Harmonization process for the identification of medical events in eight European healthcare databases: the experience from the EU-ADR project

Exploring the similarities and differences of burn registers globally: Results from a data dictionary comparison study

INTRODUCTION

Pooling and comparing data from the existing global network of burn registers represents a powerful, yet untapped, opportunity to improve burn prevention and care. There have been no studies investigating whether registers are sufficiently similar to allow data comparisons. It is also not known what differences exist that could bias analyses. Understanding this information is essential prior to any future data sharing. The aim of this project was to compare the variables collected in countrywide and intercountry burn registers to understand their similarities and differences.

METHODS

Register custodians were invited to participate and share their data dictionaries. Inclusion and exclusion criteria were compared to understand each register population. Descriptive statistics were calculated for the number of unique variables. Variables were classified into themes. Definition, method, timing of measurement, and response options were compared for a sample of register concepts.

RESULTS

13 burn registries participated in the study. Inclusion criteria varied between registers. Median number of variables per register was 94 (range 28 - 890), of which 24% (range 4.8 - 100%) were required to be collected. Six themes (patient information, admission details, injury, inpatient, outpatient, other) and 41 subthemes were identified. Register concepts of age and timing of injury show similarities in data collection. Intent, mechanism, inhalational injury, infection, and patient death show greater variation in measurement.

CONCLUSIONS

We found some commonalities between registers and some differences. Commonalities would assist in any future efforts to pool and compare data between registers. Differences between registers could introduce selection and measurement bias, which needs to be addressed in any strategy aiming to facilitate burn register data sharing. We recommend the development of common data elements used in an international minimum data set for burn injuries, including standard definitions and methods of measurement, as the next step in achieving burn register data sharing.

Optimizing Signal Management in a Vaccine Adverse Event Reporting System: A Proof-of-Concept with COVID-19 Vaccines Using Signs, Symptoms, and Natural Language Processing

INTRODUCTION

The Vaccine Adverse Event Reporting System (VAERS) has already been challenged by an extreme increase in the number of individual case safety reports (ICSRs) after the market introduction of coronavirus disease 2019 (COVID-19) vaccines. Evidence from scientific literature suggests that when there is an extreme increase in the number of ICSRs recorded in spontaneous reporting databases (such as the VAERS), an accompanying increase in the number of disproportionality signals (sometimes referred to as 'statistical alerts') generated is expected.

OBJECTIVES

The objective of this study was to develop a natural language processing (NLP)-based approach to optimize signal management by excluding disproportionality signals related to listed adverse events following immunization (AEFIs). COVID-19 vaccines were used as a proof-of-concept.

METHODS

The VAERS was used as a data source, and the Finding Associated Concepts with Text Analysis (FACTA+) was used to extract signs and symptoms of listed AEFIs from MEDLINE for COVID-19 vaccines. Disproportionality analyses were conducted according to guidelines and recommendations provided by the US Centers for Disease Control and Prevention. By using signs and symptoms of listed AEFIs, we computed the proportion of disproportionality signals dismissed for COVID-19 vaccines using this approach. Nine NLP techniques, including Generative Pre-Trained Transformer 3.5 (GPT-3.5), were used to automatically retrieve Medical Dictionary for Regulatory Activities Preferred Terms (MedDRA PTs) from signs and symptoms extracted from FACTA+.

RESULTS

Overall, 17% of disproportionality signals for COVID-19 vaccines were dismissed as they reported signs and symptoms of listed AEFIs. Eight of nine NLP techniques used to automatically retrieve MedDRA PTs from signs and symptoms extracted from FACTA+ showed suboptimal performance. GPT-3.5 achieved an accuracy of 78% in correctly assigning MedDRA PTs.

CONCLUSION

Our approach reduced the need for manual exclusion of disproportionality signals related to listed AEFIs and may lead to better optimization of time and resources in signal management.

Epidemiology of pediatric psoriasis: a population-based study using two Italian data sources

BACKGROUND

Psoriasis can be associated with certain comorbidities. This information is important for family pediatricians (FPs) and general practitioners (GPs) who have a key role in the identification and management of skin diseases. This study aimed to assess the incidence and prevalence rates of pediatrics psoriasis and its association with specific comorbidities.

METHODS

A retrospective cohort study was performed in patients aged less than 18 years registered in two Italian primary care databases (Pedianet and HSD) between 2015 and 2019. Prevalence and incidence of psoriasis were estimated, and a case-control design was adopted to assess specific comorbidities in psoriasis patients.

RESULTS

The annual prevalence rate of psoriasis was 0.2% in Pedianet and between 0.5% and 0.7% in HSD. The incidence rate ranged from 0.47 to 0.58 and from 1.3 to 1.77 per 1000 person-years in Pedianet and HSD, respectively. Allergic rhinitis, asthma, celiac disease, other malabsorption disease and non-infective cutaneous diseases showed a statistically significant association with psoriasis in Pedianet, while no statistically significant difference was found in HSD.

CONCLUSION

Given the FP-GP transition of patients, there is a need for accurate registration of clinical correlates, enabling GPs to implement strategies to minimize the lifetime risk of psoriatic progression.

Exploring the similarities and differences of variables collected by burn registers globally: protocol for a data dictionary review study

INTRODUCTION

Burn registers can provide high-quality clinical data that can be used for surveillance, research, planning service provision and clinical quality assessment. Many countrywide and intercountry burn registers now exist. The variables collected by burn registers are not standardised internationally. Few international burn register data comparisons are completed beyond basic morbidity and mortality statistics. Data comparisons across registers require analysis of homogenous variables. Little work has been done to understand whether burn registers have sufficiently similar variables to enable useful comparisons. The aim of this project is to compare the variables collected in countrywide and intercountry burn registers internationally to understand their similarities and differences.

METHODS AND ANALYSIS

Burn register custodians will be invited to participate in the study and to share their register data dictionaries. Study objectives are to compare patient inclusion and exclusion criteria of each participating burn register; determine which variables are collected by each register, and if variables are required or optional, identify common variable themes; and compare a sample of variables to understand how they are defined and measured. All variable names will be extracted from each register and common themes will be identified. Detailed information will be extracted for a sample of variables to give a deeper insight into similarities and differences between registers.

ETHICS AND DISSEMINATION

No patient data will be used in this project. Permission to use each register's data dictionary will be sought from respective register custodians. Results will be presented at international meetings and published in open access journals. These results will be of interest to register custodians and researchers wishing to explore international data comparisons, and countries wishing to establish their own burn register.

How many and who are patients with heart failure eligible to SGLT2 inhibitors? Responses from the combination of administrative healthcare and primary care databases

BACKGROUND

Recent successful findings (i.e. DAPA-HF trial) in patients with heart failure (HF) with/without diabetes treated with sodium-glucose co-transporter inhibitors (SGLT2-I) have fostered real-world data analyses. Fondazione Ricerca e Salute's (ReSD) administrative and Health Search's (HSD) primary healthcare databases were combined in the ReS-HS DB Consortium, to identify and characterize HF-patients eligible to SGLT2-I, and assess their costs charged to the Italian National Health Service (INHS).

