10-year risk of death from CHD from the EPOCH Japan study participants

Description

epoch_chd() calculates the absolute 10-year risk of death from coronary heart disease (CHD).

Mandatory variables: "age", "male", "urineprotein", "sbp", "t2dm", "tc", "hdl", "smoking". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Estimation of 10-Year Risk of Death from Coronary Heart Disease, Stroke, and Cardiovascular Disease in a Pooled Analysis of Japanese Cohorts: EPOCH-JAPAN"

DOI:10.5551/jat.58958

PMID:33041313

PMCID:PMC8326173

Usage

epoch_chd(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

epoch_chd() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 80 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  age = c(39, 85, 70),
  male = c(1, 0, 0),
  sbp = c(120, 130, 135),
  t2dm = c(NA, 1, 1),
  tc = c(180, 110, 185),
  hdl = c(55, 45, 58),
  urineprotein = c(1, 1, 0),
  smoking = c(0, 1, 0)
)

# age          :age(continuous)
# male         :male(category: 0 = female, 1 = male)
# sbp          :systolic blood pressure(continuous)
# t2dm         :diabetes mellitus(category: 0 = non-prevalent DM, 1 = prevalent DM)
# tc           :total cholesterol(continuous)
# hdl          :high-density lipoprotein cholesterol(continuous)
# urineprotein :proteinuria(category: 0 = negative, 1 = positive)
# smoking      :smoking status(category: 0 = never/past smoker, 1 = current smoker)

epoch_chd(data)

10-year risk of death from CVD from the EPOCH Japan study participants

Description

epoch_cvd() calculates the absolute 10-year risk of death from cardiovascular disease (CVD).

Mandatory variables: "age", "male", "urineprotein", "sbp", "t2dm", "smoking". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Estimation of 10-Year Risk of Death from Coronary Heart Disease, Stroke, and Cardiovascular Disease in a Pooled Analysis of Japanese Cohorts: EPOCH-JAPAN"

DOI:10.5551/jat.58958

PMID:33041313

PMCID:PMC8326173

Usage

epoch_cvd(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

epoch_cvd() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 80 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  age = c(39, 85, 70),
  male = c(1, 0, 1),
  sbp = c(120, 130, 140),
  t2dm = c(NA, 1, 0),
  urineprotein = c(1, 1, 0),
  smoking = c(0, 1, 1)
)

# age          :age(continuous)
# male         :male(category:0 = female, 1 = male)
# sbp          :systolic blood pressure(continuous)
# t2dm         :diabetes mellitus(category: 0 = non-prevalent DM, 1 = prevalent DM)
# urineprotein :proteinuria(category: 0 = negative, 1 = positive)
# smoking      :smoking status(category: 0 = never/past smoker, 1 = current smoker)


epoch_cvd(data)

10-year risk of death from stroke from the EPOCH Japan study participants

Description

epoch_stroke() calculates the absolute 10-year risk of death from stroke.

Mandatory variables: "age", "urineprotein", "sbp", "t2dm", "smoking". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Estimation of 10-Year Risk of Death from Coronary Heart Disease, Stroke, and Cardiovascular Disease in a Pooled Analysis of Japanese Cohorts: EPOCH-JAPAN"

DOI:10.5551/jat.58958

PMID:33041313

PMCID:PMC8326173

Usage

epoch_stroke(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

epoch_stroke() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 80 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  age = c(39, 85, 70),
  sbp = c(120, 130, 140),
  t2dm = c(NA, 1, 0),
  urineprotein = c(1, 1, 0),
  smoking = c(0, 1, 1)
)

# age          :age(continuous)
# sbp          :systolic blood pressure(continuous)
# t2dm         :diabetes mellitus(category: 0 = non-prevalent DM, 1 = prevalent DM)
# urineprotein :proteinuria(category: 0 = negative, 1 = positive)
# smoking      :smoking status(category: 0 = never/past smoker, 1 = current smoker)


epoch_stroke(data)

10-year risk calculation for ASCVD events based on the Hisayama study

Description

hisayama() calculates the 10-year probability of developing the first atherosclerotic cardiovascular disease (ASCVD) event. Mandatory variables: "age", "male", "sbp", "t2dm", "hdl", "ldl", "urineprotein", "smoking", "exercise". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Development and Validation of a Risk Prediction Model for Atherosclerotic Cardiovascular Disease in Japanese Adults: The Hisayama Study"

DOI:10.5551/jat.61960

PMCID:PMC8894117

PMID:33487620

Usage

hisayama(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

hisayama() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 80 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

This risk prediction model was developed based on the Hisayama study participants(older than 40 and younger than 80). In the calculation process, when the participants' age is outside of the range, the result is returned as follows.

