The function mad produces PGS values in a tibble object.
pgs(data, dur_length = 20, end_length = 30)A tibble object with two columns: subject id and corresponding PGS value.
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.
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.
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.
Elizabeth Chun
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.
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, tools:::Rd_expr_doi("10.1089/dia.2017.0080").
episode_calculation()
data(example_data_1_subject)
pgs(example_data_1_subject)
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