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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:
NOTE: Troponin I values should be used. Additional functions for Troponin T are under development
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")
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" ...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. :1.0000These 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.