1. Younger than 30: same value for 30-40.

2. Older than 80: same value for 75-79.

Examples

data <- data.frame(
  age = c(45, 55, 90),
  male = c(1, 0, 1),
  sbp = c(120, 130, 145),
  t2dm = c(0, 1, 0),
  hdl = c(55, 45, 35),
  ldl = c(110, 130, 160),
  urineprotein = c(NA, 1, 0),
  smoking = c(0, 1, 0),
  exercise = c(1, 0, 1)
)

# age          :age(continuous)
# male         :male(category:0 = female,1 = male)
# sbp          :systolic blood pressure(continuous)
# t2dm         :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# hdl          :high-density lipoprotein cholesterol(continuous)
# ldl          :low-density lipoprotein cholesterol(continuous)
# urineprotein :proteinuria(category:0 = negative,1 = positive)
# smoking      :smoking status(category:0 = never/past smoker,1 = current smoker)
# exercise     :weekly exercise(category:0 = less than 3 times,1 = 3 times or more)


hisayama(data)

5-year risk calculation for acute myocardial infarction (AMI) based on the JALS study with electrocardiogram model

Description

jals_ami_wECG() calculates the 5-year probability of developing the AMI event. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "hdl", "tc", "egfr", "smoking", "ht_medication", "af". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_ami_wECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_ami_wECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  af = c(1, 0, 0, 1, 1, 0),
  tc = c(130, 140, 240, 190, 208, 300),
  hdl = c(30, 50, 70, 40, 59, 60),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  egfr = c(45, 60, 90, 91, 100, 30),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)


# af            :atrial fibrillation(category: 0 = negative, 1 = positive)
# tc            :total cholesterol(continuous)
# hdl           :high-density lipoprotein cholesterol(continuous)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# egfr          :estimated glomerular filtration rate(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_ami_wECG(data)

5-year risk calculation for acute myocardial infarction (AMI) based on the JALS study without electrocardiogram model

Description

jals_ami_woECG() calculates the 5-year probability of developing the AMI event. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "hdl", "tc", "smoking", "ht_medication". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_ami_woECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_ami_woECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  tc = c(130, 140, 240, 190, 208, 300),
  hdl = c(30, 50, 70, 40, 59, 60),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)

# tc            :total cholesterol(continuous)
# hdl           :high-density lipoprotein cholesterol(continuous)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_ami_woECG(data)

5-year risk calculation for composite outcome (stroke + acute myocardial infarction) based on the JALS study with electrocardiogram model

Description

jals_composite_wECG() calculates the 5-year probability of developing the composite outcome. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "hdl", "smoking", "ht_medication", "af". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_composite_wECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_composite_wECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  af = c(1, 0, 0, 1, 1, 0),
  hdl = c(30, 50, 70, 40, 59, 60),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)


# af            :atrial fibrillation(category: 0 = negative, 1 = positive)
# hdl           :high-density lipoprotein cholesterol(continuous)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_composite_wECG(data)

5-year risk calculation for composite outcome (stroke + acute myocardial infarction) based on the JALS study without electrocardiogram model

Description

jals_composite_woECG() calculates the 5-year probability of developing the composite outcome. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "hdl", "smoking", "ht_medication". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_composite_woECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_composite_woECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  hdl = c(30, 50, 70, 40, 59, 60),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)

# hdl           :high-density lipoprotein cholesterol(continuous)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_composite_woECG(data)

5-year risk calculation for cardiovascular disease event based on the JALS study with electrocardiogram model

