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

The evolving role of disproportionality analysis in pharmacovigilance

INTRODUCTION

From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated.

AREAS COVERED

This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data.

EXPERT OPINION

Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.

Prescribing cascades in ambulatory care: A structured synthesis of evidence

The strength of evidence for specific ambulatory care prescribing cascades, in which a marker drug is used to treat an adverse event caused by an index drug, has not been well characterized. To perform a structured, systematic, and transparent review of the evidence supporting ambulatory care prescribing cascades. Ninety-four potential prescribing cascades identified through a previously published systematic review. Systematic search of the literature to further characterize prescribing cascades. (1) Grading of evidence based on observational studies investigating associations between index and marker drugs, including: Level I-strong evidence [i.e. multiple high-quality studies]; Level II-moderate evidence [i.e. single high-quality study]; Level III-fair evidence [no high-quality studies but one or more moderate-quality studies]; and Level IV-poor evidence [other]. (2) Listing of the adverse event associated with the index drug in the product's United States Food and Drug Administration (FDA) label. (3) Synthesis of the evidence supporting mechanisms linking index drugs and associated adverse events. Of 99 potential cascades, 94 were supported by one or more confirmatory observational studies and were therefore included in this review. The 94 cascades related to 30 types of adverse drug reactions affecting 10 different anatomic/physiologic systems and were investigated by a total of 88 confirmatory studies, including prescription sequential symmetry analysis (n = 51), cohort (n = 30), and case-control (n = 7) studies. Overall, the evidence from observational studies was strong for 18 (19.1%) prescribing cascades, moderate for 61 (64.9%), fair for 13 (13.8%), and poor for 2 (2.1%). Although the evidence supporting mechanisms that link index drugs and associated adverse events was variable, FDA labels included information about the adverse event associated with the index drug for most (n = 86) but not all of the 94 prescribing cascades. Although we identified 18 of 94 prescribing cascades supported by strong clinical evidence and most adverse events associated with index drugs are included in FDA label, the evidentiary basis for prescribing cascades varies, with many requiring further evidence of clinical relevance.

Chronic Obstructive Pulmonary Disease Adverse Event Signals Associated with Potential Inhibitors of Glutathione Peroxidase 1: A Sequence Symmetry Analysis

BACKGROUND AND OBJECTIVE

Prior molecular modelling analysis identified several medicines as potential inhibitors of glutathione peroxidase 1 (GPx1) which may contribute to development or progression of chronic obstructive pulmonary disease (COPD). This study investigates 40 medicines (index medicines) for signals of COPD development or progression in a real-world dataset.

METHODS

Sequence symmetry analysis (SSA) was conducted using a 10% extract of Australian Pharmaceutical Benefits Scheme (PBS) claims data between January 2013 and September 2019. Patients must have been initiated on an index medicine and a medicine for COPD development or progression within 12 months of each other. Sequence ratios were calculated as the number of patients who initiated an index medicine followed by a medicine for COPD development or progression divided by the number who initiated the index medicine second. An adjusted sequence ratio (aSR) was calculated which accounted for changes in prescribing trends. Adverse drug event signals (ADEs) were identified where the aSR lower 95% confidence interval (CI) was greater than 1.

RESULTS

Twenty-one of 40 (53%) index medicines had at least one ADE signal of COPD development or progression. Signals of COPD development, as identified using initiation of tiotropium, were observed for atenolol (aSR 1.32, 95% CI 1.23-1.42) and naproxen (aSR 1.14, 95% CI 1.06-1.23). Several signals of COPD progression were observed, including initiation of fluticasone propionate/salmeterol following initiation of atenolol (aSR 1.44, 95% CI 1.30-1.60) and initiation of aclidinium/formoterol following initiation of naproxen (aSR 2.21, 95% CI 1.34-3.65).

CONCLUSION

ADE signals were generated for several potential GPx1 inhibitors; however, further validation of signals is required in large well-controlled observational studies.

A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data

Purpose

Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating data-mining tools, and evaluate adaptability of such system.

