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Evaluation

Oral Presentation

Session: On Optimizing Outcomes for Older Adults

Using Prescription Symmetry Sequence Analysis in the context of Antiparkinsonian Treatments in the United Kingdom

Monday, August 26, 2024
6:15 PM – 6:30 PM CEST
Location: Room II

    Presenting Author(s)

  • Xihang Chen, MMath (he/him/his)

    Research Assistant in Health Data Science
    Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, United Kingdom

    Slides

    • Co-Author(s)

  • Berta Raventós, PhD

    Postdoctoral researcher
    Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Spain

  • TS

    Ty Stanford

    NHMRC Medicines Intelligence Centre of Research Excellence, Australia

  • Daniel Prieto-Alhambra, MD, MSc, PhD (he/him/his)

    Professor of Pharmaco-and Device Epidemiology
    University of Oxford
    University of Oxford
    Oxford, United Kingdom

  • EB

    Edward Burn, PhD

    Senior Researcher in Epidemiology and Health Economics
    University of Oxford, United Kingdom

  • Martí Català, PhD (he/him/his)

    Senior Data Scientist
    University of Oxford
    Oxford, United Kingdom

  • NP

    Nicole L. Pratt, FISPE

    Professor
    Quality Use of Medicines and Pharmacy Research Centre, University of South Australia
    Adelaide, Australia

  • LM

    Laura Molina Porce

    Neurological Tissue Bank of the Biobanc-Hospital Clinic-Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer (FRCB-IDIBAPS), Spain

  • Annika M. Jödicke, PhD (she/her/hers)

    Senior Researcher in Pharmacoepidemiology
    University of Oxford
    Oxford, United Kingdom

  • Danielle Newby, PhD

    Senior Postdoctoral Researcher in Real World Evidence
    University of Oxford, United Kingdom

Background: Treatment cascades occur when side effects of a prescribed drug are misinterpreted as a new condition, leading to further prescriptions to treat these Adverse Drug Events (ADEs). Prescription Sequence Symmetry Analysis (PSSA) uses Real World Data to detect such ADEs by considering the symmetry between the initiations of two drugs whilst taking into consideration of prescribing trends. The use of PSSA to investigate drugs that may induce Parkinson-like symptoms is currently unexplored.

Objectives: To use hypotheses to detect the signals between the potential drugs that are known to cause Parkinson-like symptoms and antiparkinsonian treatments from the United Kingdom.

Methods: The participants in this study were Parkinson’s Disease patients from the CPRD GOLD database who initiated an antiparkinsonian drug (levodopa, MAO-B inhibitors, dopamine agonists, COMT inhibitors and amantadine) from 2007 to 2021.

The index drugs included were selected based on literature evidence that they induce Parkinson-like symptoms (atypical and typical antipsychotics, calcium channel blockers (CCB) and antiemetics). Patients were included if they had at least one year of prior observation with one year of washout for each index and marker drugs and a time gap between the two initiations of one year. To validate our results, PSSA was performed using positive and negative benchmarks. Adjusted sequence ratios (aSR) with 95% CI were calculated, with the values being greater than one signalling a prescription cascade. All the analyses are completed using the CohortSymmetry R package (https://github.com/oxford-pharmacoepi/CohortSymmetry).

Results: The positive benchmark (amiodarone to thyroxine) showed an aSR of 8.35 (95% CI: 7.56 – 9.23) whereas the negative benchmark (amiodarone to allopurinol) showed an aSR of 0.93 (95% CI: 0.80 – 1.08).

The highest aSR observed was CCB to antiparkinsonian drugs (aSR: 2.00; 95% CI: 1.55 – 2.59). Typical antipsychotics and antiemetics also gave a positive signal of an aSR of 1.99 (95% CI: 1.75 – 2.26) and 1.42 (95% CI: 1.17 – 1.73) respectively. Conversely, atypical antipsychotics did not produce such signal (aSR: 1.29; 95% CI: 0.74 – 1.78).

On the ingredient level, prochlorperazine, metoclopramide and cinnarizine to levodopa gave an aSR of 2.34 (95%CI: 2.02-2.73), 1.45 (95%CI: 1.19-1.76) and 2.22 (95% CI: 1.70 – 2.89) respectively.

Conclusions: Our results confirm the literature evidence that CCB and antiemetics can show a positive adverse event signal. Whereas typical antipsychotics showed a stronger positive signal than atypical antipsychotics. This study demonstrated PSSA could be used to detect potential prescription cascades in the context of antiparkinsonian drugs and could give an insight into the management of such treatments.