Filters data down to the target populations for Stroke-01, and categorizes records to identify needed information for the calculations.
Identifies key categories related to stroke-related incidents in an EMS dataset, specifically focusing on cases where 911 was called for stroke, and a stroke scale was administered. .
Usage
stroke_01_population(
df = NULL,
patient_scene_table = NULL,
response_table = NULL,
situation_table = NULL,
vitals_table = NULL,
erecord_01_col,
eresponse_05_col,
esituation_11_col,
esituation_12_col,
evitals_23_col,
evitals_26_col,
evitals_29_col,
evitals_30_col
)Arguments
- df
A data frame or tibble containing the dataset. Each row should represent a unique patient encounter.
- 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.- vitals_table
A data.frame or tibble containing only the evitals fields needed for this measure's calculations. Default is
NULL.- erecord_01_col
The column containing unique record identifiers for each encounter.
- eresponse_05_col
The column containing EMS response codes, which should include 911 response codes.
- esituation_11_col
The column containing the primary impression codes or descriptions related to the situation.
- esituation_12_col
The column containing secondary impression codes or descriptions related to the situation.
- evitals_23_col
The column containing the Glasgow Coma Scale (GCS) score.
- evitals_26_col
The column containing the AVPU (alert, verbal, pain, unresponsive) scale value.
- evitals_29_col
The column containing the stroke scale score achieved during assessment.
- evitals_30_col
The column containing stroke scale type descriptors (e.g., FAST, NIH, etc.).
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_11 = c(rep("I60", 3), rep("I61", 2)),
esituation_12 = c(rep("I63", 2), rep("I64", 3)),
)
# vitals table
vitals_table <- tibble::tibble(
erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
evitals_23 = c(16, 15, 14, 13, 12),
evitals_26 = c("Alert", "Painful", "Verbal", "Unresponsive", "Alert"),
evitals_29 = rep("positive", 5),
evitals_30 = rep("a pain scale", 5)
)
# test the success of the function
result <- stroke_01_population(patient_scene_table = patient_table,
response_table = response_table,
situation_table = situation_table,
vitals_table = vitals_table,
erecord_01_col = erecord_01,
eresponse_05_col = eresponse_05,
esituation_11_col = esituation_11,
esituation_12_col = esituation_12,
evitals_29_col = evitals_29,
evitals_23_col = evitals_23,
evitals_26_col = evitals_26,
evitals_30_col = evitals_30
)
#> Running `stroke_01_population()` [Working on 1 of 11 tasks] ●●●●──────────────…
#> Running `stroke_01_population()` [Working on 2 of 11 tasks] ●●●●●●────────────…
#> Running `stroke_01_population()` [Working on 3 of 11 tasks] ●●●●●●●●●─────────…
#> Running `stroke_01_population()` [Working on 4 of 11 tasks] ●●●●●●●●●●●●──────…
#> Running `stroke_01_population()` [Working on 5 of 11 tasks] ●●●●●●●●●●●●●●●───…
#> Running `stroke_01_population()` [Working on 6 of 11 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `stroke_01_population()` [Working on 7 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()` [Working on 8 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()` [Working on 9 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()` [Working on 10 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()` [Working on 11 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#>
# show the results of filtering at each step
result$filter_process
#> # A tibble: 7 × 2
#> filter count
#> <chr> <int>
#> 1 911 calls 5
#> 2 Stroke cases 5
#> 3 GCUS <= 9 0
#> 4 AVPU = Unresponsive 1
#> 5 Non-Null Stroke Scale Score or Type 5
#> 6 Initial population 5
#> 7 Total dataset 5