Evidence map›Paper›PMID 42763348›Full record

ArticleDrug safety2026

A Text-Mining Algorithm for Identifying Drug Pairs Potentially Involved in Prescribing Cascades: Development and Internal Validation Within the Prescribing Inappropriateness Assessment (PINA) Digital Tool.

Massimo Carollo, Anna Forti, Salvatore Crisafulli, Luca L'Abbate, Irene Cristini, Federica Soardo, Marilisa Giustina Stano, David Bellantuono, Davide Benetti, Riccardo Lora and 1 more

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Article in Drug safety, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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11 authors.

Massimo CarolloDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Anna FortiDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Salvatore CrisafulliDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Luca L'AbbateDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Irene CristiniDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Federica SoardoDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Marilisa Giustina StanoDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
David BellantuonoDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Davide BenettiDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Riccardo LoraDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy.
Gianluca TrifiròDepartment of Diagnostics and Public Health, University of Verona, P.le L.A. Scuro 10, 37124, Verona, Italy. gianluca.trifiro@univr.it.ORCID http://orcid.org/0000-0003-1147-7296

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrescribing cascades (PCs) occur when an adverse drug reaction (ADR) is intentionally or unintentionally treated with an additional medication, potentially perpetuating inappropriate polypharmacy. Digital tools that systematically support the identification of potential PCs during medication review are lacking. Here, we describe the development of an algorithm designed to identify drug pairs potentially compatible with PCs and evaluate its classification performance.

methodsThe algorithm was developed as a component of the Prescribing INappropriateness Assessment (PINA) digital tool, a software platform designed to support clinicians in conducting medication reviews. The algorithm logic identifies potential PCs by detecting intersections between ADRs and therapeutic indications extracted from Italian Summaries of Product Characteristics (SmPCs) and encoded as Medical Dictionary for Regulatory Activities (MedDRA) terms using a natural language processing pipeline. Four algorithm configurations were tested, differing in the MedDRA semantic levels used for matching: (1) Preferred Terms (PTs) only; (2) PTs with semantic extension to High Level Terms (HLTs); (3) PTs with extension to Standardised MedDRA Queries (SMQs); and (4) PTs with combined HLT/SMQ extension. Performance was assessed against a reference standard comprising 69 literature-supported positive drug pairs and 138 randomly generated negative drug pairs in a 1:2 ratio. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, F

resultsAll models demonstrated excellent specificity (≥ 99%). Sensitivity increased with semantic expansion: 50.7% for PT-only matching, 73.9% for PT+HLT, 68.1% for PT+SMQ, and 85.5% for PT+HLT/SMQ. The combined HLT/SMQ extension model achieved the highest overall performance, with an accuracy of 94.7%, a PPV of 98.3%, an NPV of 93.2%, an F

conclusionsThe newly developed algorithm demonstrated high classification performance, particularly when PT-level matching was extended using both HLTs and SMQs. This MedDRA-driven approach based on regulatory data may represent a scalable and reproducible strategy for supporting structured medication review and facilitating the recognition and clinical evaluation of potential PCs.

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PMID42763348

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.