Respiratory-02 Calculation — respiratory

The respiratory_02 function calculates metrics for pediatric and adult respiratory populations based on pre-defined criteria, such as low oxygen saturation and specific medication or procedure codes. It returns a summary table of the overall, pediatric, and adult populations, showing counts and proportions.

Usage

respiratory_02(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  vitals_table = NULL,
  medications_table = NULL,
  procedures_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  evitals_12_col,
  emedications_03_col,
  eprocedures_03_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df: A data frame containing incident data with each row representing an observation.
patient_scene_table: A data.frame or tibble containing at least epatient and escene fields as a fact table.
response_table: A data.frame or tibble containing at least the eresponse fields needed for this measure's calculations.
vitals_table: A data.frame or tibble containing at least the evitals fields needed for this measure's calculations.
medications_table: A data.frame or tibble containing only the emedications fields needed for this measure's calculations.
procedures_table: A data.frame or tibble containing only the eprocedures fields needed for this measure's calculations.
erecord_01_col: Column name for eRecord.01, used to form a unique patient 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: integer Column giving the calculated age value.
epatient_16_col: Column giving the provided age unit value.
eresponse_05_col: Column name for response codes (e.g., incident type).
evitals_12_col: Column name for oxygen saturation (SpO2) values.
emedications_03_col: Column name for medication codes.
eprocedures_03_col: Column name for procedure codes.
confidence_interval: Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.
method: 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: 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: 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),
    emedications_03 = c("Oxygen", "Oxygen", "Oxygen", "Oxygen", "Oxygen"),
    evitals_12 = c(60, 59, 58, 57, 56),
    eprocedures_03 = rep("applicable thing", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  respiratory_02(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    emedications_03_col = emedications_03,
    evitals_12_col = evitals_12,
    eprocedures_03_col = eprocedures_03,
    confidence_interval = TRUE
  )
#> 
#> ── Respiratory-02 ──────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Respiratory-02 ──
#> 
#> Running `respiratory_02_population()`  [Completed 1 of 11 tasks] ●●●●──────────…
#> Running `respiratory_02_population()`  [Completed 2 of 11 tasks] ●●●●●●────────…
#> Running `respiratory_02_population()`  [Completed 3 of 11 tasks] ●●●●●●●●●─────…
#> Running `respiratory_02_population()`  [Completed 4 of 11 tasks] ●●●●●●●●●●●●──…
#> Running `respiratory_02_population()`  [Completed 5 of 11 tasks] ●●●●●●●●●●●●●●…
#> Running `respiratory_02_population()`  [Completed 6 of 11 tasks] ●●●●●●●●●●●●●●…
#> Running `respiratory_02_population()`  [Completed 7 of 11 tasks] ●●●●●●●●●●●●●●…
#> Running `respiratory_02_population()`  [Completed 8 of 11 tasks] ●●●●●●●●●●●●●●…
#> Running `respiratory_02_population()`  [Completed 9 of 11 tasks] ●●●●●●●●●●●●●●…
#> Running `respiratory_02_population()`  [Completed 10 of 11 tasks] ●●●●●●●●●●●●●…
#> Running `respiratory_02_population()`  [Completed 11 of 11 tasks] ●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Respiratory-02 ──
#> 
#> 
#> ✔ Function completed in 0.17s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 3 × 8
#>   measure        pop    numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>          <chr>      <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Respiratory-02 Adults         3           3     1 100%          0.310        1
#> 2 Respiratory-02 Peds           2           2     1 100%          0.198        1
#> 3 Respiratory-02 All            5           5     1 100%          0.463        1