cgmquantify: Analyzing Glucose and Glucose Variability
Continuous glucose monitoring (CGM) systems provide real-time,
dynamic glucose information by tracking interstitial glucose values
throughout the day. Glycemic variability, also known as glucose variability,
is an established risk factor for hypoglycemia (Kovatchev) and
has been shown to be a risk factor in diabetes complications.
Over 20 metrics of glycemic variability have been identified.
Here, we provide functions to calculate glucose summary metrics,
glucose variability metrics (as defined in clinical publications),
and visualizations to visualize trends in CGM data.
Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract>
American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc>
Kovatchev B (2019) <doi:10.1177/1932296819826111>
Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3>
Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017>
Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 2.10) |
| Imports: |
dplyr, tidyverse, ggplot2, hms, stats, magrittr |
| Suggests: |
testthat (≥ 2.0.0), knitr, rmarkdown |
| Published: |
2021-02-05 |
| DOI: |
10.32614/CRAN.package.cgmquantify |
| Author: |
Maria Henriquez [aut, com, cph, cre, trl],
Brinnae Bent [aut, cph, dtc] |
| Maintainer: |
Maria Henriquez <marhenriq at gmail.com> |
| License: |
MIT License + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
cgmquantify results |
Documentation:
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