Description

jals_cvd_wECG() calculates the 5-year probability of developing the CVD outcome. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "hdl", "egfr", "smoking", "ht_medication", "bmi", "af". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_cvd_wECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_cvd_wECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  af = c(1, 0, 0, 1, 1, 0),
  bmi = c(30, 23, 15, 25, 18.5, 20),
  hdl = c(30, 50, 70, 40, 59, 60),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  egfr = c(45, 60, 90, 91, 100, 30),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)


# af            :atrial fibrillation(category: 0 = negative, 1 = positive)
# bmi           :body mass index(continuous)
# hdl           :high-density lipoprotein cholesterol(continuous)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# egfr          :estimated glomerular filtration rate(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_cvd_wECG(data)

5-year risk calculation for cardiovascular disease event based on the JALS study without electrocardiogram model

Description

jals_cvd_woECG() calculates the 5-year probability of developing the CVD outcome. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "hdl", "egfr", "smoking", "ht_medication", "bmi". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_cvd_woECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_cvd_woECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  bmi = c(30, 23, 15, 25, 18.5, 20),
  hdl = c(30, 50, 70, 40, 59, 60),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  egfr = c(45, 60, 90, 91, 100, 30),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)

# bmi           :body mass index(continuous)
# hdl           :high-density lipoprotein cholesterol(continuous)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# egfr          :estimated glomerular filtration rate(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_cvd_woECG(data)

5-year risk calculation for Stroke based on the JALS study with electrocardiogram model

Description

jals_stroke_wECG() calculates the 5-year probability of developing the stroke event. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "smoking", "ht_medication", "af". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_stroke_wECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_stroke_wECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  af = c(1, 0, 0, 1, 1, 0),
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)

# af            :atrial fibrillation(category: 0 = negative, 1 = positive)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)

jals_stroke_wECG(data)

5-year risk calculation for Stroke based on the JALS study without electrocardiogram model

Description

jals_stroke_woECG() calculates the 5-year probability of developing the stroke event. Mandatory variables: "age", "male", "sbp", "dbp", "t2dm", "smoking", "ht_medication". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Absolute risk score for stroke, myocardial infarction, and all cardiovascular disease: Japan Arteriosclerosis Longitudinal Study"

DOI:10.1038/s41440-019-0220-z

PMID:30760890

Usage

jals_stroke_woECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

jals_stroke_woECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 90 or older. results may not be accurate.":

4."Some individuals are aged 40 or younger. results may not be accurate.":

Examples

data <- data.frame(
  sbp = c(110, 125, 135, 150, 170, 180),
  dbp = c(75, 75, 85, 95, 105, 110),
  ht_medication = c(0, 1, 0, 1, 0, 1),
  age = c(39, 49, 59, 69, 79, 80),
  t2dm = c(0, 0, 1, 1, 1, 0),
  male = c(1, 1, 0, 0, 0, 1),
  smoking = c(1, 0, 1, 0, 0, 1)
)

# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# age           :age(continuous)
# t2dm          :diabetes mellitus(category:0 = non-prevalent DM,1 = prevalent DM)
# male          :male(category:0 = female,1 = male)
# smoking       :smoking status(category:0 = never/past smoker,1 = current smoker)


jals_stroke_woECG(data)

10-year risk calculation for CVD events based on the Suita study with electrocardiogram model

Description

suita_ecg() calculates the 10-year probability of developing the first ASCVD event. Mandatory variables: "age", "male", "sbp", "dbp", "ht_medication", "t2dm", "tc", "hdl", "ldl", "urineprotein", "smoking", "af", "lvh". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article. "Development of Cardiovascular Disease Risk Prediction Model Using the Suita Study, a Population-Based Prospective Cohort Study in Japan"

DOI:10.5551/jat.48843

PMID:32023562

PMID:PMC7803836

Usage

suita_wECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

suita_wECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 80 or older. results may not be accurate.":

4."Some individuals are aged 30 or younger. results may not be accurate.":

This risk prediction model was developed based on the Suita study participants(older than 30 and younger than 80). In the calculation process, when the participants' age is outside of the range, the result is returned as follows.