METHODS AND RESULTS

Eligibility to SGLT2-I was HF diagnosis, age ≥ 18 years, reduced (≤40%) ejection fraction (HFrEF) and glomerular filtration rate (GFR) ≥30 ml/min. The HSD, including 13,313 HF-patients (1.5% of the total HSD population) was used to develop and test the algorithms for imputing HFrEF and GFR ≥ 30 ml/min, based on a set of covariates, to the ReSD, including 67,369 (1.5% of the total ReSD population). Subjects eligible to SGLT2-I were 2187 in HSD (61.1% of HFrEF); after the imputation, 15,145 in ReSD (58.8% of HFrEF). Prevalence of eligibility to SGLT2-I was higher in males then in females and increased with age; diabetic patients were 44.3% and 33.4% of HSD and ReSD populations eligible to SGLT2-I, respectively. Estimated from ReSD, the mean annual cost charged to the INHS per patient with HF eligible to SGLT2-I was €7122 (68% due to hospitalizations).

CONCLUSIONS

Approximately 20% of patients with HF was eligible to SGLT2-I. Real-world data can identify, quantify and characterize patients eligible to SGLT2-Is and assess related costs for the health care system, thus providing useful information to Regulatory Decision makers.

Data Quality Check in Cancer Imaging Research: Deploying and Evaluating the DIQCT Tool

Data harmonization is one of the greatest challenges in cancer imaging studies, especially when it comes to multi-source data provision. Properly integrated data deriving from various sources can ensure data fairness on one side and can lead to a trusted dataset that will enhance AI engine development on the other side. Towards this direction, we are presenting a data integration quality check tool that ensures that all data uploaded to the repository are homogenized and share the same principles. The tool's aim is to report any human-induced errors and propose corrective actions. It focuses on checking the data prior to their upload to the repository in five levels: (i) clinical metadata integrity, (ii) template-imaging consistency, (iii) anonymization protocol applied, (iv) imaging analysis requirements, (v) case completeness. The tool produces reports with the corrective actions that must be followed by the user. This way the tool ensures that the data that will become available to the developers of the AI engine are homogenized, properly structured and contain all the necessary information needed for the analysis. The tool was validated in two rounds, internal and external, and at the user experience level. Clinical Relevance- Supporting the harmonized preparation and provision of medical imaging data and related clinical data will ensure data fairness and enhance the AI development.

Heterogeneity of Drug Allergies and Reaction Lists in Two U.S. Healthcare Systems' Electronic Health Records

BACKGROUND

Healthcare institutions have their own "picklist" for clinicians to document adverse drug reactions (ADRs) into the electronic health record (EHR) allergy list. Whether the lack of a nationally standardized picklist impacts clinician data entries is unknown.

OBJECTIVES

To assess the impact of defined reaction picklists on clinical documentation and, therefore, downstream analytics and clinical research using these data at two institutions.

METHODS

Adverse drug reaction data were obtained from the EHRs of patients who visited the emergency department or outpatient clinics at Brigham and Women's Hospital (BWH) and University of Colorado Hospital (UCH) from 2013-2018. Reported drug class ADR prevalences were calculated. We investigated the reactions on each picklist and compared the top 40 reactions at each institution, as well as the top 10 reactions within each drug class.

RESULTS

Of 2,160,116 patients, 640,444 (30%) had 928,973 active drug allergies. The most commonly reported drug class allergens were similar between BWH and UCH. BWH's picklist had 48 reactions, UCH's had 160 reactions; 29 reactions were shared by both picklists. While the top four reactions overall (rash, GI upset/nausea/vomiting, hives, itching) were identical between sites, reactions by drug class exhibited greater documentation diversity. For example, while the summed prevalence of swelling-related reactions to ACE inhibitors was comparable across sites, swelling was represented by two terms ("swelling", "angioedema") at BWH but 11 terms at UCH (e.g., "swelling", "edema", by body locality).

CONCLUSIONS

The availability and granularity of reaction picklists impacts ADR documentation in the EHR by healthcare providers; picklists may partially explain variations in reported ADRs across healthcare systems.

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

BACKGROUND

The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.

OBJECTIVE

Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.

METHODS

PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).

CONCLUSIONS

The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration's Sentinel system

The US Food and Drug Administration (FDA) Sentinel System uses a distributed data network, a common data model, curated real-world data, and distributed analytic tools to generate evidence for FDA decision-making. Sentinel system needs include analytic flexibility, transparency, and reproducibility while protecting patient privacy. Based on over a decade of experience, a critical system limitation is the inability to identify enough medical conditions of interest in observational data to a satisfactory level of accuracy. Improving the system's ability to use computable phenotypes will require an "all of the above" approach that improves use of electronic health data while incorporating the growing array of complementary electronic health record data sources. FDA recently funded a Sentinel System Innovation Center and a Community Building and Outreach Center that will provide a platform for collaboration across disciplines to promote better use of real-world data for decision-making.

Treatment pathway analysis of newly diagnosed dementia patients in four electronic health record databases in Europe

PURPOSE

Real-world studies to describe the use of first, second and third line therapies for the management and symptomatic treatment of dementia are lacking. This retrospective cohort study describes the first-, second- and third-line therapies used for the management and symptomatic treatment of dementia, and in particular Alzheimer's Disease.

METHODS

Medical records of patients with newly diagnosed dementia between 1997 and 2017 were collected using four databases from the UK, Denmark, Italy and the Netherlands.

RESULTS

We identified 191,933 newly diagnosed dementia patients in the four databases between 1997 and 2017 with 39,836 (IPCI (NL): 3281, HSD (IT): 1601, AUH (DK): 4474, THIN (UK): 30,480) fulfilling the inclusion criteria, and of these, 21,131 had received a specific diagnosis of Alzheimer's disease. The most common first line therapy initiated within a year (± 365 days) of diagnosis were Acetylcholinesterase inhibitors, namely rivastigmine in IPCI, donepezil in HSD and the THIN and the N-methyl-D-aspartate blocker memantine in AUH.

CONCLUSION

We provide a real-world insight into the heterogeneous management and treatment pathways of newly diagnosed dementia patients and a subset of Alzheimer's Disease patients from across Europe.

The European medical information framework: A novel ecosystem for sharing healthcare data across Europe

INTRODUCTION

The European medical information framework (EMIF) was an Innovative Medicines Initiative project jointly supported by the European Union and the European Federation of Pharmaceutical Industries and Associations, that generated a common technology and governance framework to identify, assess and (re)use healthcare data, to facilitate real-world data research. The objectives of EMIF included providing a unified platform to support a wide range of studies within two verification programmes-Alzheimer's disease (EMIF-AD), and metabolic consequences of obesity (EMIF-MET).

METHODS

The EMIF platform was built around two main data-types: electronic health record data and research cohort data, and the platform architecture composed of a set of tools designed to enable data discovery and characterisation. This included the EMIF catalogue, which allowed users to find relevant data sources, including the data-types collected. Data harmonisation via a common data model were central to the project especially for population data sources. EMIF also developed an ethical code of practice to ensure data protection, patient confidentiality and compliance with the European Data Protection Directive, and GDPR.

RESULTS

Currently 18 population-based disease agnostic and 60 cohort-based Alzheimer's data partners from across 14 countries are contained within the catalogue, and this will continue to expand. The work conducted in EMIF-AD and EMIF-MET includes standardizing cohorts, summarising baseline characteristics of patients, developing diagnostic algorithms, epidemiological studies, identifying and validating novel biomarkers and selecting potential patient samples for pharmacological intervention.

