Literature DB >> 32578157

Detecting Medicine Safety Signals Using Prescription Sequence Symmetry Analysis of a National Prescribing Data Set.

Clare E King1, Nicole L Pratt2, Nichole Craig3, Loc Thai3, Margaret Wilson4, Neillan Nandapalan4, Lisa Kalisch Ellet2, Eirene C Behm4.   

Abstract

INTRODUCTION: Medicine safety signal detection methods employed by the medicine regulator in Australia (Therapeutic Goods Administration [TGA], Department of Health) rely predominantly on analysis of spontaneous adverse event (AE) reports, sponsor notifications or information shared by international agencies. The limitations of these methods and the availability of large administrative health data sets has given rise to greater interest in the use of administrative health data to support pharmacovigilance (PV).
OBJECTIVE: We explored whether prescription sequence symmetry analysis (PSSA) of Pharmaceutical Benefits Scheme (PBS) data can enhance signal detection by the TGA, using the AE, heart failure (HF) as a case study.
METHODS: We applied the PSSA method to all single-ingredient medicines dispensed under the PBS between 2012 and 2016, using furosemide initiation as a proxy for new-onset HF. A signal was considered present if the lower limit of the 95% confidence interval for the adjusted sequence ratio was > 1. We excluded medicines known to cause HF, indicated for HF treatment or indicated for diseases that may contribute to HF.
RESULTS: Of the 654 tested medicines, 26 potential new HF signals were detected by PSSA. Five signals had additional support for the possible association provided by biological plausibility, consistency and disproportionate reporting of cases of HF to the TGA and the World Health Organization; and clinical impact.
CONCLUSION: PSSA was able to identify potential signals for further evaluation. With the increasing availability of different administrative health data sources, the strengths and weaknesses of methods used to analyse these data for the purpose of regulatory PV should be evaluated.

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Year:  2020        PMID: 32578157     DOI: 10.1007/s40264-020-00940-5

Source DB:  PubMed          Journal:  Drug Saf        ISSN: 0114-5916            Impact factor:   5.606


  3 in total

1.  The Medicines Intelligence Centre of Research Excellence: Co-creating real-world evidence to support the evidentiary needs of Australian medicines regulators and payers.

Authors:  Nicole Pratt; Ximena Camacho; Claire Vajdic; Louisa Degenhardt; Tracey-Lea Laba; Jodie Hillen; Christopher Etherton-Beer; David Preen; Louisa Jorm; Natasha Donnolley; Alys Havard; Sallie-Anne Pearson
Journal:  Int J Popul Data Sci       Date:  2022-06-13

2.  A data-driven pipeline to extract potential adverse drug reactions through prescription, procedures and medical diagnoses analysis: application to a cohort study of 2,010 patients taking hydroxychloroquine with an 11-year follow-up.

Authors:  P Sabatier; M Wack; J Pouchot; N Danchin; A S Jannot
Journal:  BMC Med Res Methodol       Date:  2022-06-08       Impact factor: 4.612

3.  Prescribing cascades in community-dwelling adults: A systematic review.

Authors:  Ann S Doherty; Faiza Shahid; Frank Moriarty; Fiona Boland; Barbara Clyne; Tobias Dreischulte; Tom Fahey; Seán P Kennelly; Emma Wallace
Journal:  Pharmacol Res Perspect       Date:  2022-10
  3 in total

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