Title: Compute Risk Scores for Cardiovascular Diseases
Version: 1.2.1
Description: Calculate various cardiovascular disease risk scores from the Framingham Heart Study (FHS), the American College of Cardiology (ACC), and the American Heart Association (AHA) as described in D’agostino, et al (2008) <doi:10.1161/circulationaha.107.699579>, Goff, et al (2013) <doi:10.1161/01.cir.0000437741.48606.98>, and Mclelland, et al (2015) <doi:10.1016/j.jacc.2015.08.035>, and Khan, et al (2024) <doi:10.1161/CIRCULATIONAHA.123.067626>.
License: GPL-3
Depends: R (≥ 3.5)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
URL: https://vcastro.github.io/CVrisk/, https://github.com/vcastro/CVrisk/
BugReports: https://github.com/vcastro/CVrisk/issues
Imports: utils, preventr
Suggests: testthat (≥ 2.1.0), covr, tibble, knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-01-10 17:15:26 UTC; vcastro
Author: Victor Castro ORCID iD [aut, cre]
Maintainer: Victor Castro <vcastro@mgh.harvard.edu>
Repository: CRAN
Date/Publication: 2026-01-10 17:32:04 UTC

CVrisk: Compute Risk Scores for Cardiovascular Diseases

Description

logo

Calculate various cardiovascular disease risk scores from the Framingham Heart Study (FHS), the American College of Cardiology (ACC), and the American Heart Association (AHA) as described in D’agostino, et al (2008) doi:10.1161/circulationaha.107.699579, Goff, et al (2013) doi:10.1161/01.cir.0000437741.48606.98, and Mclelland, et al (2015) doi:10.1016/j.jacc.2015.08.035, and Khan, et al (2024) doi:10.1161/CIRCULATIONAHA.123.067626.

Author(s)

Maintainer: Victor Castro vcastro@mgh.harvard.edu (ORCID)

See Also

Useful links:

ACC/AHA 2013 ASCVD risk score

Description

Computes 10-year risk for hard ASCVD event (defined as first occurrence of non-fatal myocardial infarction (MI), congestive heart disease (CHD) death, or fatal or nonfatal stroke).

Usage

ascvd_10y_accaha(
  race = "white",
  gender = c("male", "female"),
  age,
  totchol,
  hdl,
  sbp,
  bp_med,
  smoker,
  diabetes,
  ...
)

Arguments

race

patient race (white, aa, other)

gender

patient gender (male, female)

age

patient age (years)

totchol

Total cholesterol (mg/dL)

hdl

HDL cholesterol (mg/dL)

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

...

Additional predictors can be passed and will be ignored

Value

Estimated 10-Y Risk for hard ASCVD (percent)

References

Goff, David C., et al. "2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines." Journal of the American College of Cardiology 63.25 Part B (2014): 2935-2959.

Examples

library(CVrisk)
ascvd_10y_accaha(
  race = "aa", gender = "male", age = 55,
  totchol = 213, hdl = 50, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)

Framingham 2008 ASCVD risk score (with lab measurement)

Description

Computes 10-year risk for ASCVD event (coronary death, myocardial infarction (MI), coronary insufficiency, angina, ischemic stroke, hemorrhagic stroke, transient ischemic attack, peripheral artery disease, or heart failure).

Usage

ascvd_10y_frs(
  gender = c("male", "female"),
  age,
  hdl,
  totchol,
  sbp,
  bp_med,
  smoker,
  diabetes,
  ...
)

Arguments

gender

patient gender (male, female)

age

patient age (years), between 30 and 74

hdl

HDL cholesterol (mg/dL)

totchol

Total cholesterol (mg/dL)

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

...

Additional predictors can be passed and will be ignored

Value

Estimated 10-Y Risk for hard ASCVD event (percent)

References

D’agostino, R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M. and Kannel, W.B., 2008. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation, 117(6), pp.743-753.

