Evaluating the use of prescription sequence symmetry analysis as a pharmacovigilance tool: A scoping review

https://doi.org/10.1016/j.sapharm.2021.08.003Get rights and content

Highlights

  • The PSSA design is a pharmacovigilance tool to identify prescribing cascade signals.

  • Many signals linked to a wide array of organs have been identified using PSSA.

  • PSSA has shown moderate sensitivity and specificity in identifying adverse events.

  • Potential sources of time-varying confounding should be considered when using PSSA.

  • Consistent reporting of results is needed when disseminating PSSA findings.

Abstract

Background

The (prescription) sequence symmetry analysis (PSSA) design has been used to identify potential prescribing cascade signals by assessing the prescribing sequence of an index drug relative to a marker drug presumed to treat an adverse drug event provoked by the index drug.

Objectives

This review aimed to explore the use of the PSSA design as a pharmacovigilance tool with a particular focus on the breadth of identified signals and advances in PSSA methodology.

Methods

We searched Embase, PubMed/Medline, Google Scholar, Web of Science and grey literature to identify studies that used the PSSA methodology. Two reviewers independently extracted relevant data for each included article. Study characteristics including signals identified, exposure time window, stratified analyses, and use of controls were extracted.

Results

We identified 53 studies which reported original results obtained using PSSA methodology or quantified the validity of components of the PSSA design. Of those, nine studies provided validation metrics showing reasonable sensitivity and high specificity of PSSA to identify prescribing cascade signals. We identified 340 unique index drug – marker drug signals published in the PSSA literature, representing 281 unique index – marker pharmacological class dyads (i.e., unique fourth-level Anatomical Therapeutic Chemical [ATC] classification dyads). Commonly observed signals were identified for index drugs acting upon the nervous system (34%), cardiovascular system (21%), and blood and blood-forming organs (15%), and many marker drugs were related to the nervous system (25%), alimentary tract and metabolism (23%), cardiovascular system (17%), and genitourinary system and sex hormones (14%). Negative controls and positive controls were utilized in 21% and 13% of studies, respectively.

Conclusions

The PSSA methodology has been used in 53 studies worldwide to detect and evaluate over 300 unique prescribing cascades signals. Researchers should consider sensitivity analyses incorporating negative and/or positive controls and additional time windows to evaluate time-varying biases when designing PSSA studies.

Introduction

The (prescription) symmetry sequence analysis (PSSA) method is a pharmacovigilance tool employed to rapidly identify adverse drug event adverse drug event signals and potential prescribing cascades within large administrative health databases.1 Prescribing cascades occur when an adverse drug event is misinterpreted as a new medical condition, prompting practitioners to prescribe a new medication to treat effects caused by the first medication.2 This often suboptimal prescribing practice increases a patient's risk of adverse drug events, either from the potentiation of adverse drug events caused by the first medication or from additional adverse drug events from the new medication.2 Polypharmacy, which may be a result of prescribing cascades,3 has also been associated with an increased risk for drug-drug interactions and can negatively impact patients' adherence to their current medications.4, 5, 6, 7, 8 Furthermore, prescribing cascades, especially when considered problematic, have the potential to increase healthcare utilization and costs due to the increased medication burden among patients.3

The PSSA method exists within the family of self-controlled methods and employs a case-only design including only patients who are users of both drugs of interest (i.e., index drug and marker [e.g., outcome] drug).9,10 Conceptually, PSSA is applied to investigate a scenario where drug B (marker drug) is prescribed to treat an adverse drug event possibly caused by drug A (index drug).9 For example, the PSSA method was first used to investigate whether beta-blockers (index drug) have depression-provoking effects and lead to an excess risk of being prescribed antidepressants (marker drug).1 In assessing the association between the index drug and marker drug, the PSSA uses a population of new users of both index drug and marker drug within a given timeframe and compares the number of subjects who used the index drug before the marker drug to the number of users who used the marker drug before the index drug.1 Notably, individuals who initiate both the index drug and marker drug on the same day are not included in the analysis as it is impossible to distinguish a prescribing order for such individuals.9,10 Therefore, risk is estimated by calculating the ratio (crude sequence ratio [cSR]; Fig. 1) of subjects who initiate index drug before marker drug to subjects who initiate marker drug before index drug, while accounting for secular trends of prescribing of these medications over time (adjusted sequence ratio [aSR]; Appendix A).

If there is no association between index drug and the initiation of the marker drug, symmetry is expected as there should be equal chances of initiating the marker drug before or after the index drug. However, if the index drug does indeed increase the risk of an adverse drug event leading to the initiation of a marker drug, asymmetry will occur with an accompanying aSR and lower limit of confidence interval (CI) greater than 1. Because PSSA includes only those individuals who are users of both the index drug and marker drug, time-invariant patient characteristics like age, sex, and other demographic and environmental factors are inherently controlled.1 In other words, factors that are stable over time within the study time window cannot predict the prescribing order of index drug and marker drug for a given individual, since every individual included in the study is required to be started on both index drug and marker drug.1

The objective of this scoping review is to explore published PSSA studies and associated index drug – marker drug dyad signals to expand the current list of potential prescribing cascades.11,12 Additionally, we hope to describe the utility and methodological advances of PSSA to be used to investigate future potential prescribing cascades in pharmacovigilance studies.

Section snippets

Methods

This scoping review adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist.13

Included Prescription Sequence Symmetry Analysis Studies

The flowchart of study inclusion is illustrated in Fig. 2. A total of 678 studies were identified during the study period across all databases and through reference screening. After removing duplicates and screening for eligibility, 53 studies remained that met the inclusion criteria.1,15−55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66 Among those, 47 studies reported original PSSA results (e.g., aSRs for signals evaluated/detected),1,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30

Discussion

In this review, we identified a number of PSSA studies which aimed to detect or evaluate prescribing cascades or aimed to investigate the utility of this methodology as a pharmacovigilance tool. Other methods have been used to identify and evaluate prescribing cascades, including cohort studies, cross-sectional studies, and case reports.67, 68, 69, 70, 71, 72, 73, 74 However, the PSSA design has a number of key characteristics that contribute to its utility in detecting and evaluating

Conclusion

The PSSA methodology has been used as a pharmacovigilance tool to screen for potential prescribing cascades using prescription data. Since its development, over 300 prescribing cascade signals have been detected and/or evaluated. Nevertheless, key considerations should be taken into account when designing a study using the PSSA design with particular attention on sensitivity analyses to evaluate time-varying biases. Furthermore, high-quality reporting of PSSA results is important to provide

CRediT author statement

Earl J. Morris: Conceptualization, Investigation, Formal Analysis, Writing – Original Draft, Review & Editing, Visualization; Josef Hollmann: Conceptualization, Investigation, Writing – Review & Editing; Ann-Kathrin Hofer: Investigation, Writing – Review & Editing; Hemita Bhagwandass: Investigation, Writing – Review & Editing; Razanne Oueini: Investigation, Writing – Review & Editing; Lauren E. Adkins: Data Curation; Jesper Hallas: Conceptualization, Writing – Review & Editing; Scott M. Vouri:

Funding

The research presented in this manuscript did not receive funding from any public, private, or not-for-profit agency.

Declaration of competing interest

The authors have declared no conflicts of interest for this article.

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