CVrisk

https://vcastro.github.io/CVrisk/
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CVrisk is an R package that calculates various cardiovascular disease risk scores including...
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### Install CVrisk Package

Source: https://vcastro.github.io/CVrisk/index.html

Install the CVrisk package from CRAN or the development version from GitHub.

```r
install.packages("CVrisk")
```

```r
devtools::install_github("vcastro/CVrisk")
```

--------------------------------

### Calculate ASCVD 10-Year Risk Score Example

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_frs_simple.html

Example of how to use the `ascvd_10y_frs_simple` function to calculate the 10-year ASCVD risk for a male patient. Ensure the CVrisk library is loaded before execution.

```R
library(CVrisk)
ascvd_10y_frs_simple(
  gender = "male", age = 55,
  bmi = 30, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)
#> [1] 16.75

# 16.7
```

--------------------------------

### Example Usage of ASCVD 10-Year Risk Calculation

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_accaha.html

Demonstrates how to use the `ascvd_10y_accaha` function with specific patient data to estimate the 10-year risk of a hard ASCVD event. Ensure the CVrisk library is loaded before use.

```R
library(CVrisk)
ascvd_10y_accaha(
  race = "aa", gender = "male", age = 55,
  totchol = 213, hdl = 50, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)
#> [1] 7.95

```

--------------------------------

### Calculate 10-year CHD risk with MESA and CAC

Source: https://vcastro.github.io/CVrisk/reference/chd_10y_mesa_cac.html

Example of how to use the `chd_10y_mesa_cac` function to calculate the 10-year risk for a specific patient profile. Ensure the CVrisk library is loaded before use.

```R
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
)
#> [1] 2.95

```

--------------------------------

### Load FRS Coefficients Data

Source: https://vcastro.github.io/CVrisk/reference/frs_coef.html

This snippet shows how to access the `frs_coef` data frame, which contains the coefficients for the Framingham Risk Score model. No specific setup or imports are required as it's a data object.

```r
frs_coef
```

--------------------------------

### Handle Missing Predictor Data

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

The `ascvd_10y_prevent` function returns NA for patients with missing required predictors, such as eGFR in this example.

```r
# Patient with missing eGFR
risk_missing <- ascvd_10y_prevent(
  gender = "male",
  age = 55,
  sbp = 140,
  bp_med = 0,
  totchol = 213,
  hdl = 50,
  statin = 0,
  diabetes = 0,
  smoker = 0,
  egfr = NA,          # Missing
  bmi = 30
)

print(risk_missing)   # Returns NA
```

--------------------------------

### Use Sample Data with compute_CVrisk

Source: https://vcastro.github.io/CVrisk/reference/make_sample_data.html

Demonstrates how to use the generated sample data with the `compute_CVrisk` function. Ensure the necessary arguments are mapped correctly to the sample data columns.

```R
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"
)
```

--------------------------------

### make_sample_data()

Source: https://vcastro.github.io/CVrisk/reference/index.html

Generate sample cardiovascular risk data.

```APIDOC
## make_sample_data()

### Description
Generate sample cardiovascular risk data.

### Function Signature
make_sample_data()
```

--------------------------------

### sample_data

Source: https://vcastro.github.io/CVrisk/reference/index.html

Sample patient data.

```APIDOC
## sample_data

### Description
Sample patient data.
```

--------------------------------

### frs_simple_coef

Source: https://vcastro.github.io/CVrisk/reference/index.html

Model coefficients for ASCVD 10y FRS simple model.

```APIDOC
## frs_simple_coef

### Description
Model coefficients for ASCVD 10y FRS simple model.
```