Examples

library(CVrisk)
ascvd_10y_frs(
  gender = "male", age = 55,
  hdl = 50, totchol = 213, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)

# 16.7

Framingham 2008 ASCVD risk score (no lab measurement)

Description

Computes 10-year risk for ASCVD event (coronary death, myocardial infarction (MI),coronary insufficiency, angina, ischemic stroke, hemorrhagic stroke, transient ischemic attack, peripheral artery disease, or heart failure).

Usage

ascvd_10y_frs_simple(
  gender = c("male", "female"),
  age,
  bmi,
  sbp,
  bp_med,
  smoker,
  diabetes,
  ...
)

Arguments

gender

patient gender (male, female)

age

patient age (years), between 30 and 74

bmi

Body mass index (kg/m2)

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

...

Additional predictors can be passed and will be ignored

Value

Estimated 10-Y Risk for hard ASCVD (percent)

References

D’agostino, R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M. and Kannel, W.B., 2008. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation, 117(6), pp.743-753.

Examples

library(CVrisk)
ascvd_10y_frs_simple(
  gender = "male", age = 55,
  bmi = 30, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)

# 16.7

PREVENT 10-year ASCVD risk score

Description

Computes 10-year risk for ASCVD (atherosclerotic cardiovascular disease) using the American Heart Association PREVENT equations (2023).

Usage

ascvd_10y_prevent(
  gender = c("male", "female"),
  age,
  sbp,
  bp_med,
  totchol,
  hdl,
  statin,
  diabetes,
  smoker,
  egfr,
  bmi,
  hba1c = NULL,
  uacr = NULL,
  zip = NULL,
  model = "auto",
  ...
)

Arguments

gender

patient gender (male, female)

age

patient age (years), between 30 and 79

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

totchol

Total cholesterol (mg/dL)

hdl

HDL cholesterol (mg/dL)

statin

Patient is on a statin (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

egfr

Estimated glomerular filtration rate (mL/min/1.73m2)

bmi

Body mass index (kg/m2)

hba1c

Glycated hemoglobin (HbA1c) in percent (optional)

uacr

Urine albumin-to-creatinine ratio in mg/g (optional)

zip

ZIP code for Social Deprivation Index (optional)

model

PREVENT model variant to use: "auto" (default, selects based on available data), "base", "hba1c", "uacr", "sdi", or "full"

...

Additional predictors can be passed and will be ignored

Value

10-year ASCVD risk estimate (percent)

References

Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, Go AS, Gutierrez OM, Hwang SJ, Jassal SK, Kovesdy CP, Lloyd-Jones DM, Shlipak MG, Palaniappan LP, Sperling L, Virani SS, Tuttle K, Neeland IJ, Chow SL, Rangaswami J, Pencina MJ, Ndumele CE, Coresh J; Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group. Development and Validation of the American Heart Association's PREVENT Equations. Circulation. 2024 Feb 6;149(6):430-449.

Examples

library(CVrisk)
# Base model (default when model = "auto" and no optional predictors provided)
ascvd_10y_prevent(
  gender = "female", age = 50,
  sbp = 160, bp_med = 1,
  totchol = 200, hdl = 45,
  statin = 0, diabetes = 1, smoker = 0,
  egfr = 90, bmi = 35
)

# Explicitly specify base model
ascvd_10y_prevent(
  gender = "female", age = 50,
  sbp = 160, bp_med = 1,
  totchol = 200, hdl = 45,
  statin = 0, diabetes = 1, smoker = 0,
  egfr = 90, bmi = 35,
  model = "base"
)

# Auto model with HbA1c (will use hba1c model variant)
ascvd_10y_prevent(
  gender = "male", age = 55,
  sbp = 140, bp_med = 0,
  totchol = 213, hdl = 50,
  statin = 0, diabetes = 0, smoker = 0,
  egfr = 90, bmi = 30,
  hba1c = 6.5
)

PREVENT 30-year ASCVD risk score

Description

Computes 30-year risk for ASCVD (atherosclerotic cardiovascular disease) using the American Heart Association PREVENT equations (2023).

