Nothing
R/trauma_03.R
In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations
Defines functions
trauma_03
Documented in
trauma_03
#' @title Trauma-03 Calculation
#'
#' @description
#'
#' This function calculates the "Trauma-03" measure, which evaluates pain scale
#' reassessment for trauma patients, using a comprehensive data frame with EMS
#' records. The function processes input data to create both fact and dimension
#' tables, identifies eligible patients, and summarizes results for adult and
#' pediatric populations.
#'
#' @param df A data frame or tibble containing EMS data with all relevant
#' columns. Default is `NULL`.
#' @param patient_scene_table A data frame or tibble containing only epatient
#' and escene fields as a fact table. Default is `NULL`.
#' @param response_table A data frame or tibble containing only the eresponse
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data frame or tibble containing only the esituation
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param disposition_table A data frame or tibble containing only the
#' edisposition fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param vitals_table A data frame or tibble containing only the evitals fields
#' needed for this measure's calculations. Default is `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier.
#' @param 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.
#' @param 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.
#' @param epatient_15_col The column for patient age numeric value.
#' @param epatient_16_col The column for patient age unit (e.g., "Years",
#' "Months").
#' @param esituation_02_col The column containing information on the presence of
#' injury.
#' @param eresponse_05_col The column representing the 911 response type.
#' @param edisposition_28_col The column for patient care disposition details.
#' @param transport_disposition_col The column for patient transport
#' disposition.
#' @param evitals_01_col The column for the time of pain scale measurement.
#' @param evitals_27_col The column for the full set of pain scale scores.
#' @param evitals_27_initial_col The column for the initial pain scale score.
#' @param evitals_27_last_col The column for the last pain scale score.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("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.
#' @param conf.level `r lifecycle::badge("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.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return 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`).
#'
#' @examples
#' # Synthetic test data
#' # for testing a single pain scale column
#' test_data2 <- 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_02 = rep("Yes", 5),
#' evitals_01 = lubridate::as_datetime(c("2025-01-01 12:00:00", "2025-01-05
#' 18:00:00", "2025-02-01 06:00:00", "2025-01-01 01:00:00", "2025-06-01
#' 14:00:00")),
#' edisposition_28 = rep(4228001, 5),
#' edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
#' )
#'
#' # Expand data so each erecord_01 has 2 rows (one for each pain score)
#' test_data_expanded2 <- test_data2 |>
#' tidyr::uncount(weights = 2) |> # Duplicate each row twice
#' # Assign pain scores
#' dplyr::mutate(evitals_27 = c(0, 0, 2, 1, 4, 3, 6, 5, 8, 7)) |>
#' dplyr::group_by(erecord_01) |>
#' dplyr::mutate(
#' # Lower score = later time
#' time_offset = dplyr::if_else(dplyr::row_number() == 1, -5, 0),
#' evitals_01 = evitals_01 + lubridate::dminutes(time_offset)
#' ) |>
#' dplyr::ungroup() |>
#' dplyr::select(-time_offset) # Remove temporary column
#'
#' # Run function with the single pain score column
#' # Return 95% confidence intervals using the Wilson method
#' trauma_03(
#' df = test_data_expanded2,
#' erecord_01_col = erecord_01,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' esituation_02_col = esituation_02,
#' evitals_01_col = evitals_01,
#' evitals_27_initial_col = NULL,
#' evitals_27_last_col = NULL,
#' evitals_27_col = evitals_27,
#' edisposition_28_col = edisposition_28,
#' transport_disposition_col = edisposition_30,
#' confidence_interval = TRUE
#' )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
trauma_03 <- function(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,
esituation_02_col,
eresponse_05_col,
edisposition_28_col,
transport_disposition_col,
evitals_01_col,
evitals_27_col = NULL,
evitals_27_initial_col = NULL,
evitals_27_last_col = NULL,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
# utilize applicable tables to analyze the data for the measure
if (
any(
!is.null(patient_scene_table),
!is.null(vitals_table),
!is.null(situation_table),
!is.null(disposition_table),
!is.null(response_table)
) &&
is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# Header
cli::cli_h1("Trauma-03")
# Header
cli::cli_h2("Gathering Records for Trauma-03")
# Gather the population of interest
trauma_03_populations <- trauma_03_population(
patient_scene_table = patient_scene_table,
response_table = response_table,
situation_table = situation_table,
vitals_table = vitals_table,
disposition_table = disposition_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
esituation_02_col = {{ esituation_02_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_27_col = {{ evitals_27_col }},
evitals_27_initial_col = {{ evitals_27_initial_col }},
evitals_27_last_col = {{ evitals_27_last_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_col = {{ transport_disposition_col }}
)
# Create a separator
cli::cli_text("\n")
# Header for calculations
cli::cli_h2("Calculating Trauma-03")
# summarize
trauma.03 <- results_summarize(total_population = trauma_03_populations$initial_population,
adult_population = trauma_03_populations$adults,
peds_population = trauma_03_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Trauma-03",
numerator_col = PAIN_SCALE,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(trauma.03$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(trauma.03)
} else if (
any(
is.null(patient_scene_table),
is.null(vitals_table),
is.null(situation_table),
is.null(disposition_table),
is.null(response_table)
) &&
!is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# Header
cli::cli_h1("Trauma-03")
# Header
cli::cli_h2("Gathering Records for Trauma-03")
# Gather the population of interest
trauma_03_populations <- trauma_03_population(
df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
esituation_02_col = {{ esituation_02_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_27_col = {{ evitals_27_col }},
evitals_27_initial_col = {{ evitals_27_initial_col }},
evitals_27_last_col = {{ evitals_27_last_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_col = {{ transport_disposition_col }}
)
# Create a separator
cli::cli_text("\n")
# Header for calculations
cli::cli_h2("Calculating Trauma-03")
# summarize
trauma.03 <- results_summarize(total_population = trauma_03_populations$initial_population,
adult_population = trauma_03_populations$adults,
peds_population = trauma_03_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Trauma-03",
numerator_col = PAIN_SCALE,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(trauma.03$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(trauma.03)
}
}
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Defines functions trauma_03
Documented in trauma_03
#' @title Trauma-03 Calculation
#'
#' @description
#'
#' This function calculates the "Trauma-03" measure, which evaluates pain scale
#' reassessment for trauma patients, using a comprehensive data frame with EMS
#' records. The function processes input data to create both fact and dimension
#' tables, identifies eligible patients, and summarizes results for adult and
#' pediatric populations.
#'
#' @param df A data frame or tibble containing EMS data with all relevant
#' columns. Default is `NULL`.
#' @param patient_scene_table A data frame or tibble containing only epatient
#' and escene fields as a fact table. Default is `NULL`.
#' @param response_table A data frame or tibble containing only the eresponse
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data frame or tibble containing only the esituation
#' fields needed for this measure's calculations. Default is `NULL`.
#' @param disposition_table A data frame or tibble containing only the
#' edisposition fields needed for this measure's calculations. Default is
#' `NULL`.
#' @param vitals_table A data frame or tibble containing only the evitals fields
#' needed for this measure's calculations. Default is `NULL`.
#' @param erecord_01_col The column representing the EMS record unique
#' identifier.
#' @param 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.
#' @param 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.
#' @param epatient_15_col The column for patient age numeric value.
#' @param epatient_16_col The column for patient age unit (e.g., "Years",
#' "Months").
#' @param esituation_02_col The column containing information on the presence of
#' injury.
#' @param eresponse_05_col The column representing the 911 response type.
#' @param edisposition_28_col The column for patient care disposition details.
#' @param transport_disposition_col The column for patient transport
#' disposition.
#' @param evitals_01_col The column for the time of pain scale measurement.
#' @param evitals_27_col The column for the full set of pain scale scores.
#' @param evitals_27_initial_col The column for the initial pain scale score.
#' @param evitals_27_last_col The column for the last pain scale score.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#' `TRUE`, the function calculates a confidence interval for the proportion
#' estimate.
#' @param method `r lifecycle::badge("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.
#' @param conf.level `r lifecycle::badge("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.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#' applies a continuity correction to the Wilson score interval when `method =
#' "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return 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`).
