Filters data down to the target populations for Trauma-08, and categorizes records to identify needed information for the calculations.
Identifies key categories to records that are 911 requests for patients with trauma during which GCS, systolic blood pressure, and respiratory rate are documented based on specific criteria and calculates related ECG measures. This function segments the data by age into adult and pediatric populations.
Usage
trauma_08_population(
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,
esituation_02_col,
eresponse_05_col,
transport_disposition_col,
evitals_06_col,
evitals_14_col,
evitals_23_col
)Arguments
- df
A data frame or tibble containing EMS data with all relevant columns. Default is
NULL.- 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
A column specifying unique patient records.
- 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
A column indicating the patient’s age in numeric form.
- epatient_16_col
A column specifying the unit of patient age (e.g., "Years", "Days").
- esituation_02_col
A column containing information about the nature of the patient’s condition (e.g., injury type).
- eresponse_05_col
A column specifying the type of response (e.g., 911 codes).
- transport_disposition_col
A column specifying transport disposition for the patient.
- evitals_06_col
A column containing systolic blood pressure (SBP) data from initial vital signs.
- evitals_14_col
A column containing respiratory rate data from initial vital signs.
- evitals_23_col
A column containing total Glasgow Coma Scale (GCS) scores from initial vital signs.
Value
A list that contains the following:
a tibble with counts for each filtering step,
a tibble for each population of interest
a tibble for the initial population
a tibble for the total dataset with computations
Examples
# create tables to test correct functioning
# patient table
patient_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
"2025-01-01", "2025-06-01")),
patient_dob = as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
"2023-01-01", "1970-06-01")),
epatient_15 = c(25, 5, 2, 2, 55), # Ages
epatient_16 = c("Years", "Years", "Years", "Years", "Years")
)
# response table
response_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
eresponse_05 = rep(2205001, 5)
)
# situation table
situation_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
esituation_02 = rep("Yes", 5)
)
# vitals table
vitals_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
evitals_06 = c(100, 90, 80, 70, 85),
evitals_14 = c(30, 9, 8, 7, 31),
evitals_23 = c(6, 7, 8, 8, 7),
)
# disposition table
disposition_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
)
# test the success of the function
result <- trauma_08_population(patient_scene_table = patient_table,
response_table = response_table,
situation_table = situation_table,
vitals_table = vitals_table,
disposition_table = disposition_table,
erecord_01_col = erecord_01,
incident_date_col = incident_date,
patient_DOB_col = patient_dob,
epatient_15_col = epatient_15,
epatient_16_col = epatient_16,
eresponse_05_col = eresponse_05,
esituation_02_col = esituation_02,
evitals_06_col = evitals_06,
evitals_14_col = evitals_14,
evitals_23_col = evitals_23,
transport_disposition_col = edisposition_30
)
#> Running `trauma_08_population()` [Working on 1 of 12 tasks] ●●●───────────────…
#> Running `trauma_08_population()` [Working on 2 of 12 tasks] ●●●●●●────────────…
#> Running `trauma_08_population()` [Working on 3 of 12 tasks] ●●●●●●●●●─────────…
#> Running `trauma_08_population()` [Working on 4 of 12 tasks] ●●●●●●●●●●●───────…
#> Running `trauma_08_population()` [Working on 7 of 12 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_08_population()` [Working on 8 of 12 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_08_population()` [Working on 9 of 12 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_08_population()` [Working on 10 of 12 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_08_population()` [Working on 11 of 12 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_08_population()` [Working on 12 of 12 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 8 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 5
#> 2 Transports 5
#> 3 Injury cases 5
#> 4 Non-null RR, SBP, and GCS 5
#> 5 Adults denominator 2
#> 6 Peds denominator 3
#> 7 Initial population 5
#> 8 Total dataset 5