Usage

ascvd_30y_prevent(
  gender = c("male", "female"),
  age,
  sbp,
  bp_med,
  totchol,
  hdl,
  statin,
  diabetes,
  smoker,
  egfr,
  bmi,
  hba1c = NULL,
  uacr = NULL,
  zip = NULL,
  model = "auto",
  ...
)

Arguments

gender

patient gender (male, female)

age

patient age (years), between 30 and 79

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

totchol

Total cholesterol (mg/dL)

hdl

HDL cholesterol (mg/dL)

statin

Patient is on a statin (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

egfr

Estimated glomerular filtration rate (mL/min/1.73m2)

bmi

Body mass index (kg/m2)

hba1c

Glycated hemoglobin (HbA1c) in percent (optional)

uacr

Urine albumin-to-creatinine ratio in mg/g (optional)

zip

ZIP code for Social Deprivation Index (optional)

model

PREVENT model variant to use: "auto" (default, selects based on available data), "base", "hba1c", "uacr", "sdi", or "full"

...

Additional predictors can be passed and will be ignored

Value

30-year ASCVD risk estimate (percent)

References

Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, Go AS, Gutierrez OM, Hwang SJ, Jassal SK, Kovesdy CP, Lloyd-Jones DM, Shlipak MG, Palaniappan LP, Sperling L, Virani SS, Tuttle K, Neeland IJ, Chow SL, Rangaswami J, Pencina MJ, Ndumele CE, Coresh J; Chronic Kidney Disease Prognosis Consortium and the American Heart Association Cardiovascular-Kidney-Metabolic Science Advisory Group. Development and Validation of the American Heart Association's PREVENT Equations. Circulation. 2024 Feb 6;149(6):430-449.

Examples

library(CVrisk)
# Base model (default when model = "auto" and no optional predictors provided)
ascvd_30y_prevent(
  gender = "female", age = 50,
  sbp = 160, bp_med = 1,
  totchol = 200, hdl = 45,
  statin = 0, diabetes = 1, smoker = 0,
  egfr = 90, bmi = 35
)

# Explicitly specify base model
ascvd_30y_prevent(
  gender = "male", age = 45,
  sbp = 130, bp_med = 0,
  totchol = 200, hdl = 50,
  statin = 0, diabetes = 0, smoker = 1,
  egfr = 95, bmi = 28,
  model = "base"
)

# Auto model with UACR (will use uacr model variant)
ascvd_30y_prevent(
  gender = "male", age = 55,
  sbp = 140, bp_med = 0,
  totchol = 213, hdl = 50,
  statin = 0, diabetes = 0, smoker = 0,
  egfr = 90, bmi = 30,
  uacr = 25
)

Model coefficients for ASCVD 10y ACC/AHA model

Description

A data set containing the 2013 ACC/AHA ASCVD 10-year risk pooled cohort coefficients

Usage

ascvd_pooled_coef

Format

A data frame with 4 obs. and 17 variables:

race

Patient race, either white or aa

gender

Patient gender, either female or male

ln_age

Natural log of patient age

ln_age_squared

Natural log of patient age in years, squared

ln_totchol

Natural log of total cholesterol level

ln_age_totchol

Natural log of combined age and total cholesterol

ln_hdl

Natural log of HDL level

ln_age_hdl

Natural log of HDL and age

ln_treated_sbp

Natural log of treated systolic blood pressure

ln_age_treated_sbp

Natural log of treated systolic blood pressure and age

ln_untreated_sbp

Natural log of untreated systolic blood pressure

ln_age_untreated_sbp

Natural log of untreated systolic blood pressure and age

smoker

Smoking status

ln_age_smoker

Natural log of smoking status and age

diabetes

Diabetes status

group_mean

Grouped mean

baseline_survival

Baseline survival

References

Goff, David C., et al. "2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines." Journal of the American College of Cardiology 63.25 Part B (2014): 2935-2959.

MESA 2015 CHD risk score

Description

Computes 10-year risk for hard coronary heart disease (CHD) event (defined as first occurrence of myocardial infarction (MI), resuscitated cardiac arrest, CHD death, or revascularization with prior or concurrent adjudicated angina).

