Summary
Background: Eligibility criteria (EC) of clinical trials play a key role in selecting appropriate
study candidates and the validity of the outcome of a clinical trial. However, in
most cases EC are provided in unstandardised ways such as free text, which raises
significant challenges for machine-readability.
Objectives: To establish a list of most frequent medical concepts in clinical trials with semantic
annotations. This concept list contributes to standardisation of EC and identifies
relevant data items in electronic health records (EHRs) for clinical research. The
coverage of the list in two major clinical vocabularies, MeSH and SNOMED-CT, will
be assessed.
Methods: Four hundred and twenty-fivec linical trials conducted between 2000 and 2011 at a German university hospital were
analysed. 6671 EC were manually annotated by a medical coder using Concept Unique
Identifiers (CUIs) provided by the Unified Medical Language System. Two physicians
performed a semi-automatic CUI code revision. Concept frequency was analysed and clusters
of concepts were manually identified.A binomial significance test was applied to quantify
coverage differences of the most frequent concepts in MeSH and SNOMED-CT.
Results: Based on manual medical coding of 425 clinical trials, 7588 concepts were identified,
of which 5236 were distinct. A top 100 list containing 101 most frequent medical concepts
was established. The concepts of this list cover 25 % of all concept occur-rences
in all analysed clinical trials. This list reveals six missing entries in SNOMED-CT,
12 in MeSH. The median of EC frequency per trial has increased throughout the trial
years (2000 –2005: 8 EC/trial, 2011: 14 EC/ trial).
Conclusions: Relatively few concepts cover one quarter of concept occurrences that represent EC
in recent studies. Therefore, these concepts can serve as candidate data elements
for integration into EHRs to optimise patient recruitment in clinical research.
Keywords
Eligibility criteria - CUI - data items - clinical trials - ODM - UMLS - MeSH - SNOMED-CT