Informing a medical subject heading (MeSH) request: The 'Prescribing cascades' case study

Abstract

Background: 'Prescribing cascades' describes initiation of new treatments in response to adverse drug effects, often misinterpreted as new conditions. Although Medical Subject Headings (MeSH) enable accurate indexing and retrieval, no MeSH term exists for this concept. This study assessed the need for a dedicated MeSH term and its potential impact on indexing consistency. Methods: A cross-sectional analysis was conducted using 139 articles from a published scoping review. PubMed metadata provided publication details and indexing status. MEDLINE-indexed articles were analyzed for MeSH attribution. Associations between the presence of keywords (inapprop-, adverse, cascade) in titles/abstracts and specific MeSH terms were quantified using odds ratios (OR) with 95% confidence intervals (CI) (R/ RStudio). Results: Of 139 articles included in the scoping review, 119 were indexed in PubMed and 106 in MEDLINE, with 1055 MeSH terms covering 313 unique terms. The most prevalent were Drug-Related Side Effects and Adverse Reactions (32.1%), Polypharmacy (30.2%), and Inappropriate Prescribing (26.4%). Indexing was manual in 47 cases, automated in 45, and curated in 14. After 2022, automated indexing reduced use of Drug Interactions and Medication Errors. Only three associations demonstrated predictive values: the root inapprop- in abstracts predicted the MeSH Inappropriate Prescribing (OR = 5.1 [95% CI 1.9-13.7]); inapprop- in titles for Potentially Inappropriate Medication List (OR = 204 [95% CI 9.1-4551.3]); and adverse in abstracts for Drug-Related Side Effects and Adverse Reactions (OR = 2.8 [95% CI 1.2-6.7]). The word cascade in titles was associated with Drug-Related Side Effects and Adverse Reactions (OR = 3.6 [95% CI 1.4-9.2]). Conclusion: Current evidence does not support a dedicated MeSH for 'prescribing cascades' as existing descriptors are sufficient to capture its main dimensions. Further research should prioritize definitional consensus and compositional indexing strategies to enhance retrieval accuracy.