IPF Prediction Calculator

yaml

Libaries

library(tidyverse)

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library(readr)
library(stringr)
library(dplyr)
library(ggplot2)
library(gtsummary)
library(ggsignif)
library(ROCR)
library(pROC)

Type 'citation("pROC")' for a citation.

Attaching package: 'pROC'

The following objects are masked from 'package:stats':

    cov, smooth, var

library(readxl)
library(broom)
library(shiny)
library(rms)

Loading required package: Hmisc

Attaching package: 'Hmisc'

The following objects are masked from 'package:dplyr':

    src, summarize

The following objects are masked from 'package:base':

    format.pval, units


Attaching package: 'rms'

The following object is masked from 'package:shiny':

    validate

library(rmda)

IPF Probabilty Calculator

IPF_calc <- read_csv("Dataset/IPF_calculator.csv")

New names:
Rows: 622 Columns: 3119
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(88): SSID, NTP_Exc, Dx2, Dx3, Site.x, Site2, Sex.x, Race.x, Tob, py,... dbl
(3003): ...1, Sample_ID, Dx, IPF, Age_draw, Draw_date, FVC_base, FVCpct... lgl
(27): Exclude_rsn, PFT_prox, E_CigYears, E_Cig_Status, MOTHER, FATHER... date
(1): scan_date
ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
Specify the column types or set `show_col_types = FALSE` to quiet this message.
• `` -> `...1`

IPF_calc$UIP <- as.factor(IPF_calc$UIP)
IPF_calc$HP_antigen <- as.factor(IPF_calc$HP_antigen)

# Create the model
model_3 <- glm(Dx ~ Sex.x + HP_antigen + UIP + MMP12, data = IPF_calc, family = binomial)

# Generate IPF probability calculator
ui <- fluidPage(
  titlePanel("IPF probability calculator: "),
  sidebarLayout(
    sidebarPanel(
      selectInput("Sex.x", "Sex:", choices = c("Male", "Female")),
      selectInput("HP_antigen", "Positive HP-related IgG:", choices = c("Yes", "No")),
      selectInput("UIP", "Definite or Probable UIP on CT:", choices = c("Yes", "No")),
      numericInput("MMP12", "MMP12 value:", value = 3.5)
    ),
    mainPanel(
      verbatimTextOutput("prediction")
    )
  )
)

server <- function(input, output) {
  output$prediction <- renderPrint({
    # Create a new data frame with proper factor conversions:
    new_data <- data.frame(
      Sex.x = factor(tolower(input$Sex.x), levels = c("female", "male")),
      HP_antigen = factor(ifelse(input$HP_antigen == "Yes", "1", "0"), levels = c("0", "1")),
      UIP = factor(ifelse(input$UIP == "Yes", "1", "0"), levels = c("0", "1")),
      MMP12 = input$MMP12
    )
    
    # Predict probability using model_3
    prob <- predict(model_3, newdata = new_data, type = "response")
    percentage <- prob * 100
    paste("Predicted Probability of IPF rather than FHP:", round(percentage, 2), "%")
  })
}

shinyApp(ui = ui, server = server)

Shiny applications not supported in static R Markdown documents