Optimal Search Filters for Renal Information in EMBASE

Background EMBASE is a popular database used to retrieve biomedical information. Our objective was to develop and test search filters to help clinicians and researchers efficiently retrieve articles with renal information in EMBASE. Study Design We used a diagnostic test assessment framework because filters operate similarly to screening tests. Settings & Participants We divided a sample of 5,302 articles from 39 journals into development and validation sets of articles. Index Test Information retrieval properties were assessed by treating each search filter as a “diagnostic test” or screening procedure for the detection of relevant articles. We tested the performance of 1,936,799 search filters made of unique renal terms and their combinations. Reference Standard & Outcome The reference standard was manual review of each article. We calculated the sensitivity and specificity of each filter to identify articles with renal information. Results The best renal filters consisted of multiple search terms, such as “renal replacement therapy,” “renal,” “kidney disease,” and “proteinuria,” and the truncated terms “kidney,” “dialy,” “neph,” “glomerul,” and “hemodial.” These filters achieved peak sensitivities of 98.7% (95% CI, 97.9-99.6) and specificities of 98.5% (95% CI, 98.0-99.0). The retrieval performance of these filters remained excellent in the validation set of independent articles. Limitations The retrieval performance of any search will vary depending on the quality of all search concepts used, not just renal terms. Conclusions We empirically developed and validated high-performance renal search filters for EMBASE. These filters can be programmed into the search engine or used on their own to improve the efficiency of searching. EMBASE is a popular database used to retrieve biomedical information. Our objective was to develop and test search filters to help clinicians and researchers efficiently retrieve articles with renal information in EMBASE. We used a diagnostic test assessment framework because filters operate similarly to screening tests. We divided a sample of 5,302 articles from 39 journals into development and validation sets of articles. Information retrieval properties were assessed by treating each search filter as a “diagnostic test” or screening procedure for the detection of relevant articles. We tested the performance of 1,936,799 search filters made of unique renal terms and their combinations. The reference standard was manual review of each article. We calculated the sensitivity and specificity of each filter to identify articles with renal information. The best renal filters consisted of multiple search terms, such as “renal replacement therapy,” “renal,” “kidney disease,” and “proteinuria,” and the truncated terms “kidney,” “dialy,” “neph,” “glomerul,” and “hemodial.” These filters achieved peak sensitivities of 98.7% (95% CI, 97.9-99.6) and specificities of 98.5% (95% CI, 98.0-99.0). The retrieval performance of these filters remained excellent in the validation set of independent articles. The retrieval performance of any search will vary depending on the quality of all search concepts used, not just renal terms. We empirically developed and validated high-performance renal search filters for EMBASE. These filters can be programmed into the search engine or used on their own to improve the efficiency of searching.