Patients and Methods

Based on relevant guidelines and structural frameworks in Taiwan's pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan's National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system.

Results

Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation.

Conclusion

The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.

Prescribing cascades in community-dwelling adults: A systematic review

The misattribution of an adverse drug reaction (ADR) as a symptom or illness can lead to the prescribing of additional medication, referred to as a prescribing cascade. The aim of this systematic review is to identify published prescribing cascades in community-dwelling adults. A systematic review was reported in line with the PRISMA guidelines and pre-registered with PROSPERO. Electronic databases (Medline [Ovid], EMBASE, PsycINFO, CINAHL, Cochrane Library) and grey literature sources were searched. Inclusion criteria: community-dwelling adults; risk-prescription medication; outcomes-initiation of new medicine to "treat" or reduce ADR risk; study type-cohort, cross-sectional, case-control, and case-series studies. Title/abstract screening, full-text screening, data extraction, and methodological quality assessment were conducted independently in duplicate. A narrative synthesis was conducted. A total of 101 studies (reported in 103 publications) were included. Study sample sizes ranged from 126 to 11 593 989 participants and 15 studies examined older adults specifically (≥60 years). Seventy-eight of 101 studies reported a potential prescribing cascade including calcium channel blockers to loop diuretic (n = 5), amiodarone to levothyroxine (n = 5), inhaled corticosteroid to topical antifungal (n = 4), antipsychotic to anti-Parkinson drug (n = 4), and acetylcholinesterase inhibitor to urinary incontinence drugs (n = 4). Identified prescribing cascades occurred within three months to one year following initial medication. Methodological quality varied across included studies. Prescribing cascades occur for a broad range of medications. ADRs should be included in the differential diagnosis for patients presenting with new symptoms, particularly older adults and those who started a new medication in the preceding 12 months.

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

Regulators and payers play a pivotal role in facilitating timely and affordable access to safe and efficacious medicines. They use evidence generated from randomised clinical trials (RCTs) to support decisions to register and subsidise medicines. However, at the time of registration and subsidy approval, regulators and payers face uncertainty about how RCT outcomes will translate to real-world clinical practice. In response to this situation, medicines policy agencies worldwide have endorsed the use of real-world data (RWD) to derive novel insights on the use and outcomes of prescribed medicines. Recent reforms around data availability and use in Australia are creating unparalleled data access and opportunities for Australian researchers to undertake large-scale research to generate evidence on the safety and effectiveness of medicines in the real world. Highlighting the critical importance of research in this area, Quality Use of Medicines and Medicine Safety was announced as Australia's 10th National Health Priority in 2019. The National Health and Medical Research Council, Medicines Intelligence Centre of Research Excellence (MI-CRE) has been formed to take advantage of the renewed focus on quality use of medicines and the changing data landscape in Australia. It will generate timely research supporting the evidentiary needs of Australian medicines regulators and payers by accelerating the development and translation of real-world evidence on medicines use and outcomes. MI-CRE is developing a coordinated approach to identify, triage and respond to priority questions where there are significant uncertainties about medicines use, (cost)-effectiveness, and/or safety and creating a data ecosystem that will streamline access to Australian data to enable researchers to generate robust evidence in a timely manner. This paper outlines how MI-CRE will partner with policy makers, clinicians, and consumer advocates to leverage real-world data to co-create real-world evidence, to improve quality use of medicines and reduce medicine-related harm.

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

Context

Real-life data consist of exhaustive data which are not subject to selection bias. These data enable to study drug-safety profiles but are underused because of their temporality, necessitating complex models (i.e., safety depends on the dose, timing, and duration of treatment). We aimed to create a data-driven pipeline strategy that manages the complex temporality of real-life data to highlight the safety profile of a given drug.

Methods

We proposed to apply the weighted cumulative exposure (WCE) statistical model to all health events occurring after a drug introduction (in this paper HCQ) and performed bootstrap to select relevant diagnoses, drugs and interventions which could reflect an adverse drug reactions (ADRs). We applied this data-driven pipeline on a French national medico-administrative database to extract the safety profile of hydroxychloroquine (HCQ) from a cohort of 2,010 patients.

