CDISC invites you to provide comments on the LOINC to LB Mapping file.
FDA will require the submission of Logical Observation Identifiers Names and Codes (LOINC®) within clinical LB domain datasets in new drug applications (NDAs), abbreviated new drug applications (ANDAs), and biologics license applications (BLAs) for studies starting after 15 March 2020 and for certain investigational new drugs (INDs) for studies starting after 15 March 2021.
A recommendations document produced by a LOINC working group, composed of representatives from FDA, NIH, CDISC, and Regenstrief Institute, called for the development of a mapping file to map the most commonly submitted LOINCs in clinical research to the associated CDISC Controlled Terminology components associated with each LOINC part.
LOINC to LB Mapping File
This mapping file is intended to show examples of LOINC code mappings to CDISC variables and terminology to aid researchers’ adoption of the FDA requirement. The file is composed of LOINCs from the 2005 CDISC-LOINC mapping project, the LOINC Top 2000 SI, the LOINC Top 2000 US, as well as CDISC LB domain datasets submitted to FDA from 2014 - 2016. It also includes more than 1400 LOINCs across the Chemistry, Hematology, Coagulation, Toxicology, Urinalysis, Serology, and Miscellaneous categories. The mapping file does not include LOINCs relevant to microbiology, pathology, genomics, vital signs, fetal/neonatal screening, or administrative questions as these can be found in other SDTM domains. Uncommon allergies are not included as we assume their submission in a dataset would be rare. The mapping file does not include deprecated or discouraged codes.
Lab vendors would gain the most usage out of this mapping as the lab vendor is expected to provide the LOINCs. At a minimum, lab vendors should provide the LOINCs included within this mapping, if available.
- View the draft: LOINC to LB Mapping File
- Instructions for providing comments: LOINC to LB Mapping Home
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Thank you for contributing your time and expertise. CDISC relies on your input to ensure neutral, consensus-based data standards are developed and adopted by a diverse global community interested in improving research processes and quality for the benefit of all.