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TBI-01 Calculation

Source: R/tbi_01.R
tbi_01.Rd

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,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

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 NULL as 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 NULL as 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.

confidence_interval

[Experimental] Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

[Experimental]Character. Specifies the method used to calculate confidence intervals. Options are "wilson" (Wilson score interval) and "clopper-pearson" (exact binomial interval). Partial matching is supported, so "w" and "c" can be used as shorthand.

conf.level

[Experimental]Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.

correct

[Experimental]Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.

...

optional additional arguments to pass onto dplyr::summarize.

Value

A data.frame summarizing results for two population groups (All, Adults and Peds) with the following columns:

  • pop: Population type (All, Adults, and Peds).

  • numerator: Count of incidents meeting the measure.

  • denominator: Total count of included incidents.

  • prop: Proportion of incidents meeting the measure.

  • prop_label: Proportion formatted as a percentage with a specified number of decimal places.

  • lower_ci: Lower bound of the confidence interval for prop (if confidence_interval = TRUE).

  • upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Author

Nicolas Foss, Ed.D., MS

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 the function
# Return 95% confidence intervals using the Wilson method
  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,
    confidence_interval = TRUE
  )
#> 
#> ── 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 0.19s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 2 × 8
#>   measure pop    numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>   <chr>      <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 TBI-01  Adults         3           3     1 100%          0.310        1
#> 2 TBI-01  Peds           2           2     1 100%          0.198        1