Evidence map›Paper›PMID 38326827›Full record

ArticleBMC medical informatics and decision making2024

Evaluating MedDRA-to-ICD terminology mappings.

Xinyuan Zhang, Yixue Feng, Fang Li, Jin Ding, Danyal Tahseen, Ezekiel Hinojosa, Yong Chen, Cui Tao

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xinyuan ZhangMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Yixue FengSchool of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.
Fang LiMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Jin DingMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
Danyal TahseenMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Ezekiel HinojosaMcGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Yong ChenThe Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Cui TaoMcWilliam School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA. Cui.Tao@uth.tmc.edu.ORCID 0000-0002-4267-1924

Funding

Dynamic learning for post-vaccine event prediction using temporal information in VAERSR01AI130460 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI CHEN, YONG, TAO, CUI · 2017 to 2021
$3.4M
Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingRF1AG072799 · NIA · YALE UNIVERSITY · PI LIU, HONGFANG, TAO, CUI · 2021 to 2021
$2.4M
Facilitate Observational Studies of Alzheimer's Disease and Alzheimer's Disease-Related Dementias Using Ontology and Natural Language ProcessingR01AG072799 · NIA · YALE UNIVERSITY · PI HONGFANG LIU, Cui Tao · 2025 to 2026
$1.7M
NIAID NIH HHS R01 AI130460NIA NIH HHS R01 AG072799NIA NIH HHS RF1 AG072799NIH HHS R01AI130460
6 · The paper itself

Abstract

backgroundIn this era of big data, data harmonization is an important step to ensure reproducible, scalable, and collaborative research. Thus, terminology mapping is a necessary step to harmonize heterogeneous data. Take the Medical Dictionary for Regulatory Activities (MedDRA) and International Classification of Diseases (ICD) for example, the mapping between them is essential for drug safety and pharmacovigilance research. Our main objective is to provide a quantitative and qualitative analysis of the mapping status between MedDRA and ICD. We focus on evaluating the current mapping status between MedDRA and ICD through the Unified Medical Language System (UMLS) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We summarized the current mapping statistics and evaluated the quality of the current MedDRA-ICD mapping; for unmapped terms, we used our self-developed algorithm to rank the best possible mapping candidates for additional mapping coverage.

resultsThe identified MedDRA-ICD mapped pairs cover 27.23% of the overall MedDRA preferred terms (PT). The systematic quality analysis demonstrated that, among the mapped pairs provided by UMLS, only 51.44% are considered an exact match. For the 2400 sampled unmapped terms, 56 of the 2400 MedDRA Preferred Terms (PT) could have exact match terms from ICD.

conclusionSome of the mapped pairs between MedDRA and ICD are not exact matches due to differences in granularity and focus. For 72% of the unmapped PT terms, the identified exact match pairs illustrate the possibility of identifying additional mapped pairs. Referring to its own mapping standard, some of the unmapped terms should qualify for the expansion of MedDRA to ICD mapping in UMLS.

Indexed as

Adverse Drug Reaction Reporting SystemsInternational Classification of DiseasesAlgorithmsHumansPharmacovigilanceUnified Medical Language SystemInternational classification of diseases (ICD)Observational medical outcomes partnership common data model (OMOP CDM)Terminology mappingThe medical dictionary for regulatory activities (MedDRA)Unified medical language system (UMLS)

Identifiers

PMID38326827
PMCPMC10851449

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.