Phenotype Details
🎯 What This Phenotype Does
This phenotype identifies patients based on specific clinical criteria and can be executed across any OMOP CDM database.
- ✅ Compatible with OHDSI Atlas
- ✅ Direct SQL execution
- ✅ WebAPI integration
- ✅ Standardized vocabulary
📈 Execution Metrics
👥 Patient Demographics
📊 Available Phenotypes
Interactive Phenotype Execution Demo
Watch how this phenotype executes step-by-step:
Step 1: Loading Phenotype Definition
Loading JSON definition and parsing cohort logic...
Step 2: Connecting to Database
Establishing connection to OMOP CDM database...
Step 3: Executing Cohort Logic
Running the cohort definition against patient data...
Step 4: Results Generated
Cohort execution completed successfully!
📊 Execution Results
🎯 Cohort Summary
Atlas-Compatible JSON Definition
This JSON can be imported into OHDSI Atlas for cohort creation:
📊 JSON Structure Analysis
🔍 Definition Components
Executable SQL Query
This SQL can be run directly against an OMOP CDM database:
📊 SQL Complexity Analysis
🔧 Query Components
Vocabulary Mappings
Standardized medical concepts used in this phenotype:
📊 Concept Distribution
🏥 Domain Coverage
📋 Concept Set Details
Sample Patient Charts
Review 5-10 randomly selected patients from this phenotype cohort:
🎯 Chart Review Summary
These patients were identified by the phenotype and represent typical cases in the cohort.
- ✅ Clinical timeline visualization
- ✅ Key events and diagnoses
- ✅ Treatment patterns
- ✅ Risk factors and comorbidities
📊 Patient Timeline Overview
👤 Individual Patient Charts
How to Execute This Phenotype
🌐 Method 1: OHDSI Atlas
- Open OHDSI Atlas
- Go to Cohort Definitions
- Import the JSON definition
- Generate cohort
💾 Method 2: Direct SQL
- Connect to OMOP CDM database
- Replace schema parameters
- Execute the SQL query
- Results in generated table
🔌 Method 3: WebAPI
- Use the JSON definition
- POST to /cohortdefinition
- Use returned ID for generation
🎯 Execution Best Practices
- Always validate your database schema before execution
- Test with a small subset of data first
- Monitor execution time and resource usage
- Review results for clinical validity
- Document any modifications made to the phenotype