1. Younger than 30: same value for 30-40.

2. Older than 80: same value for 75-79.

Examples

data <- data.frame(
  age = c(45, 55, 90),
  male = c(1, 0, 1),
  sbp = c(120, 130, 145),
  dbp = c(80, 90, 110),
  ht_medication = c(0, 1, 0),
  t2dm = c(0, 1, 0),
  tc = c(180, 160, 210),
  hdl = c(55, 45, 35),
  ldl = c(110, 100, 160),
  urineprotein = c(1, 1, 0),
  smoking = c(0, 1, 0),
  af = c(1, 0, 1),
  lvh = c(1, 0, 1)
)

# age           :age(continuous)
# male          :male(category: 0 = female, 1 = male)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# t2dm          :diabetes mellitus(category: 0 = non-prevalent DM, 1 = prevalent DM)
# tc            :total cholesterol(continuous)
# hdl           :high-density lipoprotein cholesterol(continuous)
# ldl           :low-density lipoprotein cholesterol(continuous)
# urineprotein  :proteinuria(category: 0 = negative, 1 = positive)
# smoking       :smoking status(category: 0 = never/past smoker, 1 = current smoker)
# af            :atrial fibrillation(category: 0 = negative, 1 = positive)
# lvh           :left ventricular hypertrophy(category: 0 = negative, 1 = positive)

suita_wECG(data)

10-year risk calculation for CVD events based on the Suita study without electrocardiogram model

Description

suita() calculates the 10-year probability of developing the first incidence of cardiovascular disease(CVD) (stroke or coronary heart disease) without electrocardiogram (ECG).

Mandatory variables: "age", "male", "sbp", "dbp", "ht_medication", "t2dm", "tc", "hdl", "ldl", "urineprotein", "smoking". These variables must be applied with the above spelling.

The details of this prediction model should be referred to the original article.

"Development of Cardiovascular Disease Risk Prediction Model Using the Suita Study, a Population-Based Prospective Cohort Study in Japan"

DOI:10.5551/jat.48843

PMID:32023562

PMCID:PMC7803836

Usage

suita_woECG(data)

Arguments

Value

Based on the mandatory variables, a risk probability will be calculated for each line. The primary cautions are as follows,

1."Data frame must contain the following columns.":

If you contact this warning message, the data frame did not contain required variables. Please check the data frame contain the mandatory variables in the designated spelling.

2."Data frame contains NA values in required columns.":

suita_woECG() returns the NA if the required variables were missing, or NAs were generated in the calculation process.

3."Some individuals are aged 80 or older. results may not be accurate.":

4."Some individuals are aged 30 or younger. results may not be accurate.":

This risk prediction model was developed based on the Suita study participants(older than 30 and younger than 80). In the calculation process, when the participants' age is outside of the range, the result is returned as follows.

1. Younger than 30: same value for 30-40.

2. Older than 80: same value for 75-79.

Examples

data <- data.frame(
  age = c(45, 55, 90),
  male = c(1, 0, 1),
  sbp = c(120, 130, 145),
  dbp = c(80, 90, 110),
  ht_medication = c(0, 1, 0),
  t2dm = c(0, 1, 0),
  tc = c(180, 160, 210),
  hdl = c(55, 45, 35),
  ldl = c(110, 100, 160),
  urineprotein = c(1, 1, 0),
  smoking = c(0, 1, 0)
)

# age           :age(continuous)
# male          :male(category: 0 = female, 1 = male)
# sbp           :systolic blood pressure(continuous)
# dbp           :diastolic blood pressure(continuous)
# ht_medication :hypertensive medication user(category:0 = non-user,1 = user)
# t2dm          :diabetes mellitus(category: 0 = non-prevalent DM, 1 = prevalent DM)
# tc            :total cholesterol(continuous)
# hdl           :high-density lipoprotein cholesterol(continuous)
# ldl           :low-density lipoprotein cholesterol(continuous)
# urineprotein  :proteinuria(category: 0 = negative, 1 = positive)
# smoking       :smoking status(category: 0 = never/past smoker, 1 = current smoker)

suita_woECG(data)