Skip to contents

Calculates Hypo/Hyperglycemic episodes with summary statistics

Source: R/episode_calculation.R
episode_calculation.Rd

The function determines episodes or events, calculates summary statistics, and optionally returns data with episode label columns added

Usage

episode_calculation(
  data,
  lv1_hypo = 70,
  lv2_hypo = 54,
  lv1_hyper = 180,
  lv2_hyper = 250,
  dur_length = 15,
  end_length = 15,
  return_data = FALSE,
  dt0 = NULL,
  inter_gap = 45,
  tz = ""
)

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.

lv1_hypo

Numeric value specifying a hypoglycemia threshold for level 1

lv2_hypo

Numeric value specifying a hypoglycemia threshold for level 2

lv1_hyper

Numeric value specifying a hyperglycemia threshold for level 1

lv2_hyper

Numeric value specifying a hyperglycemia threshold for level 2

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 15 minutes to match consensus.

end_length

Numeric value specifying the minimum duration in minutes of improved glycemia for an episode to end. Default is equal to dur_length to match consensus.

return_data

Boolean indicating whether to also return data with episode labels. Defaults to FALSE which means only episode summary statistics will be returned

dt0

The time frequency for interpolation in minutes, the default will match the CGM meter's frequency (e.g. 5 min for Dexcom).

inter_gap

The maximum allowable gap (in minutes) for interpolation. The values will not be interpolated between the glucose measurements that are more than inter_gap minutes apart. The default value is 45 min.

tz

A character string specifying the time zone to be used. System-specific (see as.POSIXct), but " " is the current time zone, and "GMT" is UTC (Universal Time, Coordinated). Invalid values are most commonly treated as UTC, on some platforms with a warning.

Value

If return_data is FALSE, a single dataframe with columns:

id

Subject id

type

Type of episode - either hypoglycemia or hyperglycemia

level

Level of episode - one of lv1, lv2, extended, lv1_excl

avg_ep_per_day

Average number of episodes per day calculated as (total # episodes)/(recording time in days (24hrs))

avg_ep_duration

Average duration of episodes in minutes

avg_ep_gl

Average glucose in the episode in mg/dL

total_episodes

Total number of episodes in the subject's glucose trace

If return_data is TRUE, returns a list where the first entry is the episode summary dataframe (see above) and the second entry is the input data with episode labels added. Note the data returned here has been interpolated using the CGMS2DayByDay() function. Mostly for use with epicalc_profile function. Format of the second list entry is:

id

Subject id

time

Interpolated timestamps

gl

glucose in mg/dL

[episode_label]

One column per episode label - i.e. lv1_hypo, lv2_hypo, lv1_hyper, lv2_hyper, ext_hypo. 0 means not this type of episode, a positive integer label is assigned to each episode. Note the labels are *not* unique by subject only unique by segment

Details

We follow the definition of episodes given in the 2023 consensus by Battelino et al. Note we have classified lv2 as a subset of lv1 since we find the consensus to be slightly ambiguous. For lv1 exclusive of lv2, please see lv1_excl which summarises episodes that were exclusively lv1 and did not cross the lv2 threshold. Also note, hypo extended refers to episodes that are >120 consecutive minutes below lv1 hypo and ends with at least 15 minutes of normoglycemia. For more details on each category please see the reference below (Battelino et al 2023).

References

Battelino et al. (2023): Continuous glucose monitoring and metrics for clinical trials: an international consensus statement Lancet Diabetes & Endocrinology 11(1) .42-57, doi:10.1016/s2213-8587(22)00319-9 .

See also

epicalc_profile()

Author

Elizabeth Chun, Jung Hoon Seo, Johnathan Shih

Examples

episode_calculation(example_data_5_subject, lv1_hypo=100, lv1_hyper= 120)
#> # A tibble: 35 × 7
#>    id        type  level avg_ep_per_day avg_ep_duration avg_ep_gl total_episodes
#>    <fct>     <chr> <chr>          <dbl>           <dbl>     <dbl>          <dbl>
#>  1 Subject 1 hypo  lv1            2.88             130.      92.9             32
#>  2 Subject 1 hypo  lv2            0                  0       NA                0
#>  3 Subject 1 hypo  exte…          1.08             243.      92.1             12
#>  4 Subject 1 hyper lv1            3.42             170.     143.              38
#>  5 Subject 1 hyper lv2            0.180             30      264.               2
#>  6 Subject 1 hypo  lv1_…          2.88             130.      92.9             32
#>  7 Subject 1 hyper lv1_…          3.33             166.     141.              37
#>  8 Subject 2 hypo  lv1            0.102             50       94.6              1
#>  9 Subject 2 hypo  lv2            0                  0       NA                0
#> 10 Subject 2 hypo  exte…          0                  0       NA                0
#> # ℹ 25 more rows