Usage

chd_10y_mesa(
  race = "white",
  gender = c("male", "female"),
  age,
  totchol = NA,
  hdl = NA,
  lipid_med = NA,
  sbp = NA,
  bp_med = NA,
  smoker = NA,
  diabetes = NA,
  fh_heartattack = NA,
  ...
)

Arguments

race

patient race/ethnicity (white, aa, chinese, or hispanic)

gender

patient gender (male, female)

age

patient age (years), risk computed for 45-85 year olds

totchol

Total cholesterol (mg/dL)

hdl

HDL cholesterol (mg/dL)

lipid_med

Patient is on a hyperlipidemic medication (1=Yes, 0=No)

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

fh_heartattack

Family history of heart attacks (parents, siblings ,or children) (1=Yes, 0=No)

...

Additional predictors can be passed and will be ignored

Value

Estimated 10-Y Risk for hard CAD event (percent)

References

McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study). J Am Coll Cardiol. 2015;66(15):1643-1653. doi:10.1016/j.jacc.2015.08.035

Examples

library(CVrisk)
chd_10y_mesa(
  race = "aa", gender = "male", age = 55,
  totchol = 213, hdl = 50, sbp = 140, lipid_med = 0,
  bp_med = 1, smoker = 0, diabetes = 0, fh_heartattack = 0
)

MESA 2015 CHD risk score with CAC

Description

Computes 10-year risk for hard coronary heart disease (CHD) event (defined as first occurrence of myocardial infarction (MI), resuscitated cardiac arrest, CHD death, or revascularization with prior or concurrent adjudicated angina). Includes coronary artery calcification score for more precise estimate of risk

Usage

chd_10y_mesa_cac(
  race = "white",
  gender = c("male", "female"),
  age,
  totchol = NA,
  hdl = NA,
  lipid_med = NA,
  sbp = NA,
  bp_med = NA,
  smoker = NA,
  diabetes = NA,
  fh_heartattack = NA,
  cac = NA,
  ...
)

Arguments

race

patient race/ethnicity (white, aa, chinese, or hispanic)

gender

patient gender (male, female)

age

patient age (years), risk computed for 45-85 year olds

totchol

Total cholesterol (mg/dL)

hdl

HDL cholesterol (mg/dL)

lipid_med

Patient is on a hyperlipidemic medication (1=Yes, 0=No)

sbp

Systolic blood pressure (mm Hg)

bp_med

Patient is on a blood pressure medication (1=Yes, 0=No)

smoker

Current smoker (1=Yes, 0=No)

diabetes

Diabetes (1=Yes, 0=No)

fh_heartattack

Family history of heart attacks (parents, siblings ,or children) (1=Yes, 0=No)

cac

Coronary artery calcification (Agatston units)

...

Additional predictors can be passed and will be ignored

Value

Estimated 10-Y Risk for hard CAD event (percent)

References

McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study). J Am Coll Cardiol. 2015;66(15):1643-1653. doi:10.1016/j.jacc.2015.08.035

Examples

library(CVrisk)
chd_10y_mesa_cac(
  race = "aa", gender = "male", age = 55,
  totchol = 213, hdl = 50, sbp = 140, lipid_med = 0,
  bp_med = 1, smoker = 0, diabetes = 0, fh_heartattack = 0, cac = 0
)

Compute multiple CV risk scores

Description

Compute multiple CV risk scores

Usage

compute_CVrisk(
  df,
  scores = c("ascvd_10y_accaha", "ascvd_10y_frs", "ascvd_10y_frs_simple", "chd_10y_mesa",
    "chd_10y_mesa_cac"),
  age,
  gender,
  race = NULL,
  sbp = NULL,
  bmi = NULL,
  hdl = NULL,
  totchol = NULL,
  bp_med = NULL,
  smoker = NULL,
  diabetes = NULL,
  lipid_med = NULL,
  statin = NULL,
  egfr = NULL,
  fh_heartattack = NULL,
  cac = NULL,
  ...
)