--------------------------------

### ASCVD Risk with Optional Predictors (HbA1c)

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Demonstrates calculating ASCVD risk when optional predictors like HbA1c are available. The 'auto' model variant intelligently selects the appropriate model.

```APIDOC
## Using Optional Predictors (HbA1c)

### Description
Demonstrates calculating ASCVD risk when optional predictors like HbA1c are available. The 'auto' model variant intelligently selects the appropriate model.

### Function Signature
`ascvd_10y_prevent(gender, age, sbp, bp_med, totchol, hdl, statin, diabetes, smoker, egfr, bmi, hba1c, model = "auto")`

### Parameters
* **gender** (string) - Required - "male" or "female"
* **age** (numeric) - Required - Age in years
* **sbp** (numeric) - Required - Systolic blood pressure (mm Hg)
* **bp_med** (numeric) - Required - On blood pressure medication (1=Yes, 0=No)
* **totchol** (numeric) - Required - Total cholesterol (mg/dL)
* **hdl** (numeric) - Required - HDL cholesterol (mg/dL)
* **statin** (numeric) - Required - On statin therapy (1=Yes, 0=No)
* **diabetes** (numeric) - Required - Has diabetes (1=Yes, 0=No)
* **smoker** (numeric) - Required - Current smoker (1=Yes, 0=No)
* **egfr** (numeric) - Required - Estimated Glomerular Filtration Rate (mL/min/1.73m²)
* **bmi** (numeric) - Required - Body Mass Index (kg/m²)
* **hba1c** (numeric) - Optional - Glycated hemoglobin (%). If provided, the model will automatically use the 'hba1c' or 'full' variant if other optional predictors are also present.
* **model** (string) - Optional - Model variant to use. Defaults to "auto". Can be "base", "auto", "hba1c", "uacr", "sdi", "full".

### Request Example
```R
# With HbA1c - model will automatically select "hba1c" variant
risk_with_hba1c <- ascvd_10y_prevent(
  gender = "male",
  age = 55,
  sbp = 140,
  bp_med = 0,
  totchol = 213,
  hdl = 50,
  statin = 0,
  diabetes = 1,
  smoker = 0,
  egfr = 90,
  bmi = 30,
  hba1c = 7.5,
  model = "auto"
)
```

### Response
* **risk_with_hba1c** (numeric) - The calculated ASCVD risk as a percentage, using the appropriate model variant based on provided data.
```

--------------------------------

### mesa_coef

Source: https://vcastro.github.io/CVrisk/reference/index.html

mesa_coef.

```APIDOC
## mesa_coef

### Description
mesa_coef.
```

--------------------------------

### mesa_cac_coef

Source: https://vcastro.github.io/CVrisk/reference/index.html

mesa_cac_coef.

```APIDOC
## mesa_cac_coef

### Description
mesa_cac_coef.
```

--------------------------------

### Access Sample Patient Data

Source: https://vcastro.github.io/CVrisk/reference/sample_data.html

Load the sample patient data into your R environment. This dataset contains observations for 3 patients and 10 variables relevant to cardiovascular risk assessment.

```r
sample_data
```

--------------------------------

### Full Model Calculation with All Optional Predictors

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Utilize the 'auto' model setting to automatically include all available optional predictors for the most comprehensive risk assessment.

```r
# Full model with all optional predictors
risk_full <- ascvd_10y_prevent(
  gender = "female",
  age = 58,
  sbp = 145,
  bp_med = 1,
  totchol = 210,
  hdl = 52,
  statin = 1,
  diabetes = 1,
  smoker = 0,
  egfr = 80,
  bmi = 31,
  hba1c = 7.2,
  uacr = 100,
  zip = "10001",
  model = "auto"      # Will use full model
)
```

--------------------------------

### make_sample_data

Source: https://vcastro.github.io/CVrisk/reference/make_sample_data.html

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.

```APIDOC
## make_sample_data(n = 100)

### 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.

### Arguments

*   **n** (numeric) - Number of rows to generate (default: 100)

### Value
A data frame with `n` rows and the following columns:

*   **id** (integer) - Sequential patient identifier (1 to n)
*   **age** (numeric) - Patient age in years (30-79)
*   **sex** (character) - Sex at birth ("female" or "male")
*   **race** (character) - Patient race ("white", "aa", or "other")
*   **sbp** (numeric) - Systolic blood pressure in mm Hg (90-200)
*   **bp_med** (logical) - Blood pressure medication status (TRUE/FALSE)
*   **totchol** (numeric) - Total cholesterol in mg/dL (130-320)
*   **hdl** (numeric) - HDL cholesterol in mg/dL (20-100)
*   **lipid_med** (logical) - Lipid medication status (TRUE/FALSE)
*   **diabetes** (logical) - Diabetes status (TRUE/FALSE)
*   **smoker** (logical) - Smoking status (TRUE/FALSE)
*   **egfr** (numeric) - Estimated glomerular filtration rate in mL/min/1.73m2 (15-140)
*   **bmi** (numeric) - Body mass index in kg/m2 (18.5-39.9)
*   **hba1c** (numeric) - Hemoglobin A1c percentage (4.5-15.0 or NA)
*   **uacr** (numeric) - Urine albumin-to-creatinine ratio in mg/g (0.1-25000 or NA)
*   **zip** (character) - ZIP code (30 valid codes or NA)

### Examples

