In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

Integration of GIS Data into OHDSI Model Building

Motivation

Although the proposed GIS extension tables have been approved by the broader OHDSI community, they are not yet integrated natively into the OHDSI tooling. These tables and the data they capture, however, can still be referenced in standard model building and analytics workflows. The purpose of this analytics demonstration is to show how data in the EXPOSURE_OCCURRENCE table can be utilized in the training and evaluation of an OMOP-specific patient-level-prediction (PLP) model. The analytic process we executed and describe below relies on a GIS-version of the Tufts Synthetic Dataset that has both EHR and geospatial data integrated; see the description of that dataset for more details about contents or access. Much of the work is based on the detailed vignette that describes custom feature engineering in PLP vignette('AddingCustomFeatureEngineering')

Step-by-Step Process

Step 1: Create Target & Outcome Cohorts

We defined our target cohort within the sampling procedures when creating a subset of the Tufts Synthetic Data, and we described this process at length elsewhere. For the purposes of using the PLP package, we formalized this group of individuals in the cohort table using a simple cohort definition including all individuals with "any visit", and include that atlas-compatible json definition here for reference.

We also created our outcome cohort - in this case, those patients with COPD or a conceptual descendant thereof - in Atlas and have shared the json definition (we also included an equivalent SQL script).

Step 2: Create Generic PLP Lasso Logistic Regression Model in R

It is possible to create an R package that serves as a basis for a PLP model using Atlas, but given Atlas does not yet support the GIS extension, we have created this demo model directly using the PLP package.

After configuring the environment (very important!) appropriately, we imported necessary packages and defined the relevant parameters about the data source:

library(PatientLevelPrediction)
library(dplyr)
outputFolder <- "/ohdsi-gis/copdResultsPM25_NEW"
saveDirectory <- outputFolder
ExecutionDateTime <- Sys.time()
logSettings = createLogSettings(verbosity = "DEBUG", timeStamp = T, logName =
                                  "runPlp Log")
analysisName = 'Generic PLP'

# Details for connecting to the server:
connectionDetails <- DatabaseConnector::createConnectionDetails(
        dbms = 'spark',
        server = '/default',
        connectionString = '<REDACTED>'
    )
# Add the database containing the OMOP CDM data
cdmDatabaseSchema <- 'gis_syn_dataset_5_4'
# Add a sharebale name for the database containing the OMOP CDM data
cdmDatabaseName <- 'TSD-GIS'
# Add a database with read/write access as this is where the cohorts will be generated
cohortDatabaseSchema <- 'gis_syn_dataset_5_4'
tempEmulationSchema <- NULL
# table name where the cohorts will be generated
cohortTable <- 'cohort'

After defining parameters, we created a database settings object and launched the runMultiplePLP function to create a base plpData object:

databaseDetails <- PatientLevelPrediction::createDatabaseDetails(
        connectionDetails = connectionDetails, 
        cdmDatabaseSchema = cdmDatabaseSchema, 
        cdmDatabaseName = cdmDatabaseName, 
        tempEmulationSchema = tempEmulationSchema, 
        cohortDatabaseSchema = cohortDatabaseSchema, 
        cohortTable = cohortTable, 
        outcomeDatabaseSchema = cohortDatabaseSchema,  
        outcomeTable = cohortTable, 
        cdmVersion = 5
)


# Run very simple LR model against two cohorts created in Atlas. Use model 
# as basis for augmented model with pollutants below 
runMultiplePlp(
   databaseDetails = databaseDetails,
   modelDesignList = list(createModelDesign(targetId = 9, outcomeId = 8, modelSettings =
                                              setLassoLogisticRegression())),
   onlyFetchData = F,
   cohortDefinitions = NULL,
   logSettings = createLogSettings(verbosity = "DEBUG", timeStamp = T, logName =
                                     "runPlp Log"),
   saveDirectory = outputFolder,
   sqliteLocation = file.path(saveDirectory, "sqlite")
 )

Step 3: Split plpData object to train/test, augment labels with EXPOSURE_OCCURRENCE values

The labels sub-object within plpData contains per-individual data elements like gender and age; we added an additional data element to this object derived from the EXPOSURE_OCCURRENCE table:

cohortDefinitions <- NULL
modelDesign <- createModelDesign(targetId = 9, outcomeId = 8, modelSettings = setLassoLogisticRegression())
populationSettings <- modelDesign$populationSettings
splitSettings <- modelDesign$splitSettings

plpData <- loadPlpData("/ohdsi-gis/copdResultsPM25_B/targetId_9_L1")

mySplit <- splitData (plpData = plpData,
                      population = createStudyPopulation(plpData, 8, populationSettings),
                      splitSettings = splitSettings)


