Exploring the Potential Routine Use of Electronic Healthcare Record Data to Strengthen Early Signal Assessment in UK Medicines Regulation: Proof-of-Concept Study

Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study

INTRODUCTION

Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals.

OBJECTIVE

The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process.

METHODS

Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals.

RESULTS

Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development.

CONCLUSIONS

Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.

Automation in signal management in pharmacovigilance-an insight

Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.

A Novel Approach to Visualize Risk Minimization Effectiveness: Peeping at the 2012 UK Proton Pump Inhibitor Label Change Using a Rapid Cycle Analysis Tool

INTRODUCTION

Evaluation of risk minimization (RM) actions is an emerging area of regulatory science, often without tools to rapidly and systematically assess their effectiveness.

PURPOSE

The aim of this study was to evaluate whether chronographs, typically used for rapid signal detection in observational longitudinal databases, could be used to visualize RM effectiveness. We evaluated the UK Medicines and Healthcare products Regulatory Agency (MHRA) 2012 proton-pump inhibitors (PPIs) class-wide label change that warned of increased risk of bone fracture, advocated to limit duration of use, and recommended to treat those at risk for osteoporosis according to clinical guidelines.

METHODS

The cohort consisted of adults aged 18 years and above prescribed one of the five PPIs available in the UK The Health Improvement Network (THIN) database through September 2015. Four chronographs were compared using drug episodes that started before (PRE) and after (POST) the 20 April 2012 MHRA warning; fracture and osteoporosis were evaluated separately. Chronographs show a measure of observed/expected events, the Information Component (IC) and 95% credibility interval (CI), calculated at monthly time intervals relative to the start date of a prescription, and summed to estimate IC over a 3-year period; IC > 0 indicates observed > expected events. We hypothesized that chronographs may assess RM effectiveness if stratified by PRE/POST an RM intervention such as a label change.

RESULTS

There were 1,588,973 and 664,601 PPI users in the PRE and POST periods, respectively. We observed a 4.6% reduction in the proportion of long-term PPI episodes and a 4.1% reduction in the overall proportion of the THIN population using PPIs. Compared with the PRE chronographs, when both visually comparing and when examining the summed ICs for fracture in the POST period, a significant reduction was observed overall (IC = 0.024 [95% CI 0.015 to 0.33] PRE vs - 0.141 [95% CI - 0.162 to - 0.120] POST), suggesting less observed events than expected, and prior to PPI start, suggestive of strong channeling (IC = - 0.027 [95% CI - 0.037 to - 0.017] PRE vs - 0.291 [95% CI - 0.308 to - 0.274] POST). Results were qualitatively similar for osteoporosis.

CONCLUSIONS

This pilot demonstrated a novel application of a visual, rapid analysis technique to assess RM effectiveness, and supported a hypothesis that prescribers altered some behaviors after the MHRA label change, such as channeling patients at risk of fracture or osteoporosis away from PPI use and potentially reducing fracture outcomes. Limitations include lack of confounding control and outcomes defined only by diagnosis code. Results demonstrate the potential to use large healthcare databases with chronographs to rapidly assess RM effectiveness, similar to signal detection in pharmacovigilance, and may help design more comprehensive RM evaluation studies.