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

An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

Active surveillance pharmacovigilance for Clostridioides difficile infection and gastrointestinal bleeding: an analytic framework based on case-control studies

BACKGROUND

Active surveillance pharmacovigilance is an emerging approach to identify medications with unanticipated effects. We previously developed a framework called pharmacopeia-wide association studies (PharmWAS) that limits false positive medication associations through high-dimensional confounding adjustment and set enrichment. We aimed to assess the transportability and generalizability of the PharmWAS framework by using medical claims data to reproduce known medication associations with Clostridioides difficile infection (CDI) or gastrointestinal bleeding (GIB).

METHODS

We conducted case-control studies using Optum's de-identified Clinformatics Data Mart Database of individuals enrolled in large commercial and Medicare Advantage health plans in the United States. Individuals with CDI (from 2010 to 2015) or GIB (from 2010 to 2021) were matched to controls by age and sex. We identified all medications utilized prior to diagnosis and analysed the association of each with CDI or GIB using conditional logistic regression adjusted for risk factors for the outcome and a high-dimensional propensity score.

FINDINGS

For the CDI study, we identified 55,137 cases, 220,543 controls, and 290 medications to analyse. Antibiotics with Gram-negative spectrum, including ciprofloxacin (aOR 2.83), ceftriaxone (aOR 2.65), and levofloxacin (aOR 1.60), were strongly associated. For the GIB study, we identified 450,315 cases, 1,801,260 controls, and 354 medications to analyse. Antiplatelets, anticoagulants, and non-steroidal anti-inflammatory drugs, including ticagrelor (aOR 2.81), naproxen (aOR 1.87), and rivaroxaban (aOR 1.31), were strongly associated.

INTERPRETATION

These studies demonstrate the generalizability and transportability of the PharmWAS pharmacovigilance framework. With additional validation, PharmWAS could complement traditional passive surveillance systems to identify medications that unexpectedly provoke or prevent high-impact conditions.

FUNDING

U.S. National Institute of Diabetes and Digestive and Kidney Diseases.

Use of Electronic Health Record Data for Drug Safety Signal Identification: A Scoping Review

INTRODUCTION

Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance.

METHODS

To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices.

RESULTS

We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations.

CONCLUSION

Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance.

Use of Diagnosis Codes to Find Blood Transