hypoglycemia_01: Hypoglycemia-01

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

Hypoglycemia-01

Description

The hypoglycemia_01 function calculates the NEMSQA measure evaluating how often hypoglycemic patients with altered mental status receive hypoglycemia treatment.

Usage

hypoglycemia_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_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,
  esituation_11_col,
  esituation_12_col,
  evitals_18_col,
  evitals_23_col,
  evitals_26_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 or tibble containing emergency response records. Default is NULL.
patient_scene_table	A data.frame or tibble containing at least ePatient and eScene fields as a fact table. Default is NULL.
response_table	A data.frame or tibble containing at least the eResponse fields needed for this measure's calculations. Default is NULL.
situation_table	A data.frame or tibble containing at least the eSituation fields needed for this measure's calculations. Default is NULL.
vitals_table	A data.frame or tibble containing at least the eVitals fields needed for this measure's calculations. Default is NULL.
medications_table	A data.frame or tibble containing at least the eMedications fields needed for this measure's calculations. Default is NULL.
procedures_table	A data.frame or tibble containing at least the eProcedures fields needed for this measure's calculations. Default is NULL.
erecord_01_col	Column representing the unique record identifier.
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 representing the patient's numeric age agnostic of unit.
epatient_16_col	Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minute").
eresponse_05_col	Column containing response type codes.
esituation_11_col	Column for primary impression fields, containing ICD-10 codes.
esituation_12_col	Column for secondary impression fields, containing ICD-10 codes.
evitals_18_col	Column for blood glucose levels.
evitals_23_col	Column for Glasgow Coma Scale (GCS) scores.
evitals_26_col	Column for AVPU alertness levels.
emedications_03_col	Column for administered medications.
eprocedures_03_col	Column for procedures performed.
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(s)

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("E13.64", 3), rep("E16.2", 2)),
  esituation_12 = c(rep("E13.64", 2), rep("E16.2", 3)),
  emedications_03 = c(372326, 376937,
                      377980, 4850,
                      4832),
  evitals_18 = c(60, 59, 58, 57, 56),
  evitals_23 = c(16, 15, 14, 13, 12),
  evitals_26 = c("Alert", "Painful", "Verbal", "Unresponsive", "Alert"),
  eprocedures_03 = rep("710925007", 5)
)

# Run the function
# Return 95% confidence intervals using the Wilson method
hypoglycemia_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,
  emedications_03_col = emedications_03,
  evitals_18_col = evitals_18,
  evitals_23_col = evitals_23,
  evitals_26_col = evitals_26,
  eprocedures_03_col = eprocedures_03,
  confidence_interval = TRUE
)

nemsqar documentation built on Aug. 8, 2025, 6:15 p.m.