Background Deprescribing literature has been increasing rapidly. Our aim was to develop and validate search filters to identify articles on deprescribing in Medline via PubMed and in Embase via Embase.com. Methods Articles published from 2011 to 2020 in a core set of eight journals (covering fields of interest for deprescribing, such as geriatrics, pharmacology and primary care) formed a reference set. Each article was screened independently in duplicate and classified as relevant or non-relevant to deprescribing. Relevant terms were identified by term frequency analysis in a 70% subset of the reference set. Selected title and abstract terms, MeSH terms and Emtree terms were combined to develop two highly sensitive filters for Medline via Pubmed and Embase via Embase.com. The filters were validated against the remaining 30% of the reference set. Sensitivity, specificity and precision were calculated with their 95% confidence intervals (95% CI). Results A total of 23,741 articles were aggregated in the reference set, and 224 were classified as relevant to deprescribing. A total of 34 terms and 4 MeSH terms were identified to develop the Medline search filter. A total of 27 terms and 6 Emtree terms were identified to develop the Embase search filter. The sensitivity was 92% (95% CI: 83–97%) in Medline via Pubmed and 91% (95% CI: 82–96%) in Embase via Embase.com. Conclusions These are the first deprescribing search filters that have been developed objectively and validated. These filters can be used in search strategies for future deprescribing reviews. Further prospective studies are needed to assess their effectiveness and efficiency when used in systematic reviews.

Two deprescribing search filters for MEDLINE and one deprescribing search filter for Embase have been recently developed, including objectively developed search filters. The objective of this case study was to implement these three deprescribing search filters in systematic review (SR) search strategies and to assess their effect on performances. SR that independently developed original search strategies (OSS) were selected. The deprescribing filters were implemented in each OSS, generating two implemented search strategies (ISS1 and ISS2) in MEDLINE and one ISS (ISS3) in Embase. OSS were re‐run on the same date as ISS. The performances of ISS and OSS were calculated and compared. Two SR were included (SR1 and SR2). For MEDLINE, SR1 included 12 articles. The sensitivity was 50% for OSS, 58% for ISS1 and 42% for ISS2. SR2 included four articles. The sensitivity of OSS, ISS 1 and 2 was 25%. For Embase, SR1 included 12 articles. The sensitivity was 33% for OSS and 58% for ISS3. SR2 included four articles. None of the four included articles were retrieved with OSS or ISS3. While sensitivity of OSS was moderate, the objectively developed deprescribing filters maintained or slightly improved this sensitivity when implementing.

Deprescribing search filters aiming at maximizing sensitivity for MEDLINE and for Embase were recently developed. Simultaneously, The US Deprescribing Network (USden) developed a deprescribing search strategy that included a deprescribing search filter for MEDLINE. The aim of this case study was to implement these deprescribing search filters in original search strategies from deprescribing related systematic reviews (SRs) and to calculate their performances. Two deprescribing SRs were included. Authors were asked to repeat the selection process described in SRs original methods. Performances of search strategies implemented with deprescribing search filters (ISS) were calculated and compared to original search strategies (OSS). In MEDLINE, sensitivity for SR 1 was 50% for OSS (Precision: 2.8%), 58% for ISS with maximised sensitivity filter (Precision: 1.7%) and 42% for ISS with USden filter (Precision: 5.1%). Sensitivity for SR 2 was 25% for all search strategies (Precision: 0.1%, 0.2% and 1,2% respectively). In Embase, sensitivity for SR 1 was 33% (Precision: 4,1%) for OSS and 58% for ISS (Precision 2.1%). No articles were included through Embase search strategies for SR 2. Using maximized sensitivity deprescribing filters may increase the exhaustivity of deprescribing SRs. Precision offered by the USDeN deprescribing filter is a convenient alternative for non-systematic reviews.