CONCLUSIONS

EMIF was designed to provide a sustainable model as demonstrated by the sustainability plans for EMIF-AD. Although network-wide studies using EMIF were not conducted during this project to evaluate its sustainability, learning from EMIF will be used in the follow-on IMI-2 project, European Health Data and Evidence Network (EHDEN). Furthermore, EMIF has facilitated collaborations between partners and continues to promote a wider adoption of principles, technology and architecture through some of its continued work.

Factors influencing harmonized health data collection, sharing and linkage in Denmark and Switzerland: A systematic review

Introduction

The digitalization of medicine has led to a considerable growth of heterogeneous health datasets, which could improve healthcare research if integrated into the clinical life cycle. This process requires, amongst other things, the harmonization of these datasets, which is a prerequisite to improve their quality, re-usability and interoperability. However, there is a wide range of factors that either hinder or favor the harmonized collection, sharing and linkage of health data.

Objective

This systematic review aims to identify barriers and facilitators to health data harmonization—including data sharing and linkage—by a comparative analysis of studies from Denmark and Switzerland.

Methods

Publications from PubMed, Web of Science, EMBASE and CINAHL involving cross-institutional or cross-border collection, sharing or linkage of health data from Denmark or Switzerland were searched to identify the reported barriers and facilitators to data harmonization.

Results

Of the 345 projects included, 240 were single-country and 105 were multinational studies. Regarding national projects, a Swiss study reported on average more barriers and facilitators than a Danish study. Barriers and facilitators of a technical nature were most frequently reported.

Conclusion

This systematic review gathered evidence from Denmark and Switzerland on barriers and facilitators concerning data harmonization, sharing and linkage. Barriers and facilitators were strictly interrelated with the national context where projects were carried out. Structural changes, such as legislation implemented at the national level, were mirrored in the projects. This underlines the impact of national strategies in the field of health data. Our findings also suggest that more openness and clarity in the reporting of both barriers and facilitators to data harmonization constitute a key element to promote the successful management of new projects using health data and the implementation of proper policies in this field. Our study findings are thus meaningful beyond these two countries.

A value set for documenting adverse reactions in electronic health records

Objective

To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record.

Materials and Methods

We analyzed 2 471 004 adverse reactions stored in Partners Healthcare's Enterprise-wide Allergy Repository (PEAR) of 2.7 million patients. Using the Medical Text Extraction, Reasoning, and Mapping System, we processed both structured and free-text reaction entries and mapped them to Systematized Nomenclature of Medicine - Clinical Terms. We calculated the frequencies of reaction concepts, including rare, severe, and hypersensitivity reactions. We compared PEAR concepts to a Federal Health Information Modeling and Standards value set and University of Nebraska Medical Center data, and then created an integrated value set.

Results

We identified 787 reaction concepts in PEAR. Frequently reported reactions included: rash (14.0%), hives (8.2%), gastrointestinal irritation (5.5%), itching (3.2%), and anaphylaxis (2.5%). We identified an additional 320 concepts from Federal Health Information Modeling and Standards and the University of Nebraska Medical Center to resolve gaps due to missing and partial matches when comparing these external resources to PEAR. This yielded 1106 concepts in our final integrated value set. The presence of rare, severe, and hypersensitivity reactions was limited in both external datasets. Hypersensitivity reactions represented roughly 20% of the reactions within our data.

Discussion

We developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set includes clinically important severe and hypersensitivity reactions.

Conclusion

This work contributes a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.

Quantifying outcome misclassification in multi-database studies: The case study of pertussis in the ADVANCE project

BACKGROUND

The Accelerated Development of VAccine beNefit-risk Collaboration in Europe (ADVANCE) is a public-private collaboration aiming to develop and test a system for rapid benefit-risk (B/R) monitoring of vaccines using European healthcare databases. Event misclassification can result in biased estimates. Using different algorithms for identifying cases of Bordetella pertussis (BorPer) infection as a test case, we aimed to describe a strategy to quantify event misclassification, when manual chart review is not feasible.

METHODS

Four participating databases retrieved data from primary care (PC) setting: BIFAP: (Spain), THIN and RCGP RSC (UK) and PEDIANET (Italy); SIDIAP (Spain) retrieved data from both PC and hospital settings. BorPer algorithms were defined by healthcare setting, data domain (diagnoses, drugs, or laboratory tests) and concept sets (specific or unspecified pertussis). Algorithm- and database-specific BorPer incidence rates (IRs) were estimated in children aged 0-14 years enrolled in 2012 and 2014 and followed up until the end of each calendar year and compared with IRs of confirmed pertussis from the ECDC surveillance system (TESSy). Novel formulas were used to approximate validity indices, based on a small set of assumptions. They were applied to approximately estimate positive predictive value (PPV) and sensitivity in SIDIAP.

RESULTS

The number of cases and the estimated BorPer IRs per 100,000 person-years in PC, using data representing 3,173,268 person-years, were 0 (IR = 0.0), 21 (IR = 4.3), 21 (IR = 5.1), 79 (IR = 5.7), and 2 (IR = 2.3) in BIFAP, SIDIAP, THIN, RCGP RSC and PEDIANET respectively. The IRs for combined specific/unspecified pertussis were higher than TESSy, suggesting that some false positives had been included. In SIDIAP the estimated IR was 45.0 when discharge diagnoses were included. The sensitivity and PPV of combined PC specific and unspecific diagnoses for BorPer cases in SIDIAP were approximately 85% and 72%, respectively.

CONCLUSION

Retrieving BorPer cases using only specific concepts has low sensitivity in PC databases, while including cases retrieved by unspecified concepts introduces false positives, which were approximately estimated to be 28% in one database. The share of cases that cannot be retrieved from a PC database because they are only seen in hospital was approximately estimated to be 15% in one database. This study demonstrated that quantifying the impact of different event-finding algorithms across databases and benchmarking with disease surveillance data can provide approximate estimates of algorithm validity.

Non-alcoholic fatty liver disease and risk of incident acute myocardial infarction and stroke: findings from matched cohort study of 18 million European adults

OBJECTIVE

To estimate the risk of acute myocardial infarction (AMI) or stroke in adults with non-alcoholic fatty liver disease (NAFLD) or non-alcoholic steatohepatitis (NASH).

DESIGN

Matched cohort study.

SETTING

Population based, electronic primary healthcare databases before 31 December 2015 from four European countries: Italy (n=1 542 672), Netherlands (n=2 225 925), Spain (n=5 488 397), and UK (n=12 695 046).

PARTICIPANTS

120 795 adults with a recorded diagnosis of NAFLD or NASH and no other liver diseases, matched at time of NAFLD diagnosis (index date) by age, sex, practice site, and visit, recorded at six months before or after the date of diagnosis, with up to 100 patients without NAFLD or NASH in the same database.

MAIN OUTCOME MEASURES

Primary outcome was incident fatal or non-fatal AMI and ischaemic or unspecified stroke. Hazard ratios were estimated using Cox models and pooled across databases by random effect meta-analyses.

