. In this work, we evaluated the sensitivity of the formula to the parameters.Considering two independent datasets, we predicted the uncertainty of TIR estimate for a population, using the parameters of the formula estimated for a different population. We also stressed the robustness of the formula by testing wider ranges of parameters, thus assessing the impact of large errors in the parameters' estimates.Plausible errors on the α estimate impact very slightly on the prediction (relative discrepancy < 5%), thus we suggest using a fixed value for α independently on the population being analyzed. Instead, p r should be adjusted to the TIR expected in the population, considering that errors around 20% result in a relative discrepancy of ~10%.In conclusion, the proposed formula is sufficiently robust to parameters setting and can be used by investigators to determine a suitable duration of the study.

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