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RiskScorescvd

R-CMD-check Connect

Chest pain? Calculate your cardiovascular risk score.

The goal of RiskScorescvd r package is to calculate the most commonly used cardiovascular risk scores.

We have developed five of the most commonly used risk scores with a dependency (ASCVD [PooledCohort]) making the following available:

NOTE: Troponin I values should be used. Additional functions for Troponin T are under development

Installation

You can install from CRAN with:

# Install from CRAN
install.packages("RiskScorescvd")

You can install the development version of RiskScorescvd from GitHub with:

# install.packages("devtools")
devtools::install_github("dvicencio/RiskScorescvd")

Required variables for each risk score

Example

This is a basic example of how the data set should look to calculate all risk scores available in the package:

library(RiskScorescvd)
#> Loading required package: PooledCohort
# Create a data frame or list with the necessary variables
 # Set the number of rows
 num_rows <- 100
 
 # Create a large dataset with 100 rows
  cohort_xx <- data.frame(
   typical_symptoms.num = as.numeric(sample(0:6, num_rows, replace = TRUE)),
   ecg.normal = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   abn.repolarisation = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   ecg.st.depression = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   Age = as.numeric(sample(30:80, num_rows, replace = TRUE)),
   diabetes = sample(c(1, 0), num_rows, replace = TRUE),
   smoker = sample(c(1, 0), num_rows, replace = TRUE),
   hypertension = sample(c(1, 0), num_rows, replace = TRUE),
   hyperlipidaemia = sample(c(1, 0), num_rows, replace = TRUE),
   family.history = sample(c(1, 0), num_rows, replace = TRUE),
   atherosclerotic.disease = sample(c(1, 0), num_rows, replace = TRUE),
   presentation_hstni = as.numeric(sample(10:100, num_rows, replace = TRUE)),
   Gender = sample(c("male", "female"), num_rows, replace = TRUE),
   sweating = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   pain.radiation = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   pleuritic = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   palpation = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   ecg.twi = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   second_hstni = as.numeric(sample(1:200, num_rows, replace = TRUE)),
   killip.class = as.numeric(sample(1:4, num_rows, replace = TRUE)),
   systolic.bp = as.numeric(sample(40:300, num_rows, replace = TRUE)),
   heart.rate = as.numeric(sample(0:300, num_rows, replace = TRUE)),
   creat = as.numeric(sample(0:4, num_rows, replace = TRUE)),
   cardiac.arrest = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   previous.pci = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   previous.cabg = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   aspirin = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
   number.of.episodes.24h = as.numeric(sample(0:20, num_rows, replace = TRUE)),
   total.chol = as.numeric(sample(5:100, num_rows, replace = TRUE)),
   total.hdl = as.numeric(sample(2:5, num_rows, replace = TRUE)),
   Ethnicity = sample(c("white", "black", "asian", "other"), num_rows, replace = TRUE)
 )

  str(cohort_xx)
#> 'data.frame':    100 obs. of  31 variables:
#>  $ typical_symptoms.num   : num  2 2 0 5 3 4 5 4 4 0 ...
#>  $ ecg.normal             : num  0 1 1 1 1 1 1 0 1 0 ...
#>  $ abn.repolarisation     : num  0 0 0 0 0 0 1 0 1 0 ...
#>  $ ecg.st.depression      : num  0 0 1 1 0 1 0 0 1 1 ...
#>  $ Age                    : num  52 39 41 44 40 80 77 43 80 61 ...
#>  $ diabetes               : num  0 0 0 0 0 0 0 0 0 1 ...
#>  $ smoker                 : num  1 0 1 1 1 0 0 1 0 1 ...
#>  $ hypertension           : num  1 0 0 1 1 1 1 1 1 0 ...
#>  $ hyperlipidaemia        : num  1 0 1 1 1 0 0 0 0 0 ...
#>  $ family.history         : num  0 1 0 0 0 0 1 1 0 1 ...
#>  $ atherosclerotic.disease: num  1 1 0 0 1 1 0 1 1 0 ...
#>  $ presentation_hstni     : num  72 72 59 49 93 56 65 49 63 55 ...
#>  $ Gender                 : chr  "female" "male" "female" "male" ...
#>  $ sweating               : num  1 1 0 0 1 1 0 1 0 0 ...
#>  $ pain.radiation         : num  0 0 1 0 1 1 1 0 1 1 ...
#>  $ pleuritic              : num  1 0 0 0 0 0 1 1 0 1 ...
#>  $ palpation              : num  1 0 0 1 1 1 1 1 0 1 ...
#>  $ ecg.twi                : num  1 1 1 1 0 1 0 0 0 0 ...
#>  $ second_hstni           : num  144 81 100 189 143 153 167 67 177 148 ...
#>  $ killip.class           : num  4 4 4 1 3 2 4 3 2 1 ...
#>  $ systolic.bp            : num  105 255 141 128 120 145 179 269 283 90 ...
#>  $ heart.rate             : num  296 119 13 121 84 140 72 154 107 200 ...
#>  $ creat                  : num  4 1 3 3 1 2 2 0 4 1 ...
#>  $ cardiac.arrest         : num  0 0 1 0 0 1 1 1 0 1 ...
#>  $ previous.pci           : num  1 0 0 0 1 1 1 0 1 0 ...
#>  $ previous.cabg          : num  1 0 1 1 1 1 0 1 1 1 ...
#>  $ aspirin                : num  0 0 0 0 0 0 1 1 1 1 ...
#>  $ number.of.episodes.24h : num  11 2 2 2 6 16 10 13 6 20 ...
#>  $ total.chol             : num  84 81 62 99 21 69 25 86 17 69 ...
#>  $ total.hdl              : num  3 3 2 4 4 2 5 2 5 3 ...
#>  $ Ethnicity              : chr  "black" "asian" "other" "black" ...

