title: Identifying complications of interventional procedures from UK routine healthcare databases: a systematic search for methods using clinical codes.
abstract: BACKGROUND
  Several authors have developed and applied methods to routine data sets to identify the nature and rate of complications following interventional procedures. But, to date, there has been no systematic search for such methods. The objective of this article was to find, classify and appraise published methods, based on analysis of clinical codes, which used routine healthcare databases in a United Kingdom setting to identify complications resulting from interventional procedures.
  
  
  METHODS
  A literature search strategy was developed to identify published studies that referred, in the title or abstract, to the name or acronym of a known routine healthcare database and to complications from procedures or devices. The following data sources were searched in February and March 2013: Cochrane Methods Register, Conference Proceedings Citation Index - Science, Econlit, EMBASE, Health Management Information Consortium, Health Technology Assessment database, MathSciNet, MEDLINE, MEDLINE in-process, OAIster, OpenGrey, Science Citation Index Expanded and ScienceDirect. Of the eligible papers, those which reported methods using clinical coding were classified and summarised in tabular form using the following headings: routine healthcare database; medical speciality; method for identifying complications; length of follow-up; method of recording comorbidity. The benefits and limitations of each approach were assessed.
  
  
  RESULTS
  From 3688 papers identified from the literature search, 44 reported the use of clinical codes to identify complications, from which four distinct methods were identified: 1) searching the index admission for specified clinical codes, 2) searching a sequence of admissions for specified clinical codes, 3) searching for specified clinical codes for complications from procedures and devices within the International Classification of Diseases 10th revision (ICD-10) coding scheme which is the methodology recommended by NHS Classification Service, and 4) conducting manual clinical review of diagnostic and procedure codes.
  
  
  CONCLUSIONS
  The four distinct methods identifying complication from codified data offer great potential in generating new evidence on the quality and safety of new procedures using routine data. However the most robust method, using the methodology recommended by the NHS Classification Service, was the least frequently used, highlighting that much valuable observational data is being ignored.
authors.forename: Kim
authors.initials: K
authors.lastname: Keltie
authors.forename: Helen
authors.initials: H
authors.lastname: Cole
authors.forename: Mick
authors.initials: M
authors.lastname: Arber
authors.forename: Hannah
authors.initials: H
authors.lastname: Patrick
authors.forename: John
authors.initials: J
authors.lastname: Powell
authors.forename: Bruce
authors.initials: B
authors.lastname: Campbell
authors.affiliation: Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK. andrew.sims@nuth.nhs.uk.
authors.forename: Andrew
authors.initials: A
authors.lastname: Sims
mesh_terms: D059019:Clinical Coding; D016208:Databases, Factual; D003695:Delivery of Health Care; D057286:Electronic Health Records; D063005:Health Information Systems; D055991:Health Records, Personal; D006801:Humans; D007431:Intraoperative Complications / Q000453:epidemiology*; D019300:Medical Errors; D061214:Patient Safety; D011183:Postoperative Complications / Q000453:epidemiology*; D006113:United Kingdom / Q000453:epidemiology