RESULTS

120 795 patients with recorded NAFLD or NASH diagnoses were identified with mean follow-up 2.1-5.5 years. After adjustment for age and smoking the pooled hazard ratio for AMI was 1.17 (95% confidence interval 1.05 to 1.30; 1035 events in participants with NAFLD or NASH, 67 823 in matched controls). In a group with more complete data on risk factors (86 098 NAFLD and 4 664 988 matched controls), the hazard ratio for AMI after adjustment for systolic blood pressure, type 2 diabetes, total cholesterol level, statin use, and hypertension was 1.01 (0.91 to 1.12; 747 events in participants with NAFLD or NASH, 37 462 in matched controls). After adjustment for age and smoking status the pooled hazard ratio for stroke was 1.18 (1.11 to 1.24; 2187 events in participants with NAFLD or NASH, 134 001 in matched controls). In the group with more complete data on risk factors, the hazard ratio for stroke was 1.04 (0.99 to 1.09; 1666 events in participants with NAFLD, 83 882 in matched controls) after further adjustment for type 2 diabetes, systolic blood pressure, total cholesterol level, statin use, and hypertension.

CONCLUSIONS

The diagnosis of NAFLD in current routine care of 17.7 million patient appears not to be associated with AMI or stroke risk after adjustment for established cardiovascular risk factors. Cardiovascular risk assessment in adults with a diagnosis of NAFLD is important but should be done in the same way as for the general population.

Choosing Among Common Data Models for Real-World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

Many real-world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study-specific transparency.

Using national electronic health care registries for comparing the risk of psychiatric re-hospitalisation in six European countries: Opportunities and limitations

Psychiatric re-hospitalisation rates have been of longstanding interest as health care quality metric for planners and policy makers, but are criticized for not being comparable across hospitals and countries due to measurement unclarities. The objectives of the present study were to explore the interoperability of national electronic routine health care registries of six European countries (Austria, Finland, Italy, Norway, Romania, Slovenia) and, by using variables found to be comparable, to calculate and compare re-hospitalisation rates and the associated risk factors. A "Methods Toolkit" was developed for exploring the interoperability of registry data and protocol led pilot studies were carried out. Problems encountered in this process are described. Using restricted but comparable data sets, up to twofold differences in psychiatric re-hospitalisation rates were found between countries for both a 30- and 365-day follow-up period. Cumulative incidence curves revealed noteworthy additional differences. Health system characteristics are discussed as potential causes for the differences. Multi-level logistic regression analyses showed that younger age and a diagnosis of schizophrenia/mania/bipolar disorder consistently increased the probability of psychiatric re-hospitalisation across countries. It is concluded that the advantage of having large unselected study populations of national electronic health care registries needs to be balanced against the considerable efforts to examine the interoperability of databases in cross-country comparisons.

Auto-Generated Physiological Chain Data for an Ontological Framework for Pharmacology and Mechanism of Action to Determine Suspected Drugs in Cases of Dysuria

INTRODUCTION

Patients often take several different medications for multiple conditions concurrently. Therefore, when adverse drug events (ADEs) occur, it is necessary to consider the mechanisms responsible. Few approaches consider the mechanisms of ADEs, such as changes in physiological states. We proposed that the ontological framework for pharmacology and mechanism of action (pharmacodynamics) we developed could be used for this approach. However, the existing knowledge base contains little data on physiological chains (PCs).

OBJECTIVE

We aimed to investigate a method for automatically generating missing PC from the viewpoint of anatomical structures. This study was conducted to determine dysuria-related adverse events more likely to occur during multidrug administration.

METHODS

We adopted a systematic approach to determine drugs suspected to cause adverse events and incorporated existing data and data generated in our newly developed method into our ontological framework. The performance of automated data generation was evaluated using this newly developed system. Suspected drugs determined by the system were compared with those derived from adverse events databases.

RESULTS

Of the 242 drugs involving suspected drug-induced urinary retention or dysuria, 26 suspected drugs were determined. Of these, five were drugs with side effects not listed in drug package inserts. The system derived potential mechanisms of action, PCs, and suspected drugs.

CONCLUSION

Our method is novel in that it generates PC data from anatomical structural properties and could serve as a knowledge base for determining suspected drugs by potential mechanisms of action.

From Big Data to Smart Data for Pharmacovigilance: The Role of Healthcare Databases and Other Emerging Sources

In the last decade 'big data' has become a buzzword used in several industrial sectors, including but not limited to telephony, finance and healthcare. Despite its popularity, it is not always clear what big data refers to exactly. Big data has become a very popular topic in healthcare, where the term primarily refers to the vast and growing volumes of computerized medical information available in the form of electronic health records, administrative or health claims data, disease and drug monitoring registries and so on. This kind of data is generally collected routinely during administrative processes and clinical practice by different healthcare professionals: from doctors recording their patients' medical history, drug prescriptions or medical claims to pharmacists registering dispensed prescriptions. For a long time, this data accumulated without its value being fully recognized and leveraged. Today big data has an important place in healthcare, including in pharmacovigilance. The expanding role of big data in pharmacovigilance includes signal detection, substantiation and validation of drug or vaccine safety signals, and increasingly new sources of information such as social media are also being considered. The aim of the present paper is to discuss the uses of big data for drug safety post-marketing assessment.

Risk of hospital admission for liver injury in users of NSAIDs and nonoverdose paracetamol: Preliminary results from the EPIHAM study

PURPOSE

The SALT study found similar per-user risks of acute liver failure (ALF) leading to transplantation (ALFT) between NSAIDs and a threefold higher risk in nonoverdose paracetamol (NOP) users. The objective of EPIHAM was to identify the risks of hospital admission for acute liver injury (ALI) associated with NSAIDs and NOP.

METHODS

Case-population study in the 1/97 sample of the French population claims database. Acute liver injury was identified from hospital discharge summaries, from 2009 to 2013. Exposure for cases was dispensing of NSAID or NOP resulting in exposure within 30 days before admission. Population exposure was the number of patients using the drugs over the study timeframe and total number of DDD dispensed.

RESULTS

Of 63 cases of ALI, 13 had been exposed to NSAIDs and 24 to NOP. Events per million DDD (95% CI) ranged from 0.46 (0.09-1.34) (ketoprofen) to 1.43 (0.04-7.97) (diclofenac combinations), 0.43 (0.23-0.73) all NSAIDs combined, 0.58 (0.37-0.86) for NOP. There was no association with average duration of treatment. Per patient risk ranged from 19.5 (5.31-49.9) (ibuprofen) per million users to 37.2 (19.8-63.6) all NSAIDs combined, 58.0 (37.2-86.3) for NOP. There was a linear relationship between average treatment duration and per-user risk (R²  = 0.51, P < .05 for NSAIDs, R²  = 0.97, P < .01 for NOP).

CONCLUSIONS

Risk of hospital admission for ALI with NSAIDs and NOP was similar and indicative of a dose and duration-related effect (pharmacological) effect. Acute liver injury rates were not predictive of ALFT risk.

Real-world data reveal a diagnostic gap in non-alcoholic fatty liver disease

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of liver disease worldwide. It affects an estimated 20% of the general population, based on cohort studies of varying size and heterogeneous selection. However, the prevalence and incidence of recorded NAFLD diagnoses in unselected real-world health-care records is unknown. We harmonised health records from four major European territories and assessed age- and sex-specific point prevalence and incidence of NAFLD over the past decade.

METHODS

Data were extracted from The Health Improvement Network (UK), Health Search Database (Italy), Information System for Research in Primary Care (Spain) and Integrated Primary Care Information (Netherlands). Each database uses a different coding system. Prevalence and incidence estimates were pooled across databases by random-effects meta-analysis after a log-transformation.