```R
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
if (FALSE) { # \dontrun{
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"
)
}
```
```

--------------------------------

### ascvd_10y_frs_simple()

Source: https://vcastro.github.io/CVrisk/reference/index.html

Framingham 2008 ASCVD risk score (no lab measurement).

```APIDOC
## ascvd_10y_frs_simple()

### Description
Framingham 2008 ASCVD risk score (no lab measurement).

### Function Signature
ascvd_10y_frs_simple()
```

--------------------------------

### Generate Sample Data

Source: https://vcastro.github.io/CVrisk/reference/make_sample_data.html

Generates a data frame with randomly generated patient data. Use this function to create test data for cardiovascular risk calculations.

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

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

--------------------------------

### 10-Year ASCVD Risk Calculation (Base Model)

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Calculates the 10-year ASCVD risk using the base PREVENT model. Requires standard risk factors.

```APIDOC
## Calculate 10-Year ASCVD Risk (Base Model)

### Description
Calculates the 10-year ASCVD risk using the base PREVENT model. Requires standard risk factors.

### Function Signature
`ascvd_10y_prevent(gender, age, sbp, bp_med, totchol, hdl, statin, diabetes, smoker, egfr, bmi, model = "auto")`

### Parameters
* **gender** (string) - Required - "male" or "female"
* **age** (numeric) - Required - Age in years
* **sbp** (numeric) - Required - Systolic blood pressure (mm Hg)
* **bp_med** (numeric) - Required - On blood pressure medication (1=Yes, 0=No)
* **totchol** (numeric) - Required - Total cholesterol (mg/dL)
* **hdl** (numeric) - Required - HDL cholesterol (mg/dL)
* **statin** (numeric) - Required - On statin therapy (1=Yes, 0=No)
* **diabetes** (numeric) - Required - Has diabetes (1=Yes, 0=No)
* **smoker** (numeric) - Required - Current smoker (1=Yes, 0=No)
* **egfr** (numeric) - Required - Estimated Glomerular Filtration Rate (mL/min/1.73m²)
* **bmi** (numeric) - Required - Body Mass Index (kg/m²)
* **model** (string) - Optional - Model variant to use. Defaults to "auto". Can be "base", "auto", "hba1c", "uacr", "sdi", "full".

### Request Example
```R
library(CVrisk)

risk_10y <- 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
)

print(risk_10y)
```

### Response
* **risk_10y** (numeric) - The calculated 10-year ASCVD risk as a percentage.
```

--------------------------------

### Compare PREVENT vs Framingham using compute_CVrisk()

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Compare the PREVENT risk score with the Framingham Risk Score using `compute_CVrisk()` on a patient dataset.

```r
# Compare PREVENT with Framingham Risk Score
results_frs <- compute_CVrisk(
  patient_data,
  scores = c("ascvd_10y_frs", "ascvd_10y_prevent"),
  age = "age",
  gender = "gender",
  sbp = "sbp",
  bp_med = "bp_med",
  totchol = "totchol",
  hdl = "hdl",
  diabetes = "diabetes",
  smoker = "smoker",
  egfr = "egfr",
  bmi = "bmi"
)

print(results_frs)
```

--------------------------------

### ASCVD 10-year risk calculation (Base Model)

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_prevent.html

Calculates the 10-year ASCVD risk using the base PREVENT model. This is the default behavior when `model = "auto"` and no optional predictors are provided.

```R
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
)
#> [1] 9.2
```

--------------------------------

### ascvd_10y_frs()

Source: https://vcastro.github.io/CVrisk/reference/index.html

Framingham 2008 ASCVD risk score (with lab measurement).

```APIDOC
## ascvd_10y_frs()

### Description
Framingham 2008 ASCVD risk score (with lab measurement).

### Function Signature
ascvd_10y_frs()
```

--------------------------------

### frs_coef

Source: https://vcastro.github.io/CVrisk/reference/index.html

Model coefficients for ASCVD 10y FRS model.

```APIDOC
## frs_coef

### Description
Model coefficients for ASCVD 10y FRS model.
```

