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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:
You can install the development version of RiskScorescvd from GitHub with:
# install.packages("devtools")
devtools::install_github("dvicencio/RiskScorescvd")
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 6 5 2 5 1 3 1 1 1 2 ...
#> $ ecg.normal : num 0 1 0 0 1 0 1 1 0 1 ...
#> $ abn.repolarisation : num 1 1 0 1 1 1 0 1 0 0 ...
#> $ ecg.st.depression : num 1 1 0 1 1 0 0 1 1 1 ...
#> $ Age : num 70 77 51 64 77 32 70 79 56 57 ...
#> $ diabetes : num 1 0 1 0 0 1 0 1 0 1 ...
#> $ smoker : num 1 0 0 1 0 1 0 1 1 0 ...
#> $ hypertension : num 0 1 1 1 1 0 1 1 1 1 ...
#> $ hyperlipidaemia : num 1 0 1 0 0 1 1 1 1 0 ...
#> $ family.history : num 1 1 0 0 1 0 1 1 0 1 ...
#> $ atherosclerotic.disease: num 1 1 0 1 0 1 1 1 1 0 ...
#> $ presentation_hstni : num 10 56 97 86 78 94 63 39 63 89 ...
#> $ Gender : chr "male" "female" "female" "male" ...
#> $ sweating : num 1 0 1 1 1 1 0 1 1 0 ...
#> $ pain.radiation : num 1 0 1 0 0 0 0 1 1 0 ...
#> $ pleuritic : num 0 0 0 0 0 1 0 0 1 0 ...
#> $ palpation : num 1 1 0 0 0 1 0 0 1 0 ...
#> $ ecg.twi : num 1 1 0 1 0 0 1 1 0 1 ...
#> $ second_hstni : num 161 124 9 65 116 6 134 65 106 121 ...
#> $ killip.class : num 3 4 3 1 3 2 2 3 4 3 ...
#> $ systolic.bp : num 252 51 59 166 85 145 279 246 85 101 ...
#> $ heart.rate : num 61 64 23 52 294 164 135 74 244 217 ...
#> $ creat : num 4 3 4 4 3 3 2 0 2 0 ...
#> $ cardiac.arrest : num 0 1 0 0 0 1 1 0 1 1 ...
#> $ previous.pci : num 1 1 1 1 1 0 1 0 0 0 ...
#> $ previous.cabg : num 1 0 1 0 1 0 0 0 0 1 ...
#> $ aspirin : num 0 1 0 1 0 1 0 1 1 1 ...
#> $ number.of.episodes.24h : num 5 12 17 19 2 16 17 19 0 3 ...
#> $ total.chol : num 34 29 42 97 36 41 70 56 28 7 ...
#> $ total.hdl : num 5 3 3 3 2 3 2 5 3 3 ...
#> $ Ethnicity : chr "other" "white" "white" "asian" ...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 8 High risk 20 Not low risk 110 Moderate risk
#> 2 10 High risk 10 Not low risk 191 High risk
#> 3 7 High risk 14 Not low risk 111 Moderate risk
#> 4 8 High risk 19 Not low risk 95 Moderate risk
#> 5 7 High risk 19 Not low risk 208 High risk
#> 6 5 Moderate risk 5 Not low risk 72 Low 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. : 2.00 Low risk : 7 Min. :-8.00 Low risk : 1
#> 1st Qu.: 5.00 Moderate risk:56 1st Qu.: 5.75 Not low risk:99
#> Median : 6.00 High risk :37 Median :11.00
#> Mean : 6.02 Mean :10.44
#> 3rd Qu.: 7.00 3rd Qu.:15.25
#> Max. :10.00 Max. :24.00
#> GRACE_score GRACE_strat TIMI_score TIMI_strat
#> Min. : 15.0 Low risk :26 Min. :1.00 Very low risk: 0
#> 1st Qu.: 86.0 Moderate risk:38 1st Qu.:3.00 Low risk : 6
#> Median :106.5 High risk :36 Median :4.00 Moderate risk:51
#> Mean :106.9 Mean :4.21 High risk :43
#> 3rd Qu.:126.5 3rd Qu.:5.00
#> Max. :208.0 Max. :6.00
#> SCORE2_score SCORE2_strat ASCVD_score ASCVD_strat
#> Min. : 0.00 Very low risk: 0 Min. :0.0000 Very low risk: 9
#> 1st Qu.: 25.00 Low risk : 5 1st Qu.:0.1200 Low risk : 8
#> Median : 98.50 Moderate risk: 8 Median :0.3150 Moderate risk:20
#> Mean : 69.38 High risk :87 Mean :0.4526 High risk :63
#> 3rd Qu.:100.00 3rd Qu.:0.8600
#> Max. :100.00 Max. :1.0000These 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.
Health stats visible at Monitor.