Preciosa Miguel Coloma
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MINING ELECTRONIC HEALTHCARE RECORD DATABASES TO AUGMENT DRUG SAFETY SURVEILLANCE Preciosa Miguel Coloma ISBN: 978-94-6191-258-9 Cover design and layout by Catherine Coloma Layout by Legatron Electronic Publishing, Rotterdam Printing by Ipskampdrukkers, Enschede Th e work in this thesis was conducted at the Department of Medical Informatics at the Erasmus MC, University Medical Center, Rotterdam, the Netherlands. Th e partners of the EU-ADR Consortium and the contributors to the EU-ADR network of databases are gratefully acknowledged. Th e EU-ADR Project was funded by the European Commission under the Seventh Framework Programme. Financial support for the publication of this thesis was provided by the Erasmus University Rotterdam and the Interdisciplinary Processing of Clinical Information (IPCI, formerly Integrated Primary Care Information) Project. Copyright ©2012 by Preciosa M. Coloma All rights reserved. No part of this thesis may be reproduced, distributed, stored in a retrieval system, or transmitted in any form or by any means, without the written permission of the author or, when appropriate, the publishers of the publications. MINING ELECTRONIC HEALTHCARE RECORD DATABASES TO AUGMENT DRUG SAFETY SURVEILLANCE AUTOMATISCH ANALYSEREN VAN DATABANKEN MET ELEKTRONISCHE GEZONDHEIDSZORGDOSSIERS TER VERSTERKING VAN GENEESMIDDELENBEWAKING Proefschrift ter verkrijging van de graad van doctor aan de Erasmus Universiteit Rotterdam op gezag van de rector magnifi cus Prof.dr. H.G. Schmidt en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op woensdag, 16 mei 2012 om 15.30 uur door Preciosa Miguel Coloma geboren te Laoag (Filippijnen) Promotiecommissie Promotor: Prof.dr. M.C.J.M. Sturkenboom Overige leden: Prof.dr. B.H.Ch. Stricker Prof.dr. E.M.E.H Lesaff re Prof.dr. H.G.M. Leufk ens Copromotoren: Dr. G. Trifi rò Dr. M.J. Schuemie For my Dad CONTENTS Chapter 1 General Introduction: About this thesis 9 Chapter 2 The Big Picture 15 A review of post-marketing drug safety surveillance systems: where automatic signal detection using Electronic Healthcare Records (EHR) fits into the big picture 17 Chapter 3 Breaking New Grounds 41 3.1 Data mining on electronic health record databases for signal 43 detection in pharmacovigilance: which events to monitor? 3.2 Harmonization process for the identification of medical events in 59 eight European healthcare databases: experience from the EU-ADR project 3.3 Combining electronic healthcare databases in Europe to allow for 87 large-scale drug safety monitoring: the EU-ADR Project Chapter 4 Facing up to the Challenge 105 4.1 Electronic healthcare databases for active drug safety surveillance: 107 is there enough leverage? 4.2 The power of electronic healthcare databases for active drug safety 127 surveillance in children and adolescents 4.3 A reference standard for evaluation of methods for drug safety 147 signal detection using Electronic Healthcare Record (EHR) databases 4.4 Using electronic healthcare records for drug safety signal detection: 173 a comparative evaluation of statistical methods 4.5 Positive predictive value of different coding-based algorithms for 193 identification of patients with upper gastrointestinal bleeding in four European electronic healthcare record databases 4.6 Accuracy of coding-based algorithms in identifi cation of acute 211 myocardial infarction in multi-country electronic healthcare record (EHR) databases 4.7 Epidemiology of Progressive Multifocal Leukoencephalopathy: 231 population-based study from three European countries Chapter 5 Separating the Big Fish from the Small Fry 243 Triage and evaluation of potential safety signals identifi ed from 245 Electronic Healthcare Record (EHR) databases Chapter 6 Summary Discussion and Perspectives 267 General Discussion 269 Summary 283 Samenvatting 287 Acknowledgments 293 List of publications 297 PhD Portfolio 299 About the Author 303 Colour fi gures 305 Chapter 1 General Introduction: About this thesis It is well known that placebos – and even no treatment – can be associated with adverse events. 10 | Chapter 1 INTRODUCTION It is perhaps a fundamental truth in medicine that there is no intervention – be it a drug, a medical device or a procedure – that is without risks. Even with the most rigorous eff orts in drug approval and regulation, there is not a drug out there that is 100% safe under all conditions. Randomized controlled trials (RCTs) are considered to be the most stringent approach to determining cause-and-eff ect relationship between an intervention and an outcome, but such trials are rarely designed or powered to detect uncommon or unexpected adverse events.1-4 Once drugs are marketed, they are used in a more diverse group of people, oft en for much longer periods, and sometimes with a wider range of therapeutic indications. While monitoring the risks associated with drug use has come a long way since the thalidomide disaster in the 1960s and the institution of spontaneous reporting systems (SRS), it has become evident that adverse