--------------------------------

### Explicitly Force Base Model

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Use `model = "base"` to ensure only the base predictors are used, even if optional predictors like HbA1c are provided.

```r
# Force base model even with optional predictors available
risk_base_only <- ascvd_10y_prevent(
  gender = "male",
  age = 55,
  sbp = 140,
  bp_med = 0,
  totchol = 213,
  hdl = 50,
  statin = 0,
  diabetes = 1,
  smoker = 0,
  egfr = 90,
  bmi = 30,
  hba1c = 7.5,        # Available but will be ignored
  model = "base"      # Explicitly use base model
)
```

--------------------------------

### Add HbA1c for Enhanced Risk Calculation

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

When HbA1c data is available, providing it to `ascvd_10y_prevent` with `model = "auto"` automatically selects and uses the 'hba1c' model variant for a more precise risk calculation.

```R
# With HbA1c - model will automatically select "hba1c" variant
risk_with_hba1c <- ascvd_10y_prevent(
  gender = "male",
  age = 55,
  sbp = 140,
  bp_med = 0,
  totchol = 213,
  hdl = 50,
  statin = 0,
  diabetes = 1,
  smoker = 0,
  egfr = 90,
  bmi = 30,
  hba1c = 7.5,         # Glycated hemoglobin (%)
  model = "auto"       # Automatically uses hba1c model
)
```

--------------------------------

### Calculate Risk with Social Deprivation Index (SDI)

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Incorporate the Social Deprivation Index by providing a ZIP code to account for socioeconomic factors.

```r
# With ZIP code for SDI
risk_with_sdi <- ascvd_10y_prevent(
  gender = "male",
  age = 52,
  sbp = 135,
  bp_med = 0,
  totchol = 195,
  hdl = 48,
  statin = 0,
  diabetes = 0,
  smoker = 0,
  egfr = 88,
  bmi = 29,
  zip = "02114"       # Boston, MA
)
```

--------------------------------

### ASCVD 10-year risk calculation (Explicit Base Model)

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_prevent.html

Explicitly specifies the use of the base PREVENT model for ASCVD risk calculation. This ensures the base model is used regardless of other parameters.

```R
# 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"
)
#> [1] 9.2
```

--------------------------------

### Compare PREVENT vs ACC/AHA using compute_CVrisk()

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Use `compute_CVrisk()` to calculate and compare the PREVENT 10-year risk score against the ACC/AHA 2013 score for a dataset.

```r
# Create sample patient data
patient_data <- data.frame(
  age = c(50, 55, 60),
  gender = c("female", "male", "female"),
  race = c("white", "aa", "white"),
  sbp = c(160, 140, 150),
  bp_med = c(1, 0, 1),
  totchol = c(200, 213, 220),
  hdl = c(45, 50, 55),
  statin = c(0, 0, 1),
  diabetes = c(1, 0, 1),
  smoker = c(0, 0, 0),
  egfr = c(90, 90, 75),
  bmi = c(35, 30, 32)
)

# Compare PREVENT 10-year with ACC/AHA 2013
results <- compute_CVrisk(
  patient_data,
  scores = c("ascvd_10y_accaha", "ascvd_10y_prevent"),
  age = "age",
  gender = "gender",
  race = "race",
  sbp = "sbp",
  bp_med = "bp_med",
  totchol = "totchol",
  hdl = "hdl",
  statin = "statin",
  diabetes = "diabetes",
  smoker = "smoker",
  egfr = "egfr",
  bmi = "bmi"
)

# View results
print(results[, c("age", "gender", "ascvd_10y_accaha", "ascvd_10y_prevent")])
```

--------------------------------

### compute_CVrisk()

Source: https://vcastro.github.io/CVrisk/reference/index.html

Compute multiple CV risk scores.

```APIDOC
## compute_CVrisk()

### Description
Compute multiple CV risk scores.

### Function Signature
compute_CVrisk()
```

--------------------------------

### Calculate Multiple Scores Including 30-Year Risk

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Calculate a comprehensive set of risk scores, including 10-year and 30-year PREVENT scores alongside legacy scores, using `compute_CVrisk()`.

```r
# Calculate multiple scores including 30-year risk
comprehensive_results <- compute_CVrisk(
  patient_data,
  scores = c("ascvd_10y_accaha", "ascvd_10y_frs", 
             "ascvd_10y_prevent", "ascvd_30y_prevent"),
  age = "age",
  gender = "gender",
  race = "race",
  sbp = "sbp",
  bp_med = "bp_med",
  totchol = "totchol",
  hdl = "hdl",
  statin = "statin",
  diabetes = "diabetes",
  smoker = "smoker",
  egfr = "egfr",
  bmi = "bmi"
)

# View side-by-side comparison
print(comprehensive_results[, c("age", "gender", 
                                "ascvd_10y_accaha", 
                                "ascvd_10y_frs",
                                "ascvd_10y_prevent", 
                                "ascvd_30y_prevent")])
```

