igc: Calculate Index of Glycemic Control

In irinagain/iglu: Interpreting Glucose Data from Continuous Glucose Monitors

Calculate Index of Glycemic Control

Description

The function igc produces IGC values in a tibble object.

Usage

igc(data, LLTR = 80, ULTR = 140, a = 1.1, b = 2, c = 30, d = 30)

Arguments

data	DataFrame object with column names "id", "time", and "gl", or numeric vector of glucose values.
LLTR	Lower Limit of Target Range, default value is 80 mg/dL.
ULTR	Upper Limit of Target Range, default value is 140 mg/dL.
a	Exponent, generally in the range from 1.0 to 2.0, default value is 1.1.
b	Exponent, generally in the range from 1.0 to 2.0, default value is 2.
c	Scaling factor, to display Hyperglycemia Index, Hypoglycemia Index, and IGC on approximately the same numerical range as measurements of HBGI, LBGI and GRADE, default value is 30.
d	Scaling factor,to display Hyperglycemia Index, Hypoglycemia Index, and IGC on approximately the same numerical range as measurements of HBGI, LBGI and GRADE, default value is 30.

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the IGC values is returned.

IGC is calculated by taking the sum of the Hyperglycemia Index and the Hypoglycemia index. See hypo_index and hyper_index.

Value

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

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1089/dia.2008.0132")}.

Examples

data(example_data_1_subject)
igc(example_data_1_subject)
igc(example_data_1_subject, ULTR = 160)

data(example_data_5_subject)
igc(example_data_5_subject)
igc(example_data_5_subject, LLTR = 75, ULTR = 150)

irinagain/iglu documentation built on April 15, 2024, 4:20 p.m.