Runs the documented examples for
detect_hyperglycemic_events().
example(detect_hyperglycemic_events, package = "cgmguru", run.dontrun = FALSE)
#>
#> dtct__> # Load sample data
#> dtct__> library(iglu)
#>
#> dtct__> data(example_data_5_subject)
#>
#> dtct__> data(example_data_hall)
#>
#> dtct__> # Level 1 Hyperglycemia Event (>=15 consecutive min of >180 mg/dL and event
#> dtct__> # ends when there is >=15 consecutive min with a CGM sensor value of <=180 mg/dL)
#> dtct__> hyper_lv1 <- detect_hyperglycemic_events(example_data_5_subject, type = "lv1")
#>
#> dtct__> print(hyper_lv1$events_total)
#> # A tibble: 5 × 3
#> id total_events avg_ep_per_day
#> <chr> <int> <dbl>
#> 1 Subject 1 14 1.1
#> 2 Subject 2 17 1.02
#> 3 Subject 3 8 1.39
#> 4 Subject 4 13 1.01
#> 5 Subject 5 34 3.21
#>
#> dtct__> # Level 2 Hyperglycemia Event (>=15 consecutive min of >250 mg/dL and event
#> dtct__> # ends when there is >=15 consecutive min with a CGM sensor value of <=250 mg/dL)
#> dtct__> hyper_lv2 <- detect_hyperglycemic_events(example_data_5_subject, type = "lv2")
#>
#> dtct__> print(hyper_lv2$events_total)
#> # A tibble: 5 × 3
#> id total_events avg_ep_per_day
#> <chr> <int> <dbl>
#> 1 Subject 1 2 0.16
#> 2 Subject 2 18 1.08
#> 3 Subject 3 4 0.69
#> 4 Subject 4 0 0
#> 5 Subject 5 17 1.6
#>
#> dtct__> # Extended Hyperglycemia Event (>250 mg/dL lasting >=90 cumulative min within a
#> dtct__> # 120-min period, ends when glucose returns to <=180 mg/dL for >=15 consecutive
#> dtct__> # min after)
#> dtct__> hyper_extended <- detect_hyperglycemic_events(example_data_5_subject, type = "extended")
#>
#> dtct__> print(hyper_extended$events_total)
#> # A tibble: 5 × 3
#> id total_events avg_ep_per_day
#> <chr> <int> <dbl>
#> 1 Subject 1 0 0
#> 2 Subject 2 9 0.54
#> 3 Subject 3 2 0.35
#> 4 Subject 4 0 0
#> 5 Subject 5 10 0.94
#>
#> dtct__> # Custom criteria method for the same standard definitions
#> dtct__> hyper_lv1_custom <- detect_hyperglycemic_events(
#> dtct__+ example_data_5_subject,
#> dtct__+ start_gl = 180,
#> dtct__+ dur_length = 15,
#> dtct__+ end_length = 15,
#> dtct__+ end_gl = 180
#> dtct__+ )
#>
#> dtct__> hyper_lv2_custom <- detect_hyperglycemic_events(
#> dtct__+ example_data_5_subject,
#> dtct__+ start_gl = 250,
#> dtct__+ dur_length = 15,
#> dtct__+ end_length = 15,
#> dtct__+ end_gl = 250
#> dtct__+ )
#>
#> dtct__> hyper_extended_custom <- detect_hyperglycemic_events(
#> dtct__+ example_data_5_subject,
#> dtct__+ start_gl = 250,
#> dtct__+ dur_length = 120,
#> dtct__+ end_length = 15,
#> dtct__+ end_gl = 180
#> dtct__+ )
#>
#> dtct__> # Compare event rates across levels
#> dtct__> cat("Level 1 events:", sum(hyper_lv1$events_total$total_events), "\n")
#> Level 1 events: 86
#>
#> dtct__> cat("Level 2 events:", sum(hyper_lv2$events_total$total_events), "\n")
#> Level 2 events: 41
#>
#> dtct__> cat("Extended events:", sum(hyper_extended$events_total$total_events), "\n")
