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 injury who were assessed for pain based on specific criteria and calculates related ECG measures. This function segments the data by age into adult and pediatric populations.
Usage
trauma_01_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,
evitals_23_col,
evitals_26_col,
evitals_27_col,
edisposition_28_col,
transport_disposition_col
)Arguments
- df
A data frame or tibble containing EMS records. 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
Column name representing the EMS record ID.
- incident_date_col
Column name for the incident date. Default is
NULL.- patient_DOB_col
Column name for the patient's date of birth. Default is
NULL.- epatient_15_col
Column name for the patient's age in numeric format.
- epatient_16_col
Column name for the unit of age (e.g., "Years", "Months").
- esituation_02_col
Column name indicating if the situation involved an injury.
- eresponse_05_col
Column name for the type of EMS response (e.g., 911 call).
- evitals_23_col
Column name for the Glasgow Coma Scale (GCS) total score.
- evitals_26_col
Column name for AVPU (Alert, Voice, Pain, Unresponsive) status.
- evitals_27_col
Column name for the pain scale assessment.
- edisposition_28_col
Column name for patient care disposition details.
- transport_disposition_col
Column name for transport disposition details.
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_23 = rep(15, 5),
evitals_26 = rep("Alert", 5),
evitals_27 = c(0, 2, 4, 6, 8)
)
# disposition table
disposition_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
edisposition_28 = rep(4228001, 5),
edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
)
# test the success of the function
result <- trauma_01_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_23_col = evitals_23,
evitals_26_col = evitals_26,
evitals_27_col = evitals_27,
edisposition_28_col = edisposition_28,
transport_disposition_col = edisposition_30
)
#> Running `trauma_01_population()` [Working on 1 of 14 tasks] ●●●───────────────…
#> Running `trauma_01_population()` [Working on 2 of 14 tasks] ●●●●●─────────────…
#> Running `trauma_01_population()` [Working on 3 of 14 tasks] ●●●●●●●───────────…
#> Running `trauma_01_population()` [Working on 4 of 14 tasks] ●●●●●●●●●●────────…
#> Running `trauma_01_population()` [Working on 5 of 14 tasks] ●●●●●●●●●●●●──────…
#> Running `trauma_01_population()` [Working on 6 of 14 tasks] ●●●●●●●●●●●●●●────…
#> Running `trauma_01_population()` [Working on 7 of 14 tasks] ●●●●●●●●●●●●●●●●──…
#> Running `trauma_01_population()` [Working on 8 of 14 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 9 of 14 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 10 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 11 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 12 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 13 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_01_population()` [Working on 14 of 14 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 11 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 5
#> 2 GCS is 15 5
#> 3 AVPU is alert 5
#> 4 Transports 5
#> 5 Injury cases 5
#> 6 Patient evaluated and care provided 5
#> 7 Pain scale administered 5
#> 8 Adults denominator 2
#> 9 Peds denominator 3
#> 10 Initial population 5
#> 11 Total dataset 5