eff ects of drugs may be detected - and acted upon – too late, when millions of persons have already been exposed.5-6 Th ere has been a growing clamor for improving the current passive-reactive paradigm of drug safety surveillance. Prominent issues in the last few years have emphasized the importance of a life-cycle approach to drug safety monitoring and the need to explore new methods to improve surveillance of drugs post-marketing.7-8 It has been posited that electronic healthcare record (EHR) databases represent an important resource for proactive surveillance and can augment existing pharmacovigilance systems. Various public-private initiatives worldwide have been launched, and great investments have been made, to explore the secondary use of EHR for this purpose.9-10 In this thesis, we draw on the experience of the EU-ADR network (Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge, http://www.euadr-project.org/), a federation of eight EHR databases in four countries in Europe, to demonstrate the feasibility of combining diverse and diff erently structured data and pave the way for large-scale drug safety monitoring. We describe the opportunities and challenges that come with heterogeneity in database structure, with diff erences in language and coding of both drugs and diseases, and with the diversity in the organization of European healthcare systems. ABOUT THIS THESIS In Chapter 2: Th e Big Picture, we provide an overview of ongoing initiatives exploring data from EHR for automatic signal detection vis-à-vis established SRS. We describe the role SRS has played in regulatory decision-making with respect to safety issues and further evaluate the potential added value of EHR-based signal detection systems to the current practice of drug safety surveillance. In Chapter 3: Breaking New Grounds, we set the stage for large-scale monitoring of drug safety signals using multiple EHR databases. We start off with the identifi cation of events that are considered important to monitor from a pharmacovigilance and public health perspective (3.1). We describe the process of harmonizing event data extraction from diff erent databases, the aim of General Introduction: About this thesis | 11 which is to come up with a common defi nition of events that is both clinically sound and agreeable to all stakeholders (3.2). We then present a proof-of-concept study that describes the framework, distributed data processing, and preliminary results of combining data from eight European EHR databases for drug safety signal detection. Th is is the fi rst study of its kind that explores the Chapter 1 Chapter methodological, cultural, ethical, governance and political issues of sharing healthcare data across international borders (3.3). In Chapter 4: Facing up to the Challenge, we put into perspective the expectations about what these healthcare data-based surveillance systems can do. We demonstrate how much leverage EHR databases can provide for monitoring the safety of medicines. We provide estimates of the number and types of drugs that can be monitored in these systems as a function of actual drug use, minimal detectable relative risk, and incidence rates of outcomes of interest (4.1). We further perform data simulation in an attempt to see if, and how, expansion of database size would make a diff erence in the capabilities of the system. Because the prevalence and nature of many diseases are not the same in children as in adults and because of age-related variation in drug pharmacology and drug utilization,11-13 we performed a separate study to determine the capabilities of the system for pediatric drug safety signal detection (4.2). Although several drugs have been withdrawn post- marketing by regulatory authorities aft er scientifi c evaluation of harms and benefi ts, there is no defi nitive list of confi rmed signals (i.e., list of all known adverse reactions and which drugs can cause them). As there is no true gold standard, prospective evaluation of signal detection methods remains a challenge. We propose a surrogate reference standard of drug-adverse event associations based on existing published scientifi c literature, drug product labels, and expert opinion and developed for the primary purpose of evaluating performance of methods for signal detection using EHR (4.2). We then evaluate the relative performance of 10 diff erent statistical methods for detecting drug-adverse event associations in EHR data (4.3). While the motivation for merging disparate data sources primarily comes from the need to investigate drug safety in larger populations, it is a well-acknowledged limitation that EHR databases are only able to describe exposures and outcomes of interest to the extent that they are documented within the database systems.14-15 We evaluate the positive predictive value of various coding-based algorithms in the identifi cation of upper gastrointestinal bleeding (UGIB) and acute myocardial infarction (AMI) in diff erent EHR databases by case validation and review of hospitalization charts and general practitioner (GP) records (4.4, 4.5).