The risk of cardiovascular events and death of febuxostat (FebuxostatCVRisk)

• Analytics use case(s): Population-Level Estimation
• Study type: Clinical Application
• Tags: (Febuxostat, CardioVascularRisk)
• Study lead: Seng Chan You, Seonji Kim
• Study lead forums tag: SCYou
• Study start date: 2 November 2023
• Study end date:
• Protocol: https://github.com/ohdsi-studies/FebuxostatCVRisk/tree/Master/documents/
• Publications:
• Results explorer:

This study aims to confirm the adverse cardiovascular events and death risks of febuxostat compared with allopurinol in patients with gout.

Requirements

• A database in Common Data Model version 5 in one of these platforms: SQL Server, Oracle, PostgreSQL, IBM Netezza, Apache Impala, Amazon RedShift, Google BigQuery, Spark, or Microsoft APS.
• R version 4.0.0 or newer
• On Windows: RTools
• Java
• 25 GB of free disk space

How to run

1. Follow these instructions for setting up your R environment, including RTools and Java.

2. Open your study package in RStudio. Use the following code to install all the dependencies:

   renv::restore()

3. In RStudio, select 'Build' then 'Install and Restart' to build the package.

   Once installed, you can execute the study by modifying and using the code below. For your convenience, this code is also provided under extras/CodeToRun.R:

   library(FebuxostatCVRisk)
   
   # Optional: specify where the temporary files (used by the Andromeda package) will be created:
   options(andromedaTempFolder = "D:/andromedaTemp")
   
   # Maximum number of cores to be used:
   maxCores <- parallel::detectCores()
   
   # Minimum cell count when exporting data:
   minCellCount <- 5
   
   # The folder where the study intermediate and result files will be written:
   outputFolder <- "D:/FebuxostatCVRisk"
   
   # Details for connecting to the server:
   # See ?DatabaseConnector::createConnectionDetails for help
   connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = "sql server",
                                                               server = Sys.getenv("server"),
                                                               user = Sys.getenv("user"),
                                                               password = Sys.getenv("password"),
                                                               pathToDriver = "D:/pathToDriver")
   
   # The name of the database schema where the CDM data can be found:
   cdmDatabaseSchema <- "cdm_db"
   
   # The name of the database schema and table where the study-specific cohorts will be instantiated:
   cohortDatabaseSchema <- "scratch.dbo"
   cohortTable <- "FebuxostatCVRiskStudy"
   
   
   # Some meta-information that will be used by the export function:
   databaseId <- "FebuxostatCVRisk"
   databaseName <- "FebuxostatCVRisk"
   databaseDescription <- "The risk of cardiovascular events and death of febuxostat"
   
   # For some database platforms (e.g. Oracle): define a schema that can be used to emulate temp tables:
   options(sqlRenderTempEmulationSchema = NULL)
   
   execute(connectionDetails = connectionDetails,
           cdmDatabaseSchema = cdmDatabaseSchema,
           cohortDatabaseSchema = cohortDatabaseSchema,
           cohortTable = cohortTable,
           outputFolder = outputFolder,
           databaseId = databaseId,
           databaseName = databaseName,
           databaseDescription = databaseDescription,
           verifyDependencies = FALSE,
           createCohorts = TRUE,
           synthesizePositiveControls = TRUE,
           runAnalyses = TRUE,
           packageResults = TRUE,
           maxCores = maxCores)

4. Upload the file export/Results_<DatabaseId>.zip in the output folder to the study coordinator:

   uploadResults(outputFolder, privateKeyFileName = "<file>", userName = "<name>")

   Where <file> and <name< are the credentials provided to you personally by the study coordinator.

5. To view the results, use the Shiny app:

   prepareForEvidenceExplorer("Result_<databaseId>.zip", "/shinyData")
   launchEvidenceExplorer("/shinyData", blind = TRUE)

Note that you can save plots from within the Shiny app. It is possible to view results from more than one database by applying prepareForEvidenceExplorer to the Results file from each database, and using the same data folder. Set blind = FALSE if you wish to be unblinded to the final results.

License

The FebuxostatCVRisk package is licensed under Apache License 2.0

Development

FebuxostatCVRisk was developed in ATLAS and R Studio.

Development status

Under development