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:
- HEART
- EDACS
- GRACE 2.0
- TIMI
- SCORE2/OP
- SCORE2-Diabetes
- SCORE2-CKD
- PCE (ASCVD)
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")
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)),
heart.rate = as.numeric(sample(0:300, num_rows, replace = TRUE)),
systolic.bp = as.numeric(sample(40:300, num_rows, replace = TRUE)),
aspirin = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
creat = as.numeric(sample(0:4, num_rows, replace = TRUE)),
number.of.episodes.24h = as.numeric(sample(0:20, num_rows, replace = TRUE)),
previous.pci = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
cardiac.arrest = as.numeric(sample(c(0, 1), num_rows, replace = TRUE)),
previous.cabg = as.numeric(sample(c(0, 1), 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),
eGFR = as.numeric(sample(15:120, num_rows, replace = TRUE)),
ACR = as.numeric(sample(5:1500, num_rows, replace = TRUE)),
trace = sample(c("trace", "1+", "2+", "3+", "4+"), num_rows, replace = TRUE)
)
str(cohort_xx)
#> 'data.frame': 100 obs. of 34 variables:
#> $ typical_symptoms.num : num 1 5 5 5 2 3 3 2 4 6 ...
#> $ ecg.normal : num 0 1 0 1 0 0 0 0 0 0 ...
#> $ abn.repolarisation : num 1 1 0 0 0 0 0 1 1 1 ...
#> $ ecg.st.depression : num 1 1 1 0 1 0 1 1 0 0 ...
#> $ Age : num 64 68 80 51 69 63 36 41 59 65 ...
#> $ diabetes : num 0 0 0 1 0 1 0 1 1 1 ...
#> $ smoker : num 1 1 1 1 0 1 1 1 1 0 ...
#> $ hypertension : num 1 1 0 1 0 0 0 0 0 0 ...
#> $ hyperlipidaemia : num 1 0 1 1 0 1 1 0 1 1 ...
#> $ family.history : num 1 1 0 0 1 0 1 1 1 0 ...
#> $ atherosclerotic.disease: num 1 1 1 0 0 0 1 1 1 1 ...
#> $ presentation_hstni : num 26 15 28 13 90 36 41 87 94 27 ...
#> $ Gender : chr "male" "male" "female" "male" ...
#> $ sweating : num 0 0 0 1 0 0 0 0 0 0 ...
#> $ pain.radiation : num 1 0 1 1 0 0 0 1 1 0 ...
#> $ pleuritic : num 0 1 1 1 1 1 0 1 0 0 ...
#> $ palpation : num 0 0 1 0 0 1 0 0 1 1 ...
#> $ ecg.twi : num 1 1 0 0 1 0 0 1 1 1 ...
#> $ second_hstni : num 46 156 72 50 193 72 117 78 36 190 ...
#> $ killip.class : num 2 1 1 1 3 2 4 2 2 2 ...
#> $ heart.rate : num 246 215 131 158 128 212 182 187 195 71 ...
#> $ systolic.bp : num 128 239 229 115 118 141 255 294 113 179 ...
#> $ aspirin : num 1 0 0 1 1 0 1 0 1 1 ...
#> $ creat : num 4 3 2 1 3 4 4 4 1 3 ...
#> $ number.of.episodes.24h : num 2 3 2 0 9 1 19 2 3 15 ...
#> $ previous.pci : num 1 0 0 1 1 1 1 1 1 0 ...
#> $ cardiac.arrest : num 1 1 0 1 1 1 0 1 1 1 ...
#> $ previous.cabg : num 1 1 0 0 1 0 0 1 0 0 ...
#> $ total.chol : num 98 62 69 71 92 16 61 9 27 35 ...
#> $ total.hdl : num 2 2 2 4 5 4 3 2 4 2 ...
#> $ Ethnicity : chr "black" "asian" "asian" "white" ...
#> $ eGFR : num 28 55 69 57 73 105 119 93 16 106 ...
#> $ ACR : num 673 127 250 565 420 ...
#> $ trace : chr "4+" "4+" "4+" "1+" ...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 5 Moderate risk 21 Not low risk 143 High risk
#> 2 8 High risk 14 Not low risk 111 Moderate risk
#> 3 9 High risk 11 Not low risk 128 High risk
#> 4 5 Moderate risk 16 Not low risk 82 Low risk
#> 5 7 High risk 14 Not low risk 164 High risk
#> 6 6 Moderate risk 0 Not low risk 132 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. : 1.00 Low risk : 5 Min. :-4.00 Low risk : 1
#> 1st Qu.: 5.00 Moderate risk:48 1st Qu.: 5.00 Not low risk:99
#> Median : 6.00 High risk :47 Median :10.00
#> Mean : 6.24 Mean : 9.86
#> 3rd Qu.: 7.25 3rd Qu.:15.00
#> Max. :10.00 Max. :28.00
#> GRACE_score GRACE_strat TIMI_score TIMI_strat
#> Min. : 29.0 Low risk :28 Min. :1.00 Very low risk: 0
#> 1st Qu.: 82.0 Moderate risk:30 1st Qu.:3.00 Low risk : 8
#> Median :111.0 High risk :42 Median :4.00 Moderate risk:48
#> Mean :109.9 Mean :4.21 High risk :44
#> 3rd Qu.:139.5 3rd Qu.:5.00
#> Max. :230.0 Max. :6.00
#> SCORE2_score SCORE2_strat ASCVD_score ASCVD_strat
#> Min. : 0.40 Very low risk: 0 Min. :0.0000 Very low risk:10
#> 1st Qu.: 28.60 Low risk : 7 1st Qu.:0.1400 Low risk : 6
#> Median : 98.90 Moderate risk: 5 Median :0.4450 Moderate risk:16
#> Mean : 68.72 High risk :88 Mean :0.4518 High risk :68
#> 3rd Qu.:100.00 3rd Qu.:0.7100
#> Max. :100.00 Max. :1.0000