Results

The proposed method selected eight drugs (metopimazine, anethole trithione, tropicamide, alendronic acid & colecalciferol, hydrocortisone, chlormadinone, valsartan and tixocortol), twelve procedures (six ophthalmic procedures, two dental procedures, two skin lesions procedures and osteodensitometry procedure) and two medical diagnoses (systemic lupus erythematous, unspecified and discoid lupus erythematous) to be significantly associated with HCQ exposure.

Conclusion

We provide a method extracting the broad spectrum of diagnoses, drugs and interventions associated to any given drug, potentially highlighting ADRs. Applied to hydroxychloroquine, this method extracted among others already known ADRs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01628-3.

• The challenge of drug-safety signal detection methods is to handle four types of difficulties:

○ The data source, the study of long-term adverse drug reactions or effects not suspected by healthcare professionals, requires the use of a real-life data source.

○ The consideration of a broad spectrum of potential adverse drug reactions (ADRs), and not only candidate ADRs.

○ The temporal impact (meaning that safety depends on the dose, date and duration of treatment).

○ The difference between true ADRs and disease natural course.

• We aimed to create a data-driven pipeline strategy, without any assumption of any ADRs, which take into account the complex temporality of real-life data to provide the safety profile of a given drug.

• Our pipeline used three sources of real-life data to establish a safety profile of a given drug: drug prescriptions, procedures and medical diagnoses.

• We successfully applied our data-driven pipeline strategy to hydroxychloroquine (HCQ). Our pipeline enabled us to find diagnoses, drugs and interventions related to HCQ and which could reflect an ADR due to HCQ or the disease itself.

• This data-driven pipeline strategy may be of interest to other experts involved in the pharmacovigilance discipline.

Proton pump inhibitors may contribute to progression or development of chronic obstructive pulmonary disease-A sequence symmetry analysis approach

WHAT IS KNOWN AND OBJECTIVE

Proton pump inhibitors (PPIs), used to treat and prevent gastro-oesophageal conditions, are well-tolerated but have been associated with risk including pneumonia. The extent to which initiation of PPIs can contribute to other respiratory conditions such as chronic obstructive pulmonary disease (COPD) is largely unknown.

METHODS

A sequence symmetry analysis (SSA) approach was applied to the Australian Department of Human Services, Pharmaceutical Benefits Scheme 10% extract. Participants were aged 45 years and older and were dispensed PPIs (ATC Codes A02BC01, A02BC02, A02BC03, A02BC04 and A02BC05) and long-acting bronchodilators (LABDs) for COPD (ATC Codes R03BB04 (PBS Item Code 10509D and 08626B), R03BB05, R03BB06, R03BB07 and R03AC18 (PBS Item Code 05137J and 05134F)) between 2013 and 2019. The analysis included patients initiated on an LABD within 12 months before or after their first prescription of a PPI. The crude sequence ratio (cSR) was calculated as the number of patients prescribed their first LABD after starting a PPI divided by the number of patients prescribed their first LABD before starting a PPI. Calculation of the adjusted sequence ratio (aSR) accounted for prescribing trends over time in initiation of each of the medicines. A signal was identified where the aSR lower 95% confidence interval (CI) was greater than one.

RESULTS AND DISCUSSION

Initiation of omeprazole was associated with a 29% increased risk of initiating a LABD (ASR = 1.29 95% CI 1.22-1.36). Initiation of esomeprazole, rabeprazole, pantoprazole or lansoprazole was associated with 25%, 15%, 8% and 8% increased risk, respectively.

WHAT IS NEW AND CONCLUSION

There is an established association between gastro-oesophageal reflux disease and COPD which has been confirmed by implementation of a sequence symmetry-based approach which demonstrated that PPI initiation is potentially associated with progression or exacerbation of COPD. The impact PPI use has directly on this association requires further investigation.

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

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.