RESULTS

Data were available for 17,669,973 adults, of which 176,114 had a recorded diagnosis of NAFLD. Pooled prevalence trebled from 0.60% in 2007 (95% confidence interval: 0.41-0.79) to 1.85% (0.91-2.79) in 2014. Incidence doubled from 1.32 (0.83-1.82) to 2.35 (1.29-3.40) per 1000 person-years. The FIB-4 non-invasive estimate of liver fibrosis could be calculated in 40.6% of patients, of whom 29.6-35.7% had indeterminate or high-risk scores.

CONCLUSIONS

In the largest primary-care record study of its kind to date, rates of recorded NAFLD are much lower than expected suggesting under-diagnosis and under-recording. Despite this, we have identified rising incidence and prevalence of the diagnosis. Improved recognition of NAFLD may identify people who will benefit from risk factor modification or emerging therapies to prevent progression to cardiometabolic and hepatic complications.

Can Electronic Health Records Databases Complement Spontaneous Reporting System Databases? A Historical-Reconstruction of the Association of Rofecoxib and Acute Myocardial Infarction

Background: Several initiatives have assessed if mining electronic health records (EHRs) may accelerate the process of drug safety signal detection. In Europe, Exploring and Understanding Adverse Drug Reactions (EU-ADR) Project Focused on utilizing clinical data from EHRs of over 30 million patients from several European countries. Rofecoxib is a prescription COX-2 selective Non-Steroidal Anti-Inflammatory Drugs (NSAID) approved in 1999. In September 2004, the manufacturer withdrew rofecoxib from the market because of safety concerns. In this study, we investigated if the signal concerning rofecoxib and acute myocardial infarction (AMI) could have been identified in EHR database (EU-ADR project) earlier than spontaneous reporting system (SRS), and in advance of rofecoxib withdrawal.

Methods: Data from the EU-ADR project and WHO-VigiBase (for SRS) were used for the analysis. Signals were identified when respective statistics exceeded defined thresholds. The SRS analyses was conducted two ways- based on the date the AMI events with rofecoxib as a suspect medication were entered into the database and also the date that the AMI event occurred with exposure to rofecoxib.

Results: Within the databases participating in EU-ADR it was possible to identify a strong signal concerning rofecoxib and AMI since Q3 2000 [RR LGPS = 4.5 (95% CI: 2.84–6.72)] and peaked to 4.8 in Q4 2000. In WHO-VigiBase, for AMI term grouping, the EB05 threshold of 2 was crossed in the Q4 2004 (EB05 = 2.94). Since then, the EB05 value increased consistently and peaked in Q3 2006 (EB05 = 48.3) and then again in Q2 2008 (EB05 = 48.5). About 93% (2260 out of 2422) of AMIs reported in WHO-VigiBase database actually occurred prior to the product withdrawal, however, they were reported after the risk minimization/risk communication efforts.

Conclusion: In this study, EU-EHR databases were able to detect the AMI signal 4 years prior to the SRS database. We believe that for events that are consistently documented in EHR databases, such as serious events or events requiring in-patient medical intervention or hospitalization, the signal detection exercise in EHR would be beneficial for newly introduced medicinal products on the market, in addition to the SRS data.

E-Science technologies in a workflow for personalized medicine using cancer screening as a case study

Objective

We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings.

Materials and Methods

We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences.

Results

The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform.

Discussion and Conclusion

E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.

A novel approach for medical research on lymphomas: A study validation of claims-based algorithms to identify incident cases

The use of claims database to study lymphomas in real-life conditions is a crucial issue in the future. In this way, it is essential to develop validated algorithms for the identification of lymphomas in these databases. The aim of this study was to assess the validity of diagnosis codes in the French health insurance database to identify incident cases of lymphomas according to results of a regional cancer registry, as the gold standard.Between 2010 and 2013, incident lymphomas were identified in hospital data through 2 algorithms of selection. The results of the identification process and characteristics of incident lymphomas cases were compared with data from the Tarn Cancer Registry. Each algorithm's performance was assessed by estimating sensitivity, predictive positive value, specificity (SPE), and negative predictive value.During the period, the registry recorded 476 incident cases of lymphomas, of which 52 were Hodgkin lymphomas and 424 non-Hodgkin lymphomas. For corresponding area and period, algorithm 1 provides a number of incident cases close to the Registry, whereas algorithm 2 overestimated the number of incident cases by approximately 30%. Both algorithms were highly specific (SPE = 99.9%) but moderately sensitive. The comparative analysis illustrates that similar distribution and characteristics are observed in both sources.Given these findings, the use of claims database can be consider as a pertinent and powerful tool to conduct medico-economic or pharmacoepidemiological studies in lymphomas.

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe

Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.

Dementia prevalence and incidence in a federation of European Electronic Health Record databases: The European Medical Informatics Framework resource

INTRODUCTION

The European Medical Information Framework consortium has assembled electronic health record (EHR) databases for dementia research. We calculated dementia prevalence and incidence in 25 million persons from 2004 to 2012.

METHODS

Six EHR databases (three primary care and three secondary care) from five countries were interrogated. Dementia was ascertained by consensus harmonization of clinical/diagnostic codes. Annual period prevalences and incidences by age and gender were calculated and meta-analyzed.

RESULTS

The six databases contained 138,625 dementia cases. Age-specific prevalences were around 30% of published estimates from community samples and incidences were around 50%. Pooled prevalences had increased from 2004 to 2012 in all age groups but pooled incidences only after age 75 years. Associations with age and gender were stable over time.

DISCUSSION

The European Medical Information Framework initiative supports EHR data on unprecedented number of people with dementia. Age-specific prevalences and incidences mirror estimates from community samples in pattern at levels that are lower but increasing over time.

CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project

BACKGROUND

Assessment of drug and vaccine effects by combining information from different healthcare databases in the European Union requires extensive efforts in the harmonization of codes as different vocabularies are being used across countries. In this paper, we present a web application called CodeMapper, which assists in the mapping of case definitions to codes from different vocabularies, while keeping a transparent record of the complete mapping process.

METHODS

CodeMapper builds upon coding vocabularies contained in the Metathesaurus of the Unified Medical Language System. The mapping approach consists of three phases. First, medical concepts are automatically identified in a free-text case definition. Second, the user revises the set of medical concepts by adding or removing concepts, or expanding them to related concepts that are more general or more specific. Finally, the selected concepts are projected to codes from the targeted coding vocabularies. We evaluated the application by comparing codes that were automatically generated from case definitions by applying CodeMapper's concept identification and successive concept expansion, with reference codes that were manually created in a previous epidemiological study.

RESULTS

Automated concept identification alone had a sensitivity of 0.246 and positive predictive value (PPV) of 0.420 for reproducing the reference codes. Three successive steps of concept expansion increased sensitivity to 0.953 and PPV to 0.616.

CONCLUSIONS

Automatic concept identification in the case definition alone was insufficient to reproduce the reference codes, but CodeMapper's operations for concept expansion provide an effective, efficient, and transparent way for reproducing the reference codes.

Clinical epidemiology in the era of big data: new opportunities, familiar challenges

Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring (“null”) findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.

Use of azithromycin and risk of ventricular arrhythmia

BACKGROUND

There are conflicting findings from observational studies of the arrhythrogenic potential of azithromycin. Our aim was to quantify the association between azithromycin use and the risk of ventricular arrhythmia.

