In DavidPatShuiFong/framinghamRiskEquation: Framingham Risk Equation calculator

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(printr)

Github repository at https://github.com/DavidPatShuiFong/framinghamRiskEquation

library(framinghamRiskEquation)
packageVersion("framinghamRiskEquation")

Documentation

?framinghamRiskEquation::framingham_riskequation

Examples

Example 1

This example comes from "Cardiovascular disease risk profiles" (Anderson 1991).

The worked answer in the paper is $0.22$.

framingham_riskequation(
  data.frame(InternalID = 1, BP = "135/80", Sex = "Female",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 230/48, Diabetes = TRUE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 5.96,
             Ethnicity = NA),
  outcome = "CHD", years = 10
)

The same risk-factors with outcome = "CVD" and years = 5.

The calculator on cvdcheck.org.au reports $18\%$.

framingham_riskequation(
  data.frame(InternalID = 1, BP = "135/80", Sex = "Female",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 230/48, Diabetes = TRUE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 5.96,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

The same risk-factors except Sex = "Male" with outcome = "CVD" and years = 5.

The online calculator on cvdcheck.org.au reports $20\%$.

framingham_riskequation(
  data.frame(InternalID = 1, BP = "135/80", Sex = "Male",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 230/48, Diabetes = TRUE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 5.96,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

Example 2

This comes from "An Updated Coronary Risk Profile" (Anderson 1991).

The worked answer in the paper is $0.192$.

framingham_riskequation(
  data.frame(InternalID = 2, BP = "130/80", Sex = "Male",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 240/45, Diabetes = FALSE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 6.22,
             Ethnicity = NA),
  outcome = "CHD", years = 10
)

The same risk-factors with outcome = "CVD" and years = 5.

The online calculator at cvdcheck.org.au reports $13\%$.

framingham_riskequation(
  data.frame(InternalID = 2, BP = "130/80", Sex = "Male",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 240/45, Diabetes = FALSE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 6.22,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

The same risk-factors except LVH = TRUE (outcome = CVD, years = 5).

The online calculator at cvdcheck.org.au reports $21\%$.

framingham_riskequation(
  data.frame(InternalID = 2, BP = "130/80", Sex = "Male",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 240/45, Diabetes = FALSE, LVH = TRUE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 6.22,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

Example 3

This comes from "An Updated Coronary Risk Profile" (Anderson 1991)

The worked answer in the paper is $0.135$.

framingham_riskequation(
  data.frame(InternalID = 3, BP = "130/80", Sex = "Female",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 240/45, Diabetes = FALSE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 6.22,
             Ethnicity = NA),
  outcome = "CHD", years = 10
)

The same risk-factors with outcome = "CVD" and years = 5.

The online calculator cvdcheck.org.au reports $9\%$.

framingham_riskequation(
  data.frame(InternalID = 3, BP = "130/80", Sex = "Female",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 240/45, Diabetes = FALSE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA,
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 6.22,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

The same risk-factors except LVH = TRUE (outcome = CVD, years = 5).

The online calculator cvdcheck.org.au reports $15\%$.

framingham_riskequation(
  data.frame(InternalID = 3, BP = "130/80", Sex = "Female",
             Age = 55, SmokingStatus = "Smoker",
             CholHDLRatio = 240/45, Diabetes = FALSE, LVH = TRUE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA, 
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 6.22,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

Example 4

This comes from "Maximising the effectiveness and cost-effectiveness of cardiovascular disease prevention in the general population" by Andrew M Tonkin, Andrew N Boyden and Stephen Colagiuri in 'Medical Journal of Australia' (191:6) 21 September 2009.

Using the provided Australian cardiovascular risk charts, the estimated risk is $5-9\%$.

The online calculator cvdcheck.org.au (with total cholesterol = 7.2, HDL 1.0) reports $9\%$.

framingham_riskequation(
  data.frame(InternalID = 4, BP = "140/80", Sex = "Female",
             Age = 46, SmokingStatus = "Smoker",
             CholHDLRatio = 7.2, Diabetes = FALSE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA, 
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 7.2,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)

This example also comes from "Maximising the effectiveness and cost-effectiveness of cardiovascular disease prevention in the general population".

Using the provided Australian cardiovascular risk charts, the estimated risk is $20-24\%$.

The online calculator cvdcheck.org.au (with total cholesterol = 7.2, HDL 1.0) reports $20\%$.

framingham_riskequation(
  data.frame(InternalID = 4, BP = "140/80", Sex = "Female",
             Age = 66, SmokingStatus = "Smoker",
             CholHDLRatio = 7.2, Diabetes = FALSE, LVH = FALSE,
             CardiovascularDisease = FALSE, PersistentProteinuria = FALSE,
             eGFRValue = NA, eGFRUnits = NA, 
             UrineAlbuminValue = NA, UrineAlbuminUnits = NA,
             FamilialHypercholesterolaemia = NA, Cholesterol = 7.2,
             Ethnicity = NA),
  outcome = "CVD", years = 5
)