Skip to contents

Hypoglycemia-01

Source: R/hypoglycemia_01.R
hypoglycemia_01.Rd

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

[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("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
)
#> 
#> ── Hypoglycemia-01 ─────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Hypoglycemia-01 ──
#> 
#> Running `hypoglycemia_01_population()`  [Working on 1 of 17 tasks] ●●●─────────…
#> Running `hypoglycemia_01_population()`  [Working on 2 of 17 tasks] ●●●●●───────…
#> Running `hypoglycemia_01_population()`  [Working on 3 of 17 tasks] ●●●●●●──────…
#> Running `hypoglycemia_01_population()`  [Working on 4 of 17 tasks] ●●●●●●●●────…
#> Running `hypoglycemia_01_population()`  [Working on 5 of 17 tasks] ●●●●●●●●●●──…
#> Running `hypoglycemia_01_population()`  [Working on 6 of 17 tasks] ●●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 7 of 17 tasks] ●●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 8 of 17 tasks] ●●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 9 of 17 tasks] ●●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 10 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 11 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 12 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 13 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 14 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 15 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 16 of 17 tasks] ●●●●●●●●●●●…
#> Running `hypoglycemia_01_population()`  [Working on 17 of 17 tasks] ●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Hypoglycemia-01 ──
#> 
#> 
#> ✔ Function completed in 0.24s.
#> 
#> 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 Hypoglycemia-01 Adul…         2           2     1 100%          0.198        1
#> 2 Hypoglycemia-01 Peds          2           2     1 100%          0.198        1
#> 3 Hypoglycemia-01 All           4           4     1 100%          0.396        1