labelTrain <- mySplit$Train$labels
conn <- DatabaseConnector::connect(connectionDetails)
pollutants <- DatabaseConnector::querySql(conn, "SELECT person_id as subjectID, CAST(MEAN(value_as_number) AS DOUBLE) AS pmValue FROM gis_syn_dataset_5_4.exposure_occurrence WHERE value_as_number IS NOT NULL GROUP BY person_id;")
labelTrainPol <- merge(x=labelTrain, y=pollutants, by.x = "subjectId", by.y = "SUBJECTID")

mySplit$Train$labels <- labelTrainPol

labelTest <- mySplit$Test$labels
labelTestPol <- merge(x=labelTest, y=pollutants, by.x = "subjectId", by.y = "SUBJECTID")

mySplit$Test$labels <- labelTestPol

trainData <- mySplit$Train

testData <- mySplit$Test

Step 4: Reference augmented label objects in custom feature engineering function

We would like to convert our per-patient labels into the covariateData structure referenced by the PLP workflow. To do this, we were able to follow the feature engineering vignette and create two functions, createPollutants and implementPollutants:

createPollutants <- function(
                     method = 'QNCV'
                     ){

  # create list of inputs to implement function
  featureEngineeringSettings <- list(
    method = method
    )

  # specify the function that will implement the sampling
  attr(featureEngineeringSettings, "fun") <- "implementPollutants"

  # make sure the object returned is of class "sampleSettings"
  class(featureEngineeringSettings) <- "featureEngineeringSettings"
  return(featureEngineeringSettings)

}


implementPollutants <- function(trainData, featureEngineeringSettings, model=NULL) {
  if (is.null(model)) {
    method <- featureEngineeringSettings$method
    gisData <- trainData$labels
    y <- gisData$outcomeCount
    X <- gisData$PMVALUE
    model <- mgcv::gam(
      y ~ s(X, bs='cr', k=5, m=2)
    )
    newData <- data.frame(
      rowId = gisData$rowId,
      covariateId = 2052499839,
      covariateValue = model$fitted.values 
    )
  }
  else {
    gisData <- trainData$labels
    X <- gisData$PMVALUE
    y <- gisData$outcomeCount
    newData <- data.frame(y=y, X=X)
    yHat <- predict(model, newData)
    newData <- data.frame(
      rowId = gisData$rowId,
      covariateId = 2052499839,
      covariateValue = yHat
    )
  }
  # update covRef
  Andromeda::appendToTable(trainData$covariateData$covariateRef, 
                           data.frame(covariateId=2052499839,
                                      covariateName='Average PM2.5 Concentrations',
                                      analysisId=1,
                                      conceptId=2052499839))

  # update covariates
  Andromeda::appendToTable(trainData$covariateData$covariates, newData)

  featureEngineering <- list(
    funct = 'implementPollutants',
    settings = list(
      featureEngineeringSettings = featureEngineeringSettings,
      model = model
    )
  )

  attr(trainData$covariateData, 'metaData')$featureEngineering = listAppend(
    attr(trainData$covariateData, 'metaData')$featureEngineering,
    featureEngineering
  )

  trainData$model <- model

  return(trainData)
}

We can then execute these functions to create training and test data objects that contain our extended covariates:

featureEngineeringSettingsPol <- createPollutants('QNCV')
trainDataPol <- implementPollutants(trainData, featureEngineeringSettings)
testDataPol <- implementPollutants(testData, featureEngineeringSettings, trainDataPol$model)

Note that if we plot the output model of the GAM fitting in the implementPollutants function, we end up with a plot that aligns well with our underlying relationship between Odds Ratio and PM2.5 concentration that we used to distribute our synthetic data by location:

Step 5: Apply new train and test datasets to runPlp and evaluate output

analysisId <- '1'
analysisPath = file.path(saveDirectory, analysisId)

settings <- list(
  trainData = trainDataPol, 
  modelSettings = setLassoLogisticRegression(),
  analysisId = analysisId,
  analysisPath = analysisPath
)

ParallelLogger::logInfo(sprintf('Training %s model',settings$modelSettings$name))  
model <- tryCatch(
  {
    do.call(fitPlp, settings)
  },
  error = function(e) { ParallelLogger::logError(e); return(NULL)}
)


prediction <- model$prediction
# remove prediction from model
model$prediction <- NULL

#apply to test data if exists:
if('Test' %in% names(data)){
predictionTest <- tryCatch(
  {
    predictPlp(
      plpModel = model, 
      plpData = testDataPol,
      population = testDataPol$labels
    )
  },
  error = function(e) { ParallelLogger::logError(e); return(NULL)}
)

predictionTest$evaluationType <- 'Test'

if(!is.null(predictionTest)){
  prediction <- rbind(predictionTest, prediction[, colnames(prediction)!='index'])
}


}

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.