METHODS

We conducted a nested case-control study within a cohort of new antibiotic users identified from a network of 7 population-based health care databases in Denmark, Germany, Italy, the Netherlands and the United Kingdom for the period 1997-2010. Up to 100 controls per case were selected and matched by age, sex and database. Recency of antibiotic use and type of drug (azithromycin was the exposure of interest) at the index date (occurrence of ventricular arrhythmia) were identified. We estimated the odds of ventricular arrhythmia associated with current azithromycin use relative to current amoxicillin use or nonuse of antibiotics (≥ 365 d without antibiotic exposure) using conditional logistic regression, adjusting for confounders.

RESULTS

We identified 14 040 688 new antibiotic users who met the inclusion criteria. Ventricular arrhythmia developed in 12 874, of whom 30 were current azithromycin users. The mean age of the cases and controls was 63 years, and two-thirds were male. In the pooled data analyses across databases, azithromycin use was associated with an increased risk of ventricular arrhythmia relative to nonuse of antibiotics (adjusted odds ratio [OR] 1.97, 95% confidence interval [CI] 1.35-2.86). This increased risk disappeared when current amoxicillin use was the comparator (adjusted OR 0.90, 95% CI 0.48-1.71). Database-specific estimates and meta-analysis confirmed results from the pooled data analysis.

INTERPRETATION

Current azithromycin use was associated with an increased risk of ventricular arrhythmia when compared with nonuse of antibiotics, but not when compared with current amoxicillin use. The decreased risk with an active comparator suggests significant confounding by indication.

Combining clinical and genomics queries using i2b2 - Three methods

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical "Big Data" challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.

A method for cohort selection of cardiovascular disease records from an electronic health record system

INTRODUCTION

An electronic healthcare record (EHR) system, when used by healthcare providers, improves the quality of care for patients and helps to lower costs. Information collected from manual or electronic health records can also be used for purposes not directly related to patient care delivery, in which case it is termed secondary use. EHR systems facilitate the collection of this secondary use data, which can be used for research purposes like observational studies, taking advantage of improvement in the structuring and retrieval of patient information. However, some of the following problems are common when conducting a research using this kind of data: (i) Over time, systems and data storage methods become obsolete; (ii) Data concerns arise since the data is being used in a context removed from its original intention; (iii) There are privacy concerns when sharing data about individual subjects; (iv) The partial availability of standard medical vocabularies and natural language processing tools for non-English language limits information extraction from structured and unstructured data in the EHR systems. A systematic approach is therefore needed to overcome these, where local data processing is performed prior to data sharing.

METHOD

The proposed study describes a local processing method to extract cohorts of patients for observational studies in four steps: (1) data reorganization from an existing local logical schema into a common external schema over which information can be extracted; (2) cleaning of data, generation of the database profile and retrieval of indicators; (3) computation of derived variables from original variables; (4) application of study design parameters to transform longitudinal data into anonymized data sets ready for statistical analysis and sharing. Mapping from the local logical schema into a common external schema must be performed differently for each EHR and is not subject of this work, but step 2, 3 and 4 are common to all EHRs. The external schema accepts parameters that facilitate the extraction of different cohorts for different studies without having to change the extraction algorithms, and ensures that, given an immutable data set, can be done by the idempotent process. Statistical analysis is part of the process to generate the results necessary for inclusion in reports. The generation of indicators to describe the database allows description of its characteristics, highlighting study results. The set extraction/statistical processing is available in a version controlled repository and can be used at any time to reproduce results, allowing the verification of alterations and error corrections. This methodology promotes the development of reproducible studies and allows potential research problems to be tracked upon extraction algorithms and statistical methods RESULTS: This method was applied to an admissions database, SI³, from the InCor-HCFMUSP, a tertiary referral hospital for cardiovascular disease in the city of São Paulo, as a source of secondary data with 1116848 patients records from 1999 up to 2013. The cleaning process resulted in 313894 patients records and 27698 patients in the cohort selection, with the following criteria: study period: 2003-2013, gender: Male, Female, age:≥18years old, at least 2 outpatient encounters, diagnosis of cardiovascular disease (ICD-10 codes: I20-I25, I64-I70 and G45). An R script provided descriptive statistics of the extracted cohort.

CONCLUSION

This method guarantees a reproducible cohort extraction for use of secondary data in observational studies with enough parameterization to support different study designs and can be used on diverse data sources. Moreover it allows observational electronic health record cohort research to be performed in a non-English language with limited international recognized medical vocabulary.

Identifying multiple myeloma patients using data from the French health insurance databases: Validation using a cancer registry

This study aimed to assess the performance of several algorithms based on hospital diagnoses and the long-term diseases scheme to identify multiple myeloma patients.Potential multiple myeloma patients in 2010 to 2013 were identified using the presence of hospital records with at least 1 main diagnosis code for multiple myeloma (ICD-10 "C90"). Alternative algorithms also considered related and associated diagnoses, combination with long-term conditions, or at least 2 diagnoses. Incident patients were those with no previous "C90" codes in the past 24 or 12 months. The sensitivity, specificity, and positive and negative predictive values (PPVs and NPVs) were computed, using a French cancer registry for the corresponding area and period as the criterion standard.Long-term conditions data extracted concerned 11,559 patients (21,846 for hospital data). The registry contained 125 cases of multiple myeloma. Sensitivity was 70% when using only main hospital diagnoses (specificity 100%, PPV 79%), 76% when also considering related diagnoses (specificity 100%, PPV 74%), and 90% with associated diagnoses included (100% specificity, 64% PPV).In relation with their good performance, selected algorithms can be used to study the benefit and risk of drugs in treated multiple myeloma patients.

Clinical Research Informatics for Big Data and Precision Medicine

OBJECTIVES

To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI.

METHODS

We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research.

RESULTS

The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges.

CONCLUSION

The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.

Useful Interplay Between Spontaneous ADR Reports and Electronic Healthcare Records in Signal Detection

Background and Objective

Spontaneous reporting systems (SRSs) remain the cornerstone of post-marketing drug safety surveillance despite their well-known limitations. Judicious use of other available data sources is essential to enable better detection, strengthening and validation of signals. In this study, we investigated the potential of electronic healthcare records (EHRs) to be used alongside an SRS as an independent system, with the aim of improving signal detection.

Methods

A signal detection strategy, focused on a limited set of adverse events deemed important in pharmacovigilance, was performed retrospectively in two data sources—(1) the Exploring and Understanding Adverse Drug Reactions (EU-ADR) database network and (2) the EudraVigilance database—using data between 2000 and 2010. Five events were considered for analysis: (1) acute myocardial infarction (AMI); (2) bullous eruption; (3) hip fracture; (4) acute pancreatitis; and (5) upper gastrointestinal bleeding (UGIB). Potential signals identified in each system were verified using the current published literature. The complementarity of the two systems to detect signals was expressed as the percentage of the unilaterally identified signals out of the total number of confirmed signals. As a proxy for the associated costs, the number of signals that needed to be reviewed to detect one true signal (number needed to detect [NND]) was calculated. The relationship between the background frequency of the events and the capability of each system to detect signals was also investigated.