Calculation and Results

This is a basic example of how to calculate all risk scores available in the package and create a new data set with 12 new variables of the calculated and classified risk scores:

# Call the function with the cohort_xx to calculate all risk scores available in the package
new_data_frame <- calc_scores(data = cohort_xx)
 
# Select columns created after calculation
All_scores <- new_data_frame %>% select(HEART_score, HEART_strat, EDACS_score, EDACS_strat, GRACE_score, GRACE_strat, TIMI_score, TIMI_strat, SCORE2_score, SCORE2_strat, ASCVD_score, ASCVD_strat)

# Observe the results
head(All_scores)
#> # A tibble: 6 × 12
#> # Rowwise: 
#>   HEART_score HEART_strat   EDACS_score EDACS_strat  GRACE_score GRACE_strat  
#>         <dbl> <ord>               <dbl> <ord>              <dbl> <ord>        
#> 1           7 High risk              -1 Not low risk         154 High risk    
#> 2           4 Moderate risk          11 Not low risk          77 Low risk     
#> 3           5 Moderate risk           7 Not low risk         105 Moderate risk
#> 4           7 High risk               6 Not low risk          84 Low risk     
#> 5           4 Moderate risk          14 Not low risk          82 Low risk     
#> 6           9 High risk              18 Not low risk         164 High risk    
#> # ℹ 6 more variables: TIMI_score <dbl>, TIMI_strat <ord>, SCORE2_score <dbl>,
#> #   SCORE2_strat <ord>, ASCVD_score <dbl>, ASCVD_strat <ord>

# Create a summary of them to obtain an initial idea of distribution
summary(All_scores)
#>   HEART_score           HEART_strat  EDACS_score          EDACS_strat 
#>  Min.   : 3.00   Low risk     : 2   Min.   :-6.00   Low risk    :  0  
#>  1st Qu.: 6.00   Moderate risk:43   1st Qu.: 6.00   Not low risk:100  
#>  Median : 7.00   High risk    :55   Median :11.00                     
#>  Mean   : 6.54                      Mean   :11.29                     
#>  3rd Qu.: 7.00                      3rd Qu.:18.00                     
#>  Max.   :10.00                      Max.   :30.00                     
#>   GRACE_score           GRACE_strat   TIMI_score           TIMI_strat
#>  Min.   : 33.0   Low risk     :24   Min.   :2.00   Very low risk: 0  
#>  1st Qu.: 90.0   Moderate risk:27   1st Qu.:4.00   Low risk     : 4  
#>  Median :117.5   High risk    :49   Median :4.00   Moderate risk:48  
#>  Mean   :116.1                      Mean   :4.45   High risk    :48  
#>  3rd Qu.:143.2                      3rd Qu.:5.00                     
#>  Max.   :184.0                      Max.   :7.00                     
#>   SCORE2_score           SCORE2_strat  ASCVD_score            ASCVD_strat
#>  Min.   :  0.00   Very low risk: 0    Min.   :0.0000   Very low risk:19  
#>  1st Qu.: 20.00   Low risk     :10    1st Qu.:0.1300   Low risk     : 1  
#>  Median : 90.00   Moderate risk: 6    Median :0.4400   Moderate risk:15  
#>  Mean   : 65.85   High risk    :84    Mean   :0.4742   High risk    :65  
#>  3rd Qu.:100.00                       3rd Qu.:0.8500                     
#>  Max.   :100.00                       Max.   :1.0000

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.