title: Frequency analysis of medical concepts in clinical trials and their coverage in MeSH and SNOMED-CT.
abstract: 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.
  
  
  OBJECTIVE
  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-five clinical 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 occurrences 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.
authors.affiliation: Julian Varghese, University of Muenster, Institute of Medical Informatics, Albert-Schweitzer-Campus 1, Gebäude A11, 48149 Münster, Germany, E-mail: Julian.Varghese@ukmuenster.de.
authors.forename: J
authors.initials: J
authors.lastname: Varghese
authors.forename: M
authors.initials: M
authors.lastname: Dugas
mesh_terms: D059019:Clinical Coding; D002986:Clinical Trials as Topic / Q000145:classification*; D057286:Electronic Health Records; D046650:Medical Subject Headings; D039061:Systematized Nomenclature of Medicine
keywords: CUI; Eligibility criteria; MeSH; ODM; SNOMED-CT; UMLS; clinical trials; data items