High-performance information search filters for acute kidney injury content in PubMed, Ovid Medline and Embase.
BACKGROUND
We frequently fail to identify articles relevant to the subject of acute kidney injury (AKI) when searching the large bibliographic databases such as PubMed, Ovid Medline or Embase. To address this issue, we used computer automation to create information search filters to better identify articles relevant to AKI in these databases.


METHODS
We first manually reviewed a sample of 22 992 full-text articles and used prespecified criteria to determine whether each article contained AKI content or not. In the development phase (two-thirds of the sample), we developed and tested the performance of >1.3-million unique filters. Filters with high sensitivity and high specificity for the identification of AKI articles were then retested in the validation phase (remaining third of the sample).


RESULTS
We succeeded in developing and validating high-performance AKI search filters for each bibliographic database with sensitivities and specificities in excess of 90%. Filters optimized for sensitivity reached at least 97.2% sensitivity, and filters optimized for specificity reached at least 99.5% specificity. The filters were complex; for example one PubMed filter included >140 terms used in combination, including 'acute kidney injury', 'tubular necrosis', 'azotemia' and 'ischemic injury'. In proof-of-concept searches, physicians found more articles relevant to topics in AKI with the use of the filters.


CONCLUSIONS
PubMed, Ovid Medline and Embase can be filtered for articles relevant to AKI in a reliable manner. These high-performance information filters are now available online and can be used to better identify AKI content in large bibliographic databases.
Division of Nephrology, Western University, London, ON, Canada.
Ainslie M
AM
Hildebrand
Arthur V
AV
Iansavichus
R Brian
RB
Haynes
Nancy L
NL
Wilczynski
Ravindra L
RL
Mehta
Chirag R
CR
Parikh
Amit X
AX
Garg
D058186:Acute Kidney Injury; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE / Q000706:statistics & numerical data*; D010506:Periodicals as Topic; D039781:PubMed / Q000706:statistics & numerical data*; D012372:ROC Curve
Embase; Medline; acute kidney injury; information retrieval; medical informatics