Arguments

df

input dataframe

scores

scores to compute, default is all scores

age

patient age in years (required for all scores)

gender

patient gender (male or female)

race

character string for patient race (white, aa, other) column

sbp

character string of systolic blood pressure (in mm Hg) column

bmi

character string of Body mass index (kg/m2) column

hdl

character string of HDL column

totchol

character string of total cholesterol column

bp_med

character string of blood pressure medication column

smoker

character string of smoking status column

diabetes

character string of diabetes status column

lipid_med

character string of lipid medication column (used as statin if statin not provided)

statin

character string of statin medication column (takes precedence over lipid_med)

egfr

character string of estimated glomerular filtration rate column

fh_heartattack

character string of fh of heart attack status column

cac

character string of cac column

...

Additional arguments to pass to score functions (e.g., model parameter for PREVENT scores)

Value

input data frame with risk score results appended as columns

Examples


library(CVrisk)
# Compute traditional risk scores
compute_CVrisk(sample_data,
  scores = c("ascvd_10y_accaha", "ascvd_10y_frs", "ascvd_10y_frs_simple",
             "chd_10y_mesa", "chd_10y_mesa_cac", "ascvd_10y_prevent"),
  age = "age", race = "race", gender = "gender", bmi = "BMI", sbp = "sbp",
  hdl = "hdl", totchol = "totchol", bp_med = "bp_med", smoker = "smoker",
  diabetes = "diabetes", lipid_med = "lipid_med", egfr = "egfr",
  fh_heartattack = "fh_heartattack", cac = "cac"
)

Model coefficients for ASCVD 10y FRS model

Description

A data set containing the Framingham risk score coefficients (full model with lab features)

Usage

frs_coef

Format

A data frame with 2 obs. and 10 variables:

gender

Patient gender, either female or male

ln_age

Natural log of patient age

ln_totchol

Natural log of total cholesterol level

ln_hdl

Natural log of HDL level

ln_untreated_sbp

Natural log of untreated systolic blood pressure

ln_treated_sbp

Natural log of treated systolic blood pressure

smoker

Smoking status

diabetes

Diabetes status

group_mean

Grouped mean

baseline_survival

Baseline survival

References

D’agostino, R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M. and Kannel, W.B., 2008. General cardiovascular risk profile for use in primary care. Circulation, 117(6), pp.743-753.

Model coefficients for ASCVD 10y FRS simple model

Description

A data set containing the Framingham risk score coefficients (simple model without lab features)

Usage

frs_simple_coef

Format

A data frame with 2 obs. and 10 variables:

gender

Patient gender, either female or male

ln_age

Natural log of patient age (years)

ln_bmi

Natural log of body mass index kg/m2

ln_untreated_sbp

Natural log of untreated systolic blood pressure

ln_treated_sbp

Natural log of treated systolic blood pressure

smoker

Smoking status

diabetes

Diabetes status

group_mean

Grouped mean

baseline_survival

Baseline survival

References

D’agostino, R.B., Vasan, R.S., Pencina, M.J., Wolf, P.A., Cobain, M., Massaro, J.M. and Kannel, W.B., 2008. General cardiovascular risk profile for use in primary care. Circulation, 117(6), pp.743-753.

Generate sample cardiovascular risk data

Description

Creates a data frame with randomly generated patient data suitable for testing cardiovascular risk calculations. The function generates realistic ranges for all standard cardiovascular risk factors.