#'
#' @examples
#' # Synthetic test data
#' # for testing a single pain scale column
#' test_data2 <- 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_02 = rep("Yes", 5),
#' evitals_01 = lubridate::as_datetime(c("2025-01-01 12:00:00", "2025-01-05
#' 18:00:00", "2025-02-01 06:00:00", "2025-01-01 01:00:00", "2025-06-01
#' 14:00:00")),
#' edisposition_28 = rep(4228001, 5),
#' edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
#' )
#'
#' # Expand data so each erecord_01 has 2 rows (one for each pain score)
#' test_data_expanded2 <- test_data2 |>
#' tidyr::uncount(weights = 2) |> # Duplicate each row twice
#' # Assign pain scores
#' dplyr::mutate(evitals_27 = c(0, 0, 2, 1, 4, 3, 6, 5, 8, 7)) |>
#' dplyr::group_by(erecord_01) |>
#' dplyr::mutate(
#' # Lower score = later time
#' time_offset = dplyr::if_else(dplyr::row_number() == 1, -5, 0),
#' evitals_01 = evitals_01 + lubridate::dminutes(time_offset)
#' ) |>
#' dplyr::ungroup() |>
#' dplyr::select(-time_offset) # Remove temporary column
#'
#' # Run function with the single pain score column
#' # Return 95% confidence intervals using the Wilson method
#' trauma_03(
#' df = test_data_expanded2,
#' erecord_01_col = erecord_01,
#' epatient_15_col = epatient_15,
#' epatient_16_col = epatient_16,
#' eresponse_05_col = eresponse_05,
#' esituation_02_col = esituation_02,
#' evitals_01_col = evitals_01,
#' evitals_27_initial_col = NULL,
#' evitals_27_last_col = NULL,
#' evitals_27_col = evitals_27,
#' edisposition_28_col = edisposition_28,
#' transport_disposition_col = edisposition_30,
#' confidence_interval = TRUE
#' )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
trauma_03 <- function(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,
esituation_02_col,
eresponse_05_col,
edisposition_28_col,
transport_disposition_col,
evitals_01_col,
evitals_27_col = NULL,
evitals_27_initial_col = NULL,
evitals_27_last_col = NULL,
confidence_interval = FALSE,
method = c("wilson", "clopper-pearson"),
conf.level = 0.95,
correct = TRUE,
...) {
# Set default method and adjustment method
method <- match.arg(method, choices = c("wilson", "clopper-pearson"))
# utilize applicable tables to analyze the data for the measure
if (
any(
!is.null(patient_scene_table),
!is.null(vitals_table),
!is.null(situation_table),
!is.null(disposition_table),
!is.null(response_table)
) &&
is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# Header
cli::cli_h1("Trauma-03")
# Header
cli::cli_h2("Gathering Records for Trauma-03")
# Gather the population of interest
trauma_03_populations <- trauma_03_population(
patient_scene_table = patient_scene_table,
response_table = response_table,
situation_table = situation_table,
vitals_table = vitals_table,
disposition_table = disposition_table,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
esituation_02_col = {{ esituation_02_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_27_col = {{ evitals_27_col }},
evitals_27_initial_col = {{ evitals_27_initial_col }},
evitals_27_last_col = {{ evitals_27_last_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_col = {{ transport_disposition_col }}
)
# Create a separator
cli::cli_text("\n")
# Header for calculations
cli::cli_h2("Calculating Trauma-03")
# summarize
trauma.03 <- results_summarize(total_population = trauma_03_populations$initial_population,
adult_population = trauma_03_populations$adults,
peds_population = trauma_03_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Trauma-03",
numerator_col = PAIN_SCALE,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(trauma.03$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(trauma.03)
} else if (
any(
is.null(patient_scene_table),
is.null(vitals_table),
is.null(situation_table),
is.null(disposition_table),
is.null(response_table)
) &&
!is.null(df)
) {
# Start timing the function execution
start_time <- Sys.time()
# Header
cli::cli_h1("Trauma-03")
# Header
cli::cli_h2("Gathering Records for Trauma-03")
# Gather the population of interest
trauma_03_populations <- trauma_03_population(
df = df,
erecord_01_col = {{ erecord_01_col }},
incident_date_col = {{ incident_date_col }},
patient_DOB_col = {{ patient_DOB_col }},
epatient_15_col = {{ epatient_15_col }},
epatient_16_col = {{ epatient_16_col }},
esituation_02_col = {{ esituation_02_col }},
eresponse_05_col = {{ eresponse_05_col }},
evitals_01_col = {{ evitals_01_col }},
evitals_27_col = {{ evitals_27_col }},
evitals_27_initial_col = {{ evitals_27_initial_col }},
evitals_27_last_col = {{ evitals_27_last_col }},
edisposition_28_col = {{ edisposition_28_col }},
transport_disposition_col = {{ transport_disposition_col }}
)
# Create a separator
cli::cli_text("\n")
# Header for calculations
cli::cli_h2("Calculating Trauma-03")
# summarize
trauma.03 <- results_summarize(total_population = trauma_03_populations$initial_population,
adult_population = trauma_03_populations$adults,
peds_population = trauma_03_populations$peds,
population_names = c("all", "adults", "peds"),
measure_name = "Trauma-03",
numerator_col = PAIN_SCALE,
confidence_interval = confidence_interval,
method = method,
conf.level = conf.level,
correct = correct,
...
)
# create a separator
cli::cli_text("\n")
# Calculate and display the runtime
end_time <- Sys.time()
run_time_secs <- difftime(end_time, start_time, units = "secs")
run_time_secs <- as.numeric(run_time_secs)
if (run_time_secs >= 60) {
run_time <- round(run_time_secs / 60, 2) # Convert to minutes and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")
} else {
run_time <- round(run_time_secs, 2) # Keep in seconds and round
cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")
}
# create a separator
cli::cli_text("\n")
# when confidence interval is "wilson", check for n < 10
# to warn about incorrect Chi-squared approximation
if (any(trauma.03$denominator < 10) && method == "wilson" && confidence_interval) {
cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")
}
return(trauma.03)
}
}
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