nemsqar (version 1.1.0)

respiratory_02: Respiratory-02 Calculation

Description

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,
  ...
)

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).

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.

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
  )