pgs {iglu}

Calculate Personal Glycemic State (PGS)

Description

The function mad produces PGS values in a tibble object.

Usage

pgs(data, dur_length = 20, end_length = 30)

Arguments

data

DataFrame object with column names "id", "time", and "gl". Should only be data for 1 subject. In case multiple subject ids are detected, a warning is produced and only 1st subject is used.

dur_length

Numeric value specifying the minimum duration in minutes to be considered an episode. Note dur_length should be a multiple of the data recording interval otherwise the function will round up to the nearest multiple. Default is 20 minutes to match the original PGS definition.

end_length

Numeric value specifying the minimum duration in minutes of improved glycemia for an episode to end. Default is 30 minutes to match original PGS definition.

Details

A tibble object with 1 row for each subject, a column for subject id and a column for PGS values is returned. NA glucose values are omitted from the calculation. The formula for PGS is as follows, where GVP = glucose variability percentage, MG = mean glucose, PTIR = percent time in range, and N54, N70 are the number of hypoglycemic episodes per week in the ranges <54 mg/dL and 54 to <70 mg/dL level respectively.

PGS = f(GVP) + g(MG) + h(PTIR) + j(N54, N70)

The component functions are listed below.

\newline f(GVP) = 1 + \frac{9}{1+\exp(-0.049(GVP - 65.47))} \newline g(MG) = 1 + 9(\frac{1}{1+\exp(0.1139(MG - 72.08))} + \frac{1}{1+\exp(-0.09195(MG - 157.57))}) \newline h(PTIR) = 1+\frac{9}{1+\exp(0.0833(PTIR - 55.04))} \newline j(N54, N70) = a(N54) + b(N70) \newline a(N54) = 0.5+4.5(1-\exp(-0.91093N54)

and b(N70) is defined such that b(N70) = 0.5714N70 + 0.625 if N70 <= 7.65, and b(N70) = 5 otherwise.

Note that the duration thresholds for episodes are NOT the same as the episode_calculation defaults. The defaults chosen for PGS are those that match the original PGS paper definition, while the episode_calculation defaults match the consensus.

Value

A tibble object with two columns: subject id and corresponding PGS value.

Author(s)

Elizabeth Chun

References

Hirsch et al. (2017): A Simple Composite Metric for the Assessment of Glycemic Status from Continuous Glucose Monitoring Data: Implications for Clinical Practice and the Artificial Pancreas Diabetes Technol Ther 19(S3) .S38-S48, doi:10.1089/dia.2017.0080.

See Also

episode_calculation()

Examples

data(example_data_1_subject)
pgs(example_data_1_subject)