Evaluating MedDRA-to-ICD terminology mappings.

Area of Science:

• Medical Informatics
• Biomedical Data Science
• Health Data Standardization

Background:

• Data harmonization is essential for reproducible and collaborative research in the big data era.
• Terminology mapping, specifically between Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD), is critical for drug safety and pharmacovigilance.
• Current mapping efforts require quantitative and qualitative analysis to ensure data consistency.

Purpose of the Study:

• To quantitatively and qualitatively analyze the current mapping status between MedDRA and ICD.
• To evaluate the quality of existing MedDRA-ICD mappings within the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM).
• To identify potential candidates for additional mapping coverage of unmapped terms using a self-developed algorithm.

Main Methods:

• Analysis of existing MedDRA-ICD mapped pairs within UMLS and OMOP CDM.
• Systematic quality assessment of mapped pairs to determine the degree of match (e.g., exact match).
• Application of a proprietary algorithm to a sample of unmapped MedDRA Preferred Terms (PT) to identify potential ICD mapping candidates.

Main Results:

• The current MedDRA-ICD mapping covers 27.23% of MedDRA Preferred Terms (PT).
• Among the mapped pairs evaluated, only 51.44% were identified as exact matches, highlighting quality issues.
• Analysis of 2400 unmapped MedDRA PTs revealed 56 potential exact match candidates in ICD.

Conclusions:

• Existing MedDRA-ICD mappings often lack exactness due to differences in data granularity and focus.
• A significant portion (72%) of unmapped MedDRA PTs show potential for additional exact matches, suggesting opportunities for mapping expansion.
• The findings support the expansion of MedDRA to ICD mapping within UMLS based on established mapping standards.