iglu (version 4.2.2)

hyper_index: Calculate Hyperglycemia Index

Description

The function hyper_index produces Hyperglycemia Index values in a tibble object.

Usage

hyper_index(data, ULTR = 140, a = 1.1, c = 30)

Value

If a data.frame object is passed, then a tibble object with two columns: subject id and corresponding Hyperglycemia Index value is returned. If a vector of glucose values is passed, then a tibble object with just the Hyperglycemia Index value is returned. as.numeric() can be wrapped around the latter to output just a numeric value.

Arguments

data

DataFrame object with column names "id", "time", and "gl", or numeric vector of glucose values.

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.

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.

Details

A tibble object with 1 row for each subject, a column for subject id and a column for the Hyperglycemia Index values is returned. NA glucose values are omitted from the calculation of the Hyperglycemia Index values.

Hyperglycemia Index is calculated by \(\frac{1}{n \cdot c} \sum \left(hyperBG_j - ULTR \right)^a\). Here n is the total number of Glucose measurements (excluding NA values), \(hyperBG_j\) is the jth Glucose measurement above the ULTR cutoff, a is an exponent, and c is a scaling factor.

References

Rodbard (2009) Interpretation of continuous glucose monitoring data: glycemic variability and quality of glycemic control, Diabetes Technology and Therapeutics 11 .55-67, tools:::Rd_expr_doi("10.1089/dia.2008.0132").

Examples

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

data(example_data_5_subject)
hyper_index(example_data_5_subject)
hyper_index(example_data_5_subject, ULTR = 150)