Skip to contents

Score Life's Essential 8 cardiovascular health

Source: R/score-le8.R
score_le8.Rd

Computes the eight adult Life's Essential 8 (LE8) component scores and their unweighted mean using the American Heart Association's 2022 Presidential Advisory. This initial implementation requires complete data for every required input and applies only to adults aged 20 years or older.

Usage

score_le8(data, diet_method = "mepa", mepa_columns = NULL)

Arguments

data

A data frame with one row per adult and the required columns described below.

diet_method

A single diet-scoring method applied to every row in data: "mepa" (the default) or "percentile". Values are matched case-insensitively. Score data that use different methods in separate calls.

mepa_columns

NULL, or a named character vector mapping canonical MEPA fields to columns in data. The names are canonical fields and the values are actual column names. Unmapped fields use their canonical names.

Value

A data frame containing the original columns plus mepa_total (missing for population-percentile rows), physical_activity_moderate_equivalent_minutes, the eight component score columns prefixed with le8_, le8_composite_score, and le8_category. The composite score is the exact, unrounded mean. Categories are "low" for scores below 50, "moderate" for scores from 50 to less than 80, and "high" for scores of at least 80.

Required columns

  • age: Age in years; must be at least 20.

  • For diet_method = "mepa", the 16 Table C screener-item columns named below and sex (or female when sex is absent). The item names are matched exactly after case normalization. Supply daily servings in olive_oil, green_leafy_vegetables, other_vegetables, and whole_grains; supply weekly servings or frequency in berries, other_fruit, meat, fish, chicken, cheese, butter_cream, beans, sweets_and_pastries, nuts, and alcohol; supply the number of times per week that fast-food meals are consumed in fast_food. Sex may be encoded as "m"/"f", "male"/"female", or 0/1, where 0 is male and 1 is female. Character encodings are matched case-insensitively, and character "0"/"1" values are also accepted.

  • diet_value: Required only when diet_method = "percentile". Supply a DASH or HEI-2015 percentile from 1 to 100, calculated against the relevant reference distribution. The function does not rank supplied rows against one another. If this column is present for MEPA data, all its values must be missing.

  • moderate_activity_minutes and vigorous_activity_minutes: Weekly minutes. Each vigorous minute counts as two moderate-equivalent minutes.

  • smoking_status: One of "never", "former", or "current".

  • years_since_quit: Years since quitting combustible tobacco. This is used only when smoking_status is "former".

  • current_inhaled_nds: Whether an inhaled nicotine-delivery system is currently used.

  • secondhand_smoke_home: Whether the adult lives with an active indoor smoker.

  • sleep_hours: Average hours of sleep per night.

  • bmi: Body mass index in kg/m^2. Values below 18.5 are not scored because the AHA advisory defers to clinician judgment for underweight adults.

  • bmi_profile: Either "general" or "asian_pacific". The function never infers a profile from race or ethnicity.

  • non_hdl_cholesterol: Non-HDL cholesterol in mg/dL.

  • lipid_lowering_treatment: Whether the lipid value is drug treated.

  • diabetes: Whether diabetes has been diagnosed.

  • glucose_measure: Either "fasting_glucose" or "hba1c".

  • glucose_value: Fasting glucose in mg/dL or HbA1c in percent, as named by glucose_measure. Diagnosed diabetes requires HbA1c. A record with no diabetes diagnosis but a value in the diagnostic diabetes range is rejected because the AHA scoring table does not assign that combination.

  • systolic_bp and diastolic_bp: Blood pressure in mm Hg.

  • antihypertensive_treatment: Whether the blood pressure is treated.

Optional clinical-judgment columns

  • apply_lean_muscular_bmi_override: A caller-adjudicated flag asserting that a lean, higher-muscle-mass adult in the general BMI profile has a BMI from 25 to less than 30 and should receive 100 BMI points. If absent, the standard BMI band is used.

  • apply_sleep_apnea_penalty: If TRUE, subtract 20 points for untreated or undertreated sleep apnea. If absent, no discretionary penalty is applied.

  • apply_prediabetes_metformin_penalty: A caller-adjudicated flag asserting that the adult has a history of prediabetes, is taking metformin to prevent diabetes, is currently normoglycemic, and the clinician chose to apply the AHA's discretionary 20-point decrement. If absent, no discretionary penalty is applied.

Source-constrained behavior

The function derives the 0-to-16 MEPA total from the 16 raw screener responses. For population scoring, callers must supply a DASH or HEI-2015 percentile calculated against the relevant reference population; the rows in data are not their own reference distribution.

The AHA table applies 20-point treatment decrements to blood-lipid and blood-pressure scores. These components are floored at zero, consistent with the AHA's 0-to-100 metric definition and the non-negative possible scores shown in its applied NHANES implementation. Optional sleep-apnea and prediabetes/metformin decrements are applied only through explicit flags. Inputs are not rounded. The documented MEPA daily/weekly equivalences are applied where the source question and criterion use different timeframes. Combinations without a source-defined score, including simultaneous current combustible smoking and current inhaled-NDS use, produce a structured error.

References

Lloyd-Jones DM, Allen NB, Anderson CAM, et al. (2022). Life's Essential 8: Updating and Enhancing the American Heart Association's Construct of Cardiovascular Health. Circulation, 146(5), e18-e43. doi:10.1161/CIR.0000000000001078

Lloyd-Jones DM, Ning H, Labarthe D, et al. (2022). Status of Cardiovascular Health in US Adults and Children Using the American Heart Association's New Life's Essential 8 Metrics. Circulation, 146(11), 822-835. doi:10.1161/CIRCULATIONAHA.122.060911

Examples

patient <- data.frame(
  id = "patient_1",
  age = 55,
  sex = "female",
  # Daily servings
  olive_oil = 2,
  green_leafy_vegetables = 1,
  other_vegetables = 2,
  whole_grains = 2,
  # Weekly servings
  berries = 3,
  other_fruit = 5,
  meat = 2,
  fish = 3,
  chicken = 2,
  cheese = 1,
  butter_cream = 1,
  beans = 3,
  sweets_and_pastries = 1,
  nuts = 4,
  alcohol = 4,
  # Fast-food meals per week
  fast_food = 0,
  moderate_activity_minutes = 90,
  vigorous_activity_minutes = 0,
  smoking_status = "former",
  years_since_quit = 6,
  current_inhaled_nds = FALSE,
  secondhand_smoke_home = FALSE,
  sleep_hours = 7.5,
  bmi = 27.5,
  bmi_profile = "general",
  non_hdl_cholesterol = 145,
  lipid_lowering_treatment = FALSE,
  diabetes = FALSE,
  glucose_measure = "fasting_glucose",
  glucose_value = 95,
  systolic_bp = 128,
  diastolic_bp = 78,
  antihypertensive_treatment = FALSE
)

scores <- score_le8(patient, diet_method = "mepa")
scores[
  c(
    "id",
    "mepa_total",
    "le8_diet_score",
    "le8_composite_score",
    "le8_category"
  )
]
#>          id mepa_total le8_diet_score le8_composite_score le8_category
#> 1 patient_1         14             80                  80         high