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The function hypo_index produces Hypoglycemia index values in a tibble object.

Usage

hypo_index(data, LLTR = 80, b = 2, 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.

b

Exponent, generally in the range from 1.0 to 2.0, default value is 2.

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.

Value

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

Details

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

Hypoglycemia Index is calculated by \(\frac{1}{n \cdot d} \sum \left(ULTR - hyperBG_j \right)^b\). Here n is the total number of Glucose measurements (excluding NA values), and \(hypoBG_j\) is the jth Glucose measurement below the LLTR cutoff, b is an exponent, and d 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, doi:10.1089/dia.2008.0132 .

Examples

data(example_data_1_subject)
hypo_index(example_data_1_subject, LLTR = 60)
#> # A tibble: 1 × 2
#>   id        hypo_index
#>   <fct>          <dbl>
#> 1 Subject 1          0

data(example_data_5_subject)
hypo_index(example_data_5_subject)
#> # A tibble: 5 × 2
#>   id        hypo_index
#>   <fct>          <dbl>
#> 1 Subject 1     0.0103
#> 2 Subject 2     0     
#> 3 Subject 3     0.0378
#> 4 Subject 4     0.0437
#> 5 Subject 5     0.0120
hypo_index(example_data_5_subject, LLTR = 70)
#> # A tibble: 5 × 2
#>   id        hypo_index
#>   <fct>          <dbl>
#> 1 Subject 1   0.000480
#> 2 Subject 2   0       
#> 3 Subject 3   0.00626 
#> 4 Subject 4   0.00978 
#> 5 Subject 5   0.000387