This function processes and analyzes the dataset to generate the populations of interest needed to perform calculations to obtain performance data.
Usage
airway_01_population(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
arrest_table = NULL,
procedures_table = NULL,
vitals_table = NULL,
erecord_01_col,
incident_date_col = NULL,
patient_DOB_col = NULL,
epatient_15_col,
epatient_16_col,
earrest_01_col,
eresponse_05_col,
evitals_01_col,
evitals_06_col,
evitals_12_col,
eprocedures_01_col,
eprocedures_02_col,
eprocedures_03_col,
eprocedures_05_col,
eprocedures_06_col
)Arguments
- df
A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.
- patient_scene_table
A data.frame or tibble containing at least epatient, escene, and earrest.01 fields as a fact table.
- response_table
A data.frame or tibble containing at least the eresponse fields needed for this measure's calculations.
- arrest_table
A data.frame or tibble containing at least the earrest fields needed for this measure's calculations.
- procedures_table
A dataframe or tibble containing at least the eProcedures fields needed.
- vitals_table
A dataframe or tibble containing at least the eVitals fields needed.
- erecord_01_col
The column representing the EMS record unique identifier.
- 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 representing the patient's numeric age agnostic of unit.
- epatient_16_col
Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minutes").
- earrest_01_col
Column representing whether or not the patient is in arrest.
- eresponse_05_col
Column that contains eResponse.05.
- evitals_01_col
Date-time or POSIXct column containing vital signs date/time
- evitals_06_col
Numeric column containing systolic blood pressure values
- evitals_12_col
Numeric column containing pulse oximetry values.
- eprocedures_01_col
Date-time or POSIXct column for procedures
- eprocedures_02_col
Column name for whether or not the procedure was performed prior to EMS care being provided.
- eprocedures_03_col
Column containing procedure codes with or without procedure names.
- eprocedures_05_col
Column containing a count for how many times procedure was attempted.
- eprocedures_06_col
Column indicating whether or not procedure was successful.
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
# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble
# create tables to test correct functioning
# patient table
patient_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
incident_date = rep(as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
"2025-01-01", "2025-06-01")), 2),
patient_dob = rep(as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
"2023-01-01", "1970-06-01")), 2),
epatient_15 = rep(c(25, 5, 2, 2, 55), 2), # Ages
epatient_16 = rep(c("Years", "Years", "Years", "Years", "Years"), 2)
)
# response table
response_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
eresponse_05 = rep(2205001, 10)
)
# vitals table
vitals_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
"2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
"2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
"2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
evitals_06 = rep(c(90, 100, 102, 103, 104), 2),
evitals_12 = rep(c(90, 91, 92, 93, 94), 2)
)
# arrest table
arrest_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
earrest_01 = rep("No", 10)
)
# procedures table
procedures_table <- tibble::tibble(
erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
"2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
"2025-06-01 13:00:00")), 2),
eprocedures_02 = rep("No", 10),
eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
673005), 2),
eprocedures_05 = rep(1, 10),
eprocedures_06 = rep(9923003, 10)
)
# Run the function
result <- airway_01_population(df = NULL,
patient_scene_table = patient_table,
procedures_table = procedures_table,
vitals_table = vitals_table,
arrest_table = arrest_table,
response_table = response_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,
eprocedures_01_col = eprocedures_01,
eprocedures_02_col = eprocedures_02,
eprocedures_03_col = eprocedures_03,
eprocedures_05_col = eprocedures_05,
eprocedures_06_col = eprocedures_06,
earrest_01_col = earrest_01,
evitals_01_col = evitals_01,
evitals_06_col = evitals_06,
evitals_12_col = evitals_12
)
#> Running `airway_01_population()` [Working on 1 of 19 tasks] ●●●───────────────…
#> Running `airway_01_population()` [Working on 2 of 19 tasks] ●●●●──────────────…
#> Running `airway_01_population()` [Working on 3 of 19 tasks] ●●●●●●────────────…
#> Running `airway_01_population()` [Working on 4 of 19 tasks] ●●●●●●●───────────…
#> Running `airway_01_population()` [Working on 5 of 19 tasks] ●●●●●●●●●─────────…
#> Running `airway_01_population()` [Working on 6 of 19 tasks] ●●●●●●●●●●────────…
#> Running `airway_01_population()` [Working on 7 of 19 tasks] ●●●●●●●●●●●●──────…
#> Running `airway_01_population()` [Working on 8 of 19 tasks] ●●●●●●●●●●●●●●────…
#> Running `airway_01_population()` [Working on 9 of 19 tasks] ●●●●●●●●●●●●●●●───…
#> Running `airway_01_population()` [Working on 10 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 11 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 12 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 13 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 14 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 15 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 16 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 17 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 18 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `airway_01_population()` [Working on 19 of 19 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 19 × 2
#> filter count
#> <chr> <int>
#> 1 Invasive airway procedures 5
#> 2 Successful invasive airway procedures 5
#> 3 First attempt successful invasive airway procedures 5
#> 4 911 calls 5
#> 5 Excluded cardiac arrests 0
#> 6 Excluded newborns 0
#> 7 All initial population successful intubation with no hypoxia 5
#> 8 All initial population successful intubation with no hypotension 5
#> 9 Initial population ages >= 10 yrs successful intubation with no hypoxi… 2
#> 10 Initial population ages 1-9 yrs successful intubation with no hypoxia/… 3
#> 11 Initial population ages < 1 yrs & > 28 days successful intubation with… 0
#> 12 Initial population ages < 28 days successful intubation with no hypoxi… 0
#> 13 All initial population successful intubation with no hypoxia or hypoxi… 5
#> 14 Adults successful intubation no hypoxia or hypotension 2
#> 15 Peds successful intubation no hypoxia or hypotension 3
#> 16 Adults denominator 2
#> 17 Peds denominator 3
#> 18 Initial Population 5
#> 19 Total procedures in dataset 5