mCIDE Minimum Common ICU Data Elements
The cornerstone of scientific integrity in critical care data science - standardized variables that enable reproducible, multi-center research
What is mCIDE?
The minimum Common ICU Data Elements (mCIDE) represent a carefully curated set of standardized variables identified by the CLIF consortium as essential for studying critical illness. These elements form the foundation of the CLIF data model, ensuring that data collected across different institutions can be meaningfully compared and analyzed together.
By defining common vocabularies, units of measurement, and permissible values for key clinical variables, mCIDE enables true federated analytics while maintaining the highest standards of scientific rigor.
mCIDE implements the concept of Common Data Elements (CDEs)—data elements defined and used consistently across multiple studies to standardize data collection. This approach aspires to adhere to the 2023 NIH Data Management and Sharing Policy and the FAIR data principles (Findable, Accessible, Interoperable, Reusable), ensuring that critical care research data can be effectively shared, discovered, and reused across the scientific community.
Why mCIDE is Essential
Scientific Integrity
Ensures reproducible research by standardizing how critical variables are defined and measured across all participating sites.
Data Harmonization
Bridges differences in EHR systems and local practices by mapping diverse source data to common standardized categories.
Federated Analytics
Enables meaningful multi-center analyses by ensuring that the same clinical concepts are represented consistently.
Quality Benchmarking
Allows institutions to compare their outcomes and practices against peers using standardized metrics.
Reduced Complexity
Simplifies ETL development by providing clear mappings and standardized vocabularies for common ICU variables.
Accelerated Research
Speeds time from hypothesis to insight by eliminating data wrangling and standardization bottlenecks.
Implementing mCIDE
For Data Engineers
- Map local values to mCIDE categories using provided CSV files
- Preserve original values in _name fields
- Use CLIF Lighthouse AND CLIFpy to validate mappings
For Researchers
- Query using _category fields for standardized analyses
- Reference mCIDE documentation in methods sections
- Contribute new categories through GitHub PRs
Ready to Implement mCIDE?
Join the growing community of institutions committed to reproducible critical care research