--------------------------------

### Access FRS Simple Coefficients Data

Source: https://vcastro.github.io/CVrisk/reference/frs_simple_coef.html

This R code snippet shows how to access the `frs_simple_coef` dataset, which contains the coefficients for the Framingham Risk Score simple model.

```r
frs_simple_coef
```

--------------------------------

### ASCVD 30-year PREVENT risk calculation

Source: https://vcastro.github.io/CVrisk/reference/ascvd_30y_prevent.html

Use this function to calculate the 30-year ASCVD risk. The default 'auto' model selects the appropriate PREVENT variant based on provided optional predictors. Ensure all required arguments are supplied.

```R
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
)
#> [1] 35.4
```

```R
# 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"
)
#> [1] 13.8
```

```R
# 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
)
#> [1] 19.8
```

--------------------------------

### 30-Year ASCVD Risk Calculation

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Calculates the 30-year ASCVD risk using the PREVENT model. Suitable for younger individuals.

```APIDOC
## Calculate 30-Year ASCVD Risk

### Description
Calculates the 30-year ASCVD risk using the PREVENT model. Suitable for younger individuals.

### Function Signature
`ascvd_30y_prevent(gender, age, sbp, bp_med, totchol, hdl, statin, diabetes, smoker, egfr, bmi, model = "auto")`

### Parameters
* **gender** (string) - Required - "male" or "female"
* **age** (numeric) - Required - Age in years
* **sbp** (numeric) - Required - Systolic blood pressure (mm Hg)
* **bp_med** (numeric) - Required - On blood pressure medication (1=Yes, 0=No)
* **totchol** (numeric) - Required - Total cholesterol (mg/dL)
* **hdl** (numeric) - Required - HDL cholesterol (mg/dL)
* **statin** (numeric) - Required - On statin therapy (1=Yes, 0=No)
* **diabetes** (numeric) - Required - Has diabetes (1=Yes, 0=No)
* **smoker** (numeric) - Required - Current smoker (1=Yes, 0=No)
* **egfr** (numeric) - Required - Estimated Glomerular Filtration Rate (mL/min/1.73m²)
* **bmi** (numeric) - Required - Body Mass Index (kg/m²)
* **model** (string) - Optional - Model variant to use. Defaults to "auto". Can be "base", "auto", "hba1c", "uacr", "sdi", "full".

### Request Example
```R
# Calculate 30-year risk for a 45-year-old male
risk_30y <- 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
)

print(risk_30y)
```

### Response
* **risk_30y** (numeric) - The calculated 30-year ASCVD risk as a percentage.
```

--------------------------------

### ascvd_pooled_coef

Source: https://vcastro.github.io/CVrisk/reference/index.html

Model coefficients for ASCVD 10y ACC/AHA model.

```APIDOC
## ascvd_pooled_coef

### Description
Model coefficients for ASCVD 10y ACC/AHA model.
```

--------------------------------

### Calculate 30-Year ASCVD Risk

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Use `ascvd_30y_prevent` for 30-year ASCVD risk estimation, suitable for younger individuals. It requires the same set of standard risk factors as the 10-year function.

```R
# Calculate 30-year risk for a 45-year-old male
risk_30y <- ascvd_30y_prevent(
  gender = "male",
  age = 45,
  sbp = 130,
  bp_med = 0,
  totchol = 200,
  hdl = 50,
  statin = 0,
  diabetes = 0,
  smoker = 1,          # Current smoker
  egfr = 95,
  bmi = 28
)

print(risk_30y)
```

--------------------------------

### ASCVD 10-year risk calculation (Auto Model with HbA1c)

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_prevent.html

Calculates ASCVD risk using the 'auto' model setting, which defaults to the HbA1c variant when the `hba1c` predictor is provided. This is useful for patients with diabetes.

```R
# 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
)
#> [1] 4.3
```