Results

The contribution of each system to signal detection appeared to be correlated with the background incidence of the events, being directly proportional to the incidence in EU-ADR and inversely proportional in EudraVigilance. EudraVigilance was particularly valuable in identifying bullous eruption and acute pancreatitis (71 and 42 % of signals were correctly identified from the total pool of known associations, respectively), while EU-ADR was most useful in identifying hip fractures (60 %). Both systems contributed reasonably well to identification of signals related to UGIB (45 % in EudraVigilance, 40 % in EU-ADR) but only fairly for signals related to AMI (25 % in EU-ADR, 20 % in EudraVigilance). The costs associated with detection of signals were variable across events; however, it was often more costly to detect safety signals in EU-ADR than in EudraVigilance (median NNDs: 7 versus 5).

Conclusion

An EHR-based system may have additional value for signal detection, alongside already established systems, especially in the presence of adverse events with a high background incidence. While the SRS appeared to be more cost effective overall, for some events the costs associated with signal detection in the EHR might be justifiable.

Electronic supplementary material

The online version of this article (doi:10.1007/s40264-015-0341-5) contains supplementary material, which is available to authorized users.

Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project

Due to the heterogeneity of existing European sources of observational healthcare data, data source-tailored choices are needed to execute multi-data source, multi-national epidemiological studies. This makes transparent documentation paramount. In this proof-of-concept study, a novel standard data derivation procedure was tested in a set of heterogeneous data sources. Identification of subjects with type 2 diabetes (T2DM) was the test case. We included three primary care data sources (PCDs), three record linkage of administrative and/or registry data sources (RLDs), one hospital and one biobank. Overall, data from 12 million subjects from six European countries were extracted. Based on a shared event definition, sixteeen standard algorithms (components) useful to identify T2DM cases were generated through a top-down/bottom-up iterative approach. Each component was based on one single data domain among diagnoses, drugs, diagnostic test utilization and laboratory results. Diagnoses-based components were subclassified considering the healthcare setting (primary, secondary, inpatient care). The Unified Medical Language System was used for semantic harmonization within data domains. Individual components were extracted and proportion of population identified was compared across data sources. Drug-based components performed similarly in RLDs and PCDs, unlike diagnoses-based components. Using components as building blocks, logical combinations with AND, OR, AND NOT were tested and local experts recommended their preferred data source-tailored combination. The population identified per data sources by resulting algorithms varied from 3.5% to 15.7%, however, age-specific results were fairly comparable. The impact of individual components was assessed: diagnoses-based components identified the majority of cases in PCDs (93–100%), while drug-based components were the main contributors in RLDs (81–100%). The proposed data derivation procedure allowed the generation of data source-tailored case-finding algorithms in a standardized fashion, facilitated transparent documentation of the process and benchmarking of data sources, and provided bases for interpretation of possible inter-data source inconsistency of findings in future studies.

Individual-based versus aggregate meta-analysis in multi-database studies of pregnancy outcomes: the Nordic example of selective serotonin reuptake inhibitors and venlafaxine in pregnancy

PURPOSE

Compare analyses of a pooled data set on the individual level with aggregate meta-analysis in a multi-database study.

METHODS

We reanalysed data on 2.3 million births in a Nordic register based cohort study. We compared estimated odds ratios (OR) for the effect of selective serotonin reuptake inhibitors (SSRI) and venlafaxine use in pregnancy on any cardiovascular birth defect and the rare outcome right ventricular outflow tract obstructions (RVOTO). Common covariates included maternal age, calendar year, birth order, maternal diabetes, and co-medication. Additional covariates were added in analyses with country-optimized adjustment.

RESULTS

Country adjusted OR (95%CI) for any cardiovascular birth defect in the individual-based pooled analysis was 1.27 (1.17-1.39), 1.17 (1.07-1.27) adjusted for common covariates and 1.15 (1.05-1.26) adjusted for all covariates. In fixed effects meta-analyses pooled OR was 1.29 (1.19-1.41) based on crude country specific ORs, 1.19 (1.09-1.29) adjusted for common covariates, and 1.16 (1.06-1.27) for country-optimized adjustment. In a random effects model the adjusted OR was 1.07 (0.87-1.32). For RVOTO, OR was 1.48 (1.15-1.89) adjusted for all covariates in the pooled data set, and 1.53 (1.19-1.96) after country-optimized adjustment. Country-specific adjusted analyses at the substance level were not possible for RVOTO.

CONCLUSION

Results of fixed effects meta-analysis and individual-based analyses of a pooled dataset were similar in this study on the association of SSRI/venlafaxine and cardiovascular birth defects. Country-optimized adjustment attenuated the estimates more than adjustment for common covariates only. When data are sparse pooled data on the individual level are needed for adjusted analyses. Copyright © 2016 John Wiley & Sons, Ltd.

Data Extraction and Management in Networks of Observational Health Care Databases for Scientific Research: A Comparison of EU-ADR, OMOP, Mini-Sentinel and MATRICE Strategies

Introduction:

We see increased use of existing observational data in order to achieve fast and transparent production of empirical evidence in health care research. Multiple databases are often used to increase power, to assess rare exposures or outcomes, or to study diverse populations. For privacy and sociological reasons, original data on individual subjects can’t be shared, requiring a distributed network approach where data processing is performed prior to data sharing.

Case Descriptions and Variation Among Sites:

We created a conceptual framework distinguishing three steps in local data processing: (1) data reorganization into a data structure common across the network; (2) derivation of study variables not present in original data; and (3) application of study design to transform longitudinal data into aggregated data sets for statistical analysis. We applied this framework to four case studies to identify similarities and differences in the United States and Europe: Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR), Observational Medical Outcomes Partnership (OMOP), the Food and Drug Administration’s (FDA’s) Mini-Sentinel, and the Italian network—the Integration of Content Management Information on the Territory of Patients with Complex Diseases or with Chronic Conditions (MATRICE).

Findings:

National networks (OMOP, Mini-Sentinel, MATRICE) all adopted shared procedures for local data reorganization. The multinational EU-ADR network needed locally defined procedures to reorganize its heterogeneous data into a common structure. Derivation of new data elements was centrally defined in all networks but the procedure was not shared in EU-ADR. Application of study design was a common and shared procedure in all the case studies. Computer procedures were embodied in different programming languages, including SAS, R, SQL, Java, and C++.

Conclusion:

Using our conceptual framework we found several areas that would benefit from research to identify optimal standards for production of empirical knowledge from existing databases.an opportunity to advance evidence-based care management. In addition, formalized CM outcomes assessment methodologies will enable us to compare CM effectiveness across health delivery settings.

Using real-world healthcare data for pharmacovigilance signal detection - the experience of the EU-ADR project

A prospective pharmacovigilance signal detection study, comparing the real-world healthcare data (EU-ADR) and two spontaneous reporting system (SRS) databases, US FDA's Adverse Event Reporting System and WHO's Vigibase is reported. The study compared drug safety signals found in the EU-ADR and SRS databases. The potential for signal detection in the EU-ADR system was found to be dependent on frequency of the event and utilization of drugs in the general population. The EU-ADR system may have a greater potential for detecting signals for events occurring at higher frequency in general population and those that are commonly not considered as potentially a drug-induced event. Factors influencing various differences between the datasets are discussed along with potential limitations and applications to pharmacovigilance practice.

