| TIMI {RiskScorescvd} | R Documentation |
Thrombolysis In Myocardial Infarction (TIMI) Risk Score for UA/NSTEMI
Description
This function implements the TIMI score calculation as a vector
Age <65 = 0 65 - 74 = 2 >= 75 = 3
Risk factors >3* yes = 1, no = 0
Known CAD (stenosis >= 50 yes = 1, no = 0
Aspirin Use yes = 1, no = 0
Severe angina yes = 1, no = 0
ECG ST Elevation or LBBB yes = 1, no = 0
Positive cardiac marker yes = 1, no = 0
Four possible outcomes
0 = Very low risk 1-2 = Low risk 3-4 = Moderate risk =>5 = High risk
This function allows you to calculate the HEART score row wise in a data frame with the required variables. It would then retrieve a data frame with two extra columns including the calculations and their classifications
Usage
TIMI(
Age = Age,
hypertension = hypertension,
hyperlipidaemia = hyperlipidaemia,
family.history = family.history,
diabetes = diabetes,
smoker = smoker,
previous.pci = previous.pci,
previous.cabg = previous.cabg,
aspirin = aspirin,
number.of.episodes.24h = number.of.episodes.24h,
ecg.st.depression = ecg.st.depression,
presentation_hstni = presentation_hstni,
Gender = Gender,
classify
)
TIMI_scores(
data,
Age = Age,
hypertension = hypertension,
hyperlipidaemia = hyperlipidaemia,
family.history = family.history,
diabetes = diabetes,
smoker = smoker,
previous.pci = previous.pci,
previous.cabg = previous.cabg,
aspirin = aspirin,
number.of.episodes.24h = number.of.episodes.24h,
ecg.st.depression = ecg.st.depression,
presentation_hstni = presentation_hstni,
Gender = Gender,
classify
)
Arguments
Age |
a numeric vector of age values, in years |
hypertension |
a binary numeric vector, 1 = yes and 0 = no |
hyperlipidaemia |
a binary numeric vector, 1 = yes and 0 = no |
family.history |
a binary numeric vector, 1 = yes and 0 = no |
diabetes |
a binary numeric vector, 1 = yes and 0 = no |
smoker |
a binary numeric vector, 1 = yes and 0 = no |
previous.pci |
a binary numeric vector, 1 = yes and 0 = no |
previous.cabg |
a binary numeric vector, 1 = yes and 0 = no |
aspirin |
a binary numeric vector, 1 = yes and 0 = no |
number.of.episodes.24h |
a numeric vector of number of angina episodes in 24 hours |
ecg.st.depression |
a binary numeric vector, 1 = yes and 0 = no |
presentation_hstni |
a continuous numeric vector of the troponin levels |
Gender |
a binary character vector of sex values. Categories should include only 'male' or 'female' |
classify |
set TRUE if wish to add a column with the scores' categories |
data |
A data frame with all the variables needed for calculation: typical_symptoms.num, ecg.normal, abn.repolarisation, ecg.st.depression,Age, diabetes, smoker, hypertension, hyperlipidaemia, family.history, atherosclerotic.disease, presentation_hstni, Gender |
Details
TIMI = Thrombolysis In Myocardial Infarction
Value
A vector with TIMI score calculations and/or a vector of their classifications if indicated
data frame with two extra columns including the HEART score calculations and their classifications
Examples
# Create a data frame or list with the necessary variables
# Set the number of rows
num_rows <- 100
# Create a larger 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(0:300, 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)
)
# Call the function with the cohort_xx
results <- cohort_xx %>% rowwise() %>%
mutate(TIMI_score = TIMI(Age, hypertension, hyperlipidaemia, family.history,
diabetes, smoker, previous.pci, previous.cabg, aspirin, number.of.episodes.24h,
ecg.st.depression, presentation_hstni, Gender, classify = FALSE))
# Create a data frame or list with the necessary variables
# Set the number of rows
num_rows <- 100
# Create a larger 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(0:300, 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)
)
# Call the function with the cohort_xx
result <- TIMI_scores(data = cohort_xx, classify = TRUE)
# Print the results
summary(result$TIMI_score)
summary(result$TIMI_strat)