Dialysis Search Filters for PubMed, Ovid MEDLINE, and Embase Databases

Summary Background and objectives Physicians frequently search bibliographic databases, such as MEDLINE via PubMed, for best evidence for patient care. The objective of this study was to develop and test search filters to help physicians efficiently retrieve literature related to dialysis (hemodialysis or peritoneal dialysis) from all other articles indexed in PubMed, Ovid MEDLINE, and Embase. Design, setting, participants, & measurements A diagnostic test assessment framework was used to develop and test robust dialysis filters. The reference standard was a manual review of the full texts of 22,992 articles from 39 journals to determine whether each article contained dialysis information. Next, 1,623,728 unique search filters were developed, and their ability to retrieve relevant articles was evaluated. Results The high-performance dialysis filters consisted of up to 65 search terms in combination. These terms included the words “dialy” (truncated), “uremic,” “catheters,” and “renal transplant wait list.” These filters reached peak sensitivities of 98.6% and specificities of 98.5%. The filters’ performance remained robust in an independent validation subset of articles. Conclusions These empirically derived and validated high-performance search filters should enable physicians to effectively retrieve dialysis information from PubMed, Ovid MEDLINE, and Embase.

Dialysis Search Filters for PubMed, Ovid MEDLINE, and Embase Databases Journal Articles