[Benefits of large healthcare databases for drug risk research]

Large electronic healthcare databases have become an important worldwide data resource for drug safety research after approval. Signal generation methods and drug safety studies based on these data facilitate the prospective monitoring of drug safety after approval, as has been recently required by EU law and the German Medicines Act. Despite its large size, a single healthcare database may include insufficient patients for the study of a very small number of drug-exposed patients or the investigation of very rare drug risks. For that reason, in the United States, efforts have been made to work on models that provide the linkage of data from different electronic healthcare databases for monitoring the safety of medicines after authorization in (i) the Sentinel Initiative and (ii) the Observational Medical Outcomes Partnership (OMOP). In July 2014, the pilot project Mini-Sentinel included a total of 178 million people from 18 different US databases. The merging of the data is based on a distributed data network with a common data model. In the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCEPP) there has been no comparable merging of data from different databases; however, first experiences have been gained in various EU drug safety projects. In Germany, the data of the statutory health insurance providers constitute the most important resource for establishing a large healthcare database. Their use for this purpose has so far been severely restricted by the Code of Social Law (Section 75, Book 10). Therefore, a reform of this section is absolutely necessary.

Increased risk of microscopic colitis with use of proton pump inhibitors and non-steroidal anti-inflammatory drugs

OBJECTIVES

Microscopic colitis (MC) is characterized by chronic watery diarrhea. Recently, several drugs were reported to increase the risk of MC. However, studies lacked a clear exposure definition, did not address duration relationships, and did not take important biases into account. We estimated the risk of MC during drug use.

METHODS

This is a population-based nested case-control study using a Dutch primary care database (1999-2013). Incident MC cases (aged ≥18 years) were matched to community-based and colonoscopy-negative controls on age, sex, and primary care practice. Drug use was assessed within 1 and 2 years before the index date. Adjusted odds ratios (OR) were calculated by conditional logistic regression.

RESULTS

From the source population of 1,458,410 subjects, 218 cases were matched to 15,045 community controls and 475 colonoscopy-negative controls. Current use (≤3 months) of proton pump inhibitors (PPIs), nonsteroidal anti-inflammatory drugs (NSAIDs), selective serotonin reuptake inhibitors, low-dose aspirin, angiotensin-converting enzyme (ACE) inhibitors and beta-blockers significantly increased the risk of MC compared with never use in community controls. Adjusted ORs ranged from 2.5 (95% confidence interval (CI): 1.5-4.2) for ACE inhibitors to 7.3 (95% CI: 4.5-12.1) for PPIs in the year prior to the index date. After accounting for diagnostic delay, only use of NSAIDs, PPIs, low-dose aspirin, and ACE inhibitors increased the risk of MC. Compared with colonoscopy controls, only use of PPIs (OR-adjusted 10.6; 1.8-64.2) and NSAIDs (OR-adjusted 5.6; 1.2-27.0) increased the risk of MC.

CONCLUSIONS

NSAIDs and PPIs are associated with an increased risk of MC. The association of MC with use of the other drugs is probably explained by worsening of diarrhea/symptoms rather than increasing the risk of MC itself.

A comparison of active adverse event surveillance systems worldwide

Post-marketing drug surveillance for adverse drug events (ADEs) has typically relied on spontaneous reporting. Recently, regulatory agencies have turned their attention to more preemptive approaches that use existing data for surveillance. We conducted an environmental scan to identify active surveillance systems worldwide that use existing data for the detection of ADEs. We extracted data about the systems' structures, data, and functions. We synthesized the information across systems to identify common features of these systems. We identified nine active surveillance systems. Two systems are US based-the FDA Sentinel Initiative (including both the Mini-Sentinel Initiative and the Federal Partner Collaboration) and the Vaccine Safety Datalink (VSD); two are Canadian-the Canadian Network for Observational Drug Effect Studies (CNODES) and the Vaccine and Immunization Surveillance in Ontario (VISION); and two are European-the Exploring and Understanding Adverse Drug Reactions by Integrative Mining of Clinical Records and Biomedical Knowledge (EU-ADR) Alliance and the Vaccine Adverse Event Surveillance and Communication (VAESCO). Additionally, there is the Asian Pharmacoepidemiology Network (AsPEN) and the Shanghai Drug Monitoring and Evaluative System (SDMES). We identified two systems in the UK-the Vigilance and Risk Management of Medicines (VRMM) Division and the Drug Safety Research Unit (DSRU), an independent academic unit. These surveillance systems mostly use administrative claims or electronic medical records; most conduct pharmacovigilance on behalf of a regulatory agency. Either a common data model or a centralized model is used to access existing data. The systems have been built using national data alone or via partnership with other countries. However, active surveillance systems using existing data remain rare. North America and Europe have the most population coverage; with Asian countries making good advances.

Automated Determination of Publications Related to Adverse Drug Reactions in PubMed

Timely dissemination of up-to-date information concerning adverse drug reactions (ADRs) at the point of care can significantly improve medication safety and prevent ADRs. Automated methods for finding relevant articles in MEDLINE which discuss ADRs for specific medications can facilitate decision making at the point of care. Previous work has focused on other types of clinical queries and on retrieval for specific ADRs or drug-ADR pairs, but little work has been published on finding ADR articles for a specific medication. We have developed a method to generate a PubMED query based on MESH, supplementary concepts, and textual terms for a particular medication. Evaluation was performed on a limited sample, resulting in a sensitivity of 90% and precision of 93%. Results demonstrated that this method is highly effective. Future work will integrate this method within an interface aimed at facilitating access to ADR information for specified drugs at the point of care.

Evaluating performance of electronic healthcare records and spontaneous reporting data in drug safety signal detection

BACKGROUND

Electronic reporting and processing of suspected adverse drug reactions (ADRs) is increasing and has facilitated automated screening procedures. It is crucial for healthcare professionals to understand the nature and proper use of data available in pharmacovigilance practice.

OBJECTIVES

To (a) compare performance of EU-ADR [electronic healthcare record (EHR) exemplar] and FAERS [spontaneous reporting system (SRS) exemplar] databases in detecting signals using "positive" and "negative" drug-event reference sets; and (b) evaluate the impact of timing bias on sensitivity thresholds by comparing all data to data restricted to the time before a warning/regulatory action.

METHODS

Ten events with known positive and negative reference sets were selected. Signals were identified when respective statistics exceeded defined thresholds. Main outcome measure Performance metrics, including sensitivity, specificity, positive predictive value and accuracy were calculated. In addition, the effect of regulatory action on the performance of signal detection in each data source was evaluated.

RESULTS

The sensitivity for detecting signals in EHR data varied depending on the nature of the adverse events and increased substantially if the analyses were restricted to the period preceding the first regulatory action. Across all events, using data from all years, a sensitivity of 45-73 % was observed for EU-ADR and 77 % for FAERS. The specificity was high and similar for EU-ADR (82-96 %) and FAERS (98 %). EU-ADR data showed range of PPV (78-91 %) and accuracy (78-72 %) and FAERS data yielded a PPV of 97 % with 88 % accuracy.

CONCLUSION

Using all cumulative data, signal detection in SRS data achieved higher specificity and sensitivity than EHR data. However, when data were restricted to time prior to a regulatory action, performance characteristics changed in a manner consistent with both the type of data and nature of the ADR. Further research focusing on prospective validation of is necessary to learn more about the performance and utility of these databases in modern pharmacovigilance practice.