Usage

make_sample_data(n = 100)

Arguments

n

Number of rows to generate (default: 100)

Value

A data frame with n rows and the following columns:

id

Sequential patient identifier (1 to n)

age

Patient age in years (30-79)

sex

Sex at birth ("female" or "male")

race

Patient race ("white", "aa", or "other")

sbp

Systolic blood pressure in mm Hg (90-200)

bp_med

Blood pressure medication status (TRUE/FALSE)

totchol

Total cholesterol in mg/dL (130-320)

hdl

HDL cholesterol in mg/dL (20-100)

lipid_med

Lipid medication status (TRUE/FALSE)

diabetes

Diabetes status (TRUE/FALSE)

smoker

Smoking status (TRUE/FALSE)

egfr

Estimated glomerular filtration rate in mL/min/1.73m2 (15-140)

bmi

Body mass index in kg/m2 (18.5-39.9)

hba1c

Hemoglobin A1c percentage (4.5-15.0 or NA)

uacr

Urine albumin-to-creatinine ratio in mg/g (0.1-25000 or NA)

zip

ZIP code (30 valid codes or NA)

Examples

library(CVrisk)
# Generate default 100 rows
sample_data <- make_sample_data()

# Generate 50 rows
sample_data_50 <- make_sample_data(n = 50)

# Use with compute_CVrisk
## Not run: 
data <- make_sample_data(n = 10)
result <- compute_CVrisk(
  data,
  scores = "ascvd_10y_accaha",
  age = "age",
  gender = "sex",
  race = "race",
  sbp = "sbp",
  totchol = "totchol",
  hdl = "hdl",
  bp_med = "bp_med",
  smoker = "smoker",
  diabetes = "diabetes"
)

## End(Not run)

mesa_cac_coef

Description

A data set containing the MESA risk score coefficients (model with CAC)

Usage

mesa_cac_coef

Format

A data frame with 1 obs. and 15 variables:

age

Coefficient for age

gender_male

Coefficient for male gender

race_chinese

Coefficient for Chinese race

race_aa

Coefficient for African American race

race_hispanic

Coefficient for Hispanic race

diabetes

Coefficient for diabetes status

smoker

Coefficient for current smoker

totchol

Coefficient for total cholesterol level

hdl

Coefficient for HDL level

hld_med

Coefficient for antihyperlipidemic medication

sbp

Coefficient for systolic blood pressure

bp_med

Coefficient for antihypertensive medication

fh_heartattack

Coefficient for family history of heart attacks

log1p_cac

Coefficient for ln(coronary artery calcification (units)+1)

baseline_survival

Baseline survival

References

McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study). J Am Coll Cardiol. 2015;66(15):1643-1653. doi:10.1016/j.jacc.2015.08.035

mesa_coef

Description

A data set containing the MESA risk score coefficients (model without CAC)

Usage

mesa_coef

Format

A data frame with 1 obs. and 14 variables:

age

Coefficient for age

gender_male

Coefficient for male gender

race_chinese

Coefficient for Chinese race

race_aa

Coefficient for African American race

race_hispanic

Coefficient for Hispanic race

diabetes

Coefficient for diabetes status

smoker

Coefficient for current smoker

totchol

Coefficient for total cholesterol level

hdl

Coefficient for HDL level

hld_med

Coefficient for antihyperlipidemic medication

sbp

Coefficient for systolic blood pressure

bp_med

Coefficient for antihypertensive medication

fh_heartattack

Coefficient for family history of heart attacks

baseline_survival

Baseline survival

References

McClelland RL, Jorgensen NW, Budoff M, et al. 10-Year Coronary Heart Disease Risk Prediction Using Coronary Artery Calcium and Traditional Risk Factors: Derivation in the MESA (Multi-Ethnic Study of Atherosclerosis) With Validation in the HNR (Heinz Nixdorf Recall) Study and the DHS (Dallas Heart Study). J Am Coll Cardiol. 2015;66(15):1643-1653. doi:10.1016/j.jacc.2015.08.035

Sample patient data

Description

A data set containing sample patient data

Usage

sample_data

Format

A data frame with 3 obs. and 10 variables:

age

age in years

gender

Patient gender

race

race

BMI

Body mass index (kg/m2)

sbp

systolic blood pressure

hdl

HDL

totchol

Total cholesterol

bp_med

Patient is on blood pressure medication

smoker

Smoking status

diabetes

Diabetes status

lipid_med

Patient is on hyperlipidemic medication

fh_heartattack

Family history of heart attack

cac

Coronary artery calcification score

egfr

Estimated glomerular filtration rate (mL/min/1.73m2)