Package 'CVrisk' reference manual

Title:	Compute Risk Scores for Cardiovascular Diseases
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>.
Authors:	Victor Castro [aut, cre]
Maintainer:	Victor Castro <[email protected]>
License:	GPL-3
Version:	1.1.1
Built:	2024-11-11 03:47:23 UTC
Source:	https://github.com/vcastro/cvrisk

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,
  ...
)
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
)
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,
  ...
)
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
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,
  ...
)
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

Examples

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

# 16.7
library(CVrisk)
ascvd_10y_frs_simple(
  gender = "male", age = 55,
  bmi = 30, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)

# 16.7

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
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

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,
  ...
)
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
)
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

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,
  ...
)
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

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
)
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,
  sbp = NULL,
  bmi = NULL,
  hdl = NULL,
  totchol = NULL,
  bp_med = NULL,
  smoker = NULL,
  diabetes = NULL,
  lipid_med = NULL,
  fh_heartattack = NULL,
  cac = NULL
)
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,
  sbp = NULL,
  bmi = NULL,
  hdl = NULL,
  totchol = NULL,
  bp_med = NULL,
  smoker = NULL,
  diabetes = NULL,
  lipid_med = 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
`fh_heartattack`	character string of fh of heart attack status column
`cac`	character string of cac column

Value

input data frame with risk score results appended as columns

Examples


library(CVrisk)
compute_CVrisk(sample_data,
  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",
  fh_heartattack = "fh_heartattack", cac = "cac"
)
library(CVrisk)
compute_CVrisk(sample_data,
  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",
  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
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
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

mesa_cac_coef

Description

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

Usage

mesa_cac_coef
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

mesa_coef

Description

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

Usage

mesa_coef
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

Sample patient data

Description

A data set containing sample patient data

Usage

sample_data
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

Package 'CVrisk'

Help Index

ACC/AHA 2013 ASCVD risk score

Description

Usage

Arguments

Value

References

Examples

Framingham 2008 ASCVD risk score (with lab measurement)

Description

Usage

Arguments

Value

References

Examples

Framingham 2008 ASCVD risk score (no lab measurement)

Description

Usage

Arguments

Value

References

Examples

Model coefficients for ASCVD 10y ACC/AHA model

Description

Usage

Format

References

MESA 2015 CHD risk score

Description

Usage

Arguments

Value

References

Examples

MESA 2015 CHD risk score with CAC

Description

Usage

Arguments

Value

References

Examples

Compute multiple CV risk scores

Description

Usage

Arguments

Value

Examples

Model coefficients for ASCVD 10y FRS model

Description

Usage

Format

References

Model coefficients for ASCVD 10y FRS simple model

Description

Usage

Format

References

mesa_cac_coef

Description

Usage

Format

References

mesa_coef

Description

Usage

Format

References

Sample patient data

Description

Usage

Format