--------------------------------

### Calculate Risk with UACR

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Use the `uacr` parameter to include urine albumin-to-creatinine ratio for a more comprehensive kidney function assessment.

```r
# With UACR
risk_with_uacr <- ascvd_10y_prevent(
  gender = "female",
  age = 60,
  sbp = 150,
  bp_med = 1,
  totchol = 220,
  hdl = 55,
  statin = 1,
  diabetes = 1,
  smoker = 0,
  egfr = 75,
  bmi = 32,
  uacr = 150          # UACR in mg/g
)
```

--------------------------------

### Pass Model Parameter through compute_CVrisk()

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Specify the PREVENT model variant (e.g., 'base') directly within the `compute_CVrisk()` function for consistent calculations across multiple scores.

```r
# Force base model for all PREVENT calculations
results_base <- compute_CVrisk(
  patient_data,
  scores = c("ascvd_10y_prevent", "ascvd_30y_prevent"),
  age = "age",
  gender = "gender",
  sbp = "sbp",
  bp_med = "bp_med",
  totchol = "totchol",
  hdl = "hdl",
  statin = "statin",
  diabetes = "diabetes",
  smoker = "smoker",
  egfr = "egfr",
  bmi = "bmi",
  model = "base"      # Pass model parameter
)
```

--------------------------------

### ascvd_10y_accaha()

Source: https://vcastro.github.io/CVrisk/reference/index.html

ACC/AHA 2013 ASCVD risk score.

```APIDOC
## ascvd_10y_accaha()

### Description
ACC/AHA 2013 ASCVD risk score.

### Function Signature
ascvd_10y_accaha()
```

--------------------------------

### Access MESA Risk Score Coefficients

Source: https://vcastro.github.io/CVrisk/reference/mesa_coef.html

This snippet shows how to access the MESA risk score coefficients dataset in R. This dataset contains the model coefficients for calculating the MESA risk score without considering CAC.

```r
mesa_coef
```

--------------------------------

### ascvd_10y_accaha Function

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_accaha.html

This function computes the 10-year risk for hard ASCVD events. It takes patient demographics and clinical factors as input.

```APIDOC
## Function: ascvd_10y_accaha

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

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

### Arguments

*   **race** (string) - patient race (white, aa, other)
*   **gender** (string) - patient gender (male, female)
*   **age** (numeric) - patient age (years)
*   **totchol** (numeric) - Total cholesterol (mg/dL)
*   **hdl** (numeric) - HDL cholesterol (mg/dL)
*   **sbp** (numeric) - Systolic blood pressure (mm Hg)
*   **bp_med** (numeric) - Patient is on a blood pressure medication (1=Yes, 0=No)
*   **smoker** (numeric) - Current smoker (1=Yes, 0=No)
*   **diabetes** (numeric) - 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.

### Example
```R
library(CVrisk)
ascvd_10y_accaha(
  race = "aa", gender = "male", age = 55,
  totchol = 213, hdl = 50, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)
#> [1] 7.95
```
```

--------------------------------

### ascvd_10y_prevent()

Source: https://vcastro.github.io/CVrisk/reference/index.html

PREVENT 10-year ASCVD risk score.

```APIDOC
## ascvd_10y_prevent()

### Description
PREVENT 10-year ASCVD risk score.

### Function Signature
ascvd_10y_prevent()
```

--------------------------------

### ascvd_30y_prevent()

Source: https://vcastro.github.io/CVrisk/reference/index.html

PREVENT 30-year ASCVD risk score.

```APIDOC
## ascvd_30y_prevent()

### Description
PREVENT 30-year ASCVD risk score.

### Function Signature
ascvd_30y_prevent()
```

--------------------------------

### Access MESA CAC Coefficients

Source: https://vcastro.github.io/CVrisk/reference/mesa_cac_coef.html

This snippet shows how to access the mesa_cac_coef data set in R. This data set contains the coefficients for the MESA risk score model that includes CAC.

```r
mesa_cac_coef
```

--------------------------------

### ASCVD 10-Year Risk Score Function

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_frs.html

This function computes the 10-year risk for ASCVD events. Ensure all required arguments are provided.

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

```R
library(CVrisk)
ascvd_10y_frs(
  gender = "male", age = 55,
  hdl = 50, totchol = 213, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)
#> [1] 13.53

# 16.7

```

--------------------------------

### ASCVD 10-Year Risk Calculation Function Signature

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_accaha.html

Defines the function signature for calculating the 10-year ASCVD risk. It accepts race, gender, age, total cholesterol, HDL, systolic blood pressure, blood pressure medication status, smoker status, and diabetes status as primary arguments.

