This function screens for potential traumatic brain injury (TBI) cases based on specific criteria in a patient dataset. It produces a subset of the data with calculated variables for TBI identification.
Usage
tbi_01(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
situation_table = NULL,
disposition_table = NULL,
vitals_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
eresponse_05_col,
esituation_11_col,
esituation_12_col,
transport_disposition_col,
evitals_06_col,
evitals_12_col,
evitals_16_col,
evitals_23_col,
evitals_26_col,
...
)Arguments
- df
A data frame or tibble containing the patient data.
- patient_scene_table
A data frame or tibble containing only epatient and escene fields as a fact table. Default is
NULL.- response_table
A data frame or tibble containing only the eresponse fields needed for this measure's calculations. Default is
NULL.- situation_table
A data.frame or tibble containing only the esituation fields needed for this measure's calculations. Default is
NULL.- disposition_table
A data.frame or tibble containing only the edisposition fields needed for this measure's calculations. Default is
NULL.- vitals_table
A data.frame or tibble containing only the evitals fields needed for this measure's calculations. Default is
NULL.- erecord_01_col
Column name in df with the patient’s unique record ID.
- incident_date_col
Column that contains the incident date. This defaults to
NULLas it is optional in case not available due to PII restrictions.- patient_DOB_col
Column that contains the patient's date of birth. This defaults to
NULLas it is optional in case not available due to PII restrictions.- epatient_15_col
Column name in df with the patient’s age value.
- epatient_16_col
Column name in df with the patient’s age unit (e.g., years, months).
- eresponse_05_col
Column name in df with response codes for the type of EMS call.
- esituation_11_col
Column name in df with the primary provider impression.
- esituation_12_col
Column name in df with the secondary provider impression.
- transport_disposition_col
Column name in df with the transport disposition.
- evitals_06_col
Column name in df with systolic blood pressure (SBP).
- evitals_12_col
Column name in df with pulse oximetry values.
- evitals_16_col
Column name in df with ETCO2 values. values.
- evitals_23_col
Column name in df with Glasgow Coma Scale (GCS) scores.
- evitals_26_col
Column name in df with AVPU (alert, verbal, painful, unresponsive) values.
- ...
Additional parameters passed to
dplyr::summarizeor other dplyr functions.
Value
A tibble summarizing results for three population groups (Adults, and Peds) with the following columns:
measure: The name of the measure being calculated.
pop: Population type (Adults, Peds).
numerator: Count of incidents where SP02, ETCO2, and SBP were all
measured.
denominator: Total count of incidents.
prop: Proportion of incidents where SP02, ETCO2, and SBP were all
measured.
prop_label: Proportion formatted as a percentage with a specified number
of decimal places.
Examples
# Synthetic test data
test_data <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
epatient_15 = c(34, 5, 45, 2, 60), # Ages
epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
eresponse_05 = rep(2205001, 5),
esituation_11 = c(rep("S02", 3), rep("S06", 2)),
esituation_12 = c(rep("S09.90", 2), rep("S06.0X9", 3)),
evitals_06 = c(85, 80, 100, 90, 82),
evitals_12 = c(95, 96, 97, 98, 99),
evitals_16 = c(35, 36, 37, 38, 39),
evitals_23 = rep(8, 5),
evitals_26 = c("Verbal", "Painful", "Unresponsive", "Verbal", "Painful"),
edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
)
# Run function
tbi_01(
df = test_data,
erecord_01_col = erecord_01,
epatient_15_col = epatient_15,
epatient_16_col = epatient_16,
eresponse_05_col = eresponse_05,
esituation_11_col = esituation_11,
esituation_12_col = esituation_12,
evitals_06_col = evitals_06,
evitals_12_col = evitals_12,
evitals_16_col = evitals_16,
evitals_23_col = evitals_23,
evitals_26_col = evitals_26,
transport_disposition_col = edisposition_30
)
#>
#> ── TBI-01 ──────────────────────────────────────────────────────────────────────
#>
#> ── Gathering Records for TBI-01 ──
#>
#> Running `tbi_01_population()` [Working on 1 of 15 tasks] ●●●──────────────────…
#> Running `tbi_01_population()` [Working on 2 of 15 tasks] ●●●●●────────────────…
#> Running `tbi_01_population()` [Working on 5 of 15 tasks] ●●●●●●●●●●●──────────…
#> Running `tbi_01_population()` [Working on 6 of 15 tasks] ●●●●●●●●●●●●●────────…
#> Running `tbi_01_population()` [Working on 7 of 15 tasks] ●●●●●●●●●●●●●●●──────…
#> Running `tbi_01_population()` [Working on 8 of 15 tasks] ●●●●●●●●●●●●●●●●●────…
#> Running `tbi_01_population()` [Working on 9 of 15 tasks] ●●●●●●●●●●●●●●●●●●●──…
#> Running `tbi_01_population()` [Working on 10 of 15 tasks] ●●●●●●●●●●●●●●●●●●●●…
#> Running `tbi_01_population()` [Working on 12 of 15 tasks] ●●●●●●●●●●●●●●●●●●●●…
#> Running `tbi_01_population()` [Working on 13 of 15 tasks] ●●●●●●●●●●●●●●●●●●●●…
#> Running `tbi_01_population()` [Working on 14 of 15 tasks] ●●●●●●●●●●●●●●●●●●●●…
#> Running `tbi_01_population()` [Working on 15 of 15 tasks] ●●●●●●●●●●●●●●●●●●●●…
#>
#>
#>
#> ── Calculating TBI-01 ──
#>
#>
#> ✔ Function completed in 0.18s.
#>
#> # A tibble: 2 × 6
#> measure pop numerator denominator prop prop_label
#> <chr> <chr> <int> <int> <dbl> <chr>
#> 1 TBI-01 Adults 3 3 1 100%
#> 2 TBI-01 Peds 2 2 1 100%