#> Extended events: 21
#>
#> dtct__> # Analysis on larger dataset with Level 1 criteria
#> dtct__> large_hyper <- detect_hyperglycemic_events(example_data_hall, type = "lv1")
#>
#> dtct__> print(large_hyper$events_total)
#> # A tibble: 19 × 3
#> id total_events avg_ep_per_day
#> <chr> <int> <dbl>
#> 1 1636-69-001 4 0.01
#> 2 1636-69-026 1 0.14
#> 3 1636-69-032 1 0.16
#> 4 1636-69-090 3 0.43
#> 5 1636-69-091 0 0
#> 6 1636-69-114 0 0
#> 7 1636-70-1005 3 0.37
#> 8 1636-70-1010 1 0.01
#> 9 2133-004 5 0.81
#> 10 2133-015 3 0.45
#> 11 2133-017 0 0
#> 12 2133-018 12 1.94
#> 13 2133-019 0 0
#> 14 2133-021 9 1.42
#> 15 2133-024 0 0
#> 16 2133-027 0 0
#> 17 2133-035 1 0.14
#> 18 2133-036 2 0.23
#> 19 2133-039 2 0.22
#>
#> dtct__> # Analysis on larger dataset with Level 2 criteria
#> dtct__> large_hyper_lv2 <- detect_hyperglycemic_events(example_data_hall, type = "lv2")
#>
#> dtct__> print(large_hyper_lv2$events_total)
#> # A tibble: 19 × 3
#> id total_events avg_ep_per_day
#> <chr> <int> <dbl>
#> 1 1636-69-001 0 0
#> 2 1636-69-026 0 0
#> 3 1636-69-032 0 0
#> 4 1636-69-090 0 0
#> 5 1636-69-091 0 0
#> 6 1636-69-114 0 0
#> 7 1636-70-1005 0 0
#> 8 1636-70-1010 0 0
#> 9 2133-004 0 0
#> 10 2133-015 0 0
#> 11 2133-017 0 0
#> 12 2133-018 2 0.32
#> 13 2133-019 0 0
#> 14 2133-021 0 0
#> 15 2133-024 0 0
#> 16 2133-027 0 0
#> 17 2133-035 0 0
#> 18 2133-036 0 0
#> 19 2133-039 0 0
#>
#> dtct__> # Analysis on larger dataset with Extended criteria
#> dtct__> large_hyper_extended <- detect_hyperglycemic_events(example_data_hall, type = "extended")
#>
#> dtct__> print(large_hyper_extended$events_total)
#> # A tibble: 19 × 3
#> id total_events avg_ep_per_day
#> <chr> <int> <dbl>
#> 1 1636-69-001 0 0
#> 2 1636-69-026 0 0
#> 3 1636-69-032 0 0
#> 4 1636-69-090 0 0
#> 5 1636-69-091 0 0
#> 6 1636-69-114 0 0
#> 7 1636-70-1005 0 0
#> 8 1636-70-1010 0 0
#> 9 2133-004 0 0
#> 10 2133-015 0 0
#> 11 2133-017 0 0
#> 12 2133-018 1 0.16
#> 13 2133-019 0 0
#> 14 2133-021 0 0
#> 15 2133-024 0 0
#> 16 2133-027 0 0
#> 17 2133-035 0 0
#> 18 2133-036 0 0
#> 19 2133-039 0 0
#>
#> dtct__> # View detailed events for specific subject
#> dtct__> if(nrow(hyper_lv1$events_detailed) > 0) {
#> dtct__+ first_subject <- hyper_lv1$events_detailed$id[1]
#> dtct__+ subject_events <- hyper_lv1$events_detailed[hyper_lv1$events_detailed$id == first_subject, ]
#> dtct__+ head(subject_events)
#> dtct__+ }
#> # A tibble: 6 × 7
#> id start_time start_glucose end_time end_glucose
#> <chr> <dttm> <dbl> <dttm> <dbl>
#> 1 Subject 1 2015-06-11 15:45:07 194 2015-06-11 16:50:07 187
#> 2 Subject 1 2015-06-11 17:25:07 195 2015-06-11 19:45:06 187
#> 3 Subject 1 2015-06-11 22:35:06 187 2015-06-11 23:45:06 185
#> 4 Subject 1 2015-06-12 07:50:04 181 2015-06-12 09:15:04 181
#> 5 Subject 1 2015-06-13 17:04:59 181 2015-06-13 18:24:59 186
#> 6 Subject 1 2015-06-13 19:44:59 223 2015-06-13 20:19:58 186
#> # ℹ 2 more variables: start_index <int>, end_index <int>