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

--------------------------------

### Compute Specific CVrisk Scores with PREVENT

Source: https://vcastro.github.io/CVrisk/index.html

Compute specified risk scores, including the PREVENT score, for a dataframe. Requires explicit score selection and mapping of columns for eGFR.

```r
compute_CVrisk(make_sample_data(100),
   scores = c("ascvd_10y_accaha", "ascvd_10y_prevent"),
   age = "age", gender = "sex", sbp = "sbp", 
   hdl = "hdl", totchol = "totchol", bmi = "bmi",
   bp_med = "bp_med", diabetes = "diabetes", smoker = "smoker",
   statin = "lipid_med", egfr = "egfr"
)
```

--------------------------------

### chd_10y_mesa()

Source: https://vcastro.github.io/CVrisk/reference/index.html

MESA 2015 CHD risk score.

```APIDOC
## chd_10y_mesa()

### Description
MESA 2015 CHD risk score.

### Function Signature
chd_10y_mesa()
```

--------------------------------

### Calculate 10-Year ASCVD Risks for Multiple Patients

Source: https://vcastro.github.io/CVrisk/articles/using-prevent-functions.html

Use the `ascvd_10y_prevent` function with vector inputs for patient demographics and clinical factors to calculate 10-year ASCVD risks for a cohort. The function returns a vector of risks.

```R
ages <- c(45, 50, 55, 60)
genders <- c("male", "female", "male", "female")
sbps <- c(130, 140, 150, 160)

risks <- ascvd_10y_prevent(
  gender = genders,
  age = ages,
  sbp = sbps,
  bp_med = c(0, 0, 1, 1),
  totchol = c(200, 210, 220, 230),
  hdl = c(50, 45, 55, 48),
  statin = c(0, 0, 0, 1),
  diabetes = c(0, 0, 1, 1),
  smoker = c(0, 1, 0, 0),
  egfr = c(95, 90, 85, 80),
  bmi = c(28, 30, 32, 34)
)

print(risks)  # Returns vector of risks
```

--------------------------------

### chd_10y_mesa_cac()

Source: https://vcastro.github.io/CVrisk/reference/index.html

MESA 2015 CHD risk score with CAC.

```APIDOC
## chd_10y_mesa_cac()

### Description
MESA 2015 CHD risk score with CAC.

### Function Signature
chd_10y_mesa_cac()
```

--------------------------------

### ascvd_10y_frs_simple Function

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_frs_simple.html

Calculates the 10-year ASCVD risk score based on provided patient demographics and clinical factors. It does not require lab measurements.

```APIDOC
## Function: ascvd_10y_frs_simple

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

### Arguments

*   **gender** (character) - Patient gender ('male' or 'female').
*   **age** (numeric) - Patient age in years (between 30 and 74).
*   **bmi** (numeric) - Body mass index in kg/m2.
*   **sbp** (numeric) - Systolic blood pressure in mm Hg.
*   **bp_med** (numeric) - Indicates if the patient is on blood pressure medication (1=Yes, 0=No).
*   **smoker** (numeric) - Indicates if the patient is a current smoker (1=Yes, 0=No).
*   **diabetes** (numeric) - Indicates if the patient has 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.

### Example
```R
library(CVrisk)
ascvd_10y_frs_simple(
  gender = "male", age = 55,
  bmi = 30, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)
# Expected output: [1] 16.75
```
```

--------------------------------

### ascvd_10y_frs Function

Source: https://vcastro.github.io/CVrisk/reference/ascvd_10y_frs.html

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

```APIDOC
## Function: ascvd_10y_frs

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

### Arguments

* **gender** (character) - patient gender (male, female)
* **age** (numeric) - patient age (years), between 30 and 74
* **hdl** (numeric) - HDL cholesterol (mg/dL)
* **totchol** (numeric) - Total cholesterol (mg/dL)
* **sbp** (numeric) - Systolic blood pressure (mm Hg)
* **bp_med** (numeric) - Patient is on a blood pressure medication (1=Yes, 0=No)
* **smoker** (numeric) - Current smoker (1=Yes, 0=No)
* **diabetes** (numeric) - 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.

### Example
```R
library(CVrisk)
ascvd_10y_frs(
  gender = "male", age = 55,
  hdl = 50, totchol = 213, sbp = 140,
  bp_med = 0, smoker = 0, diabetes = 0
)
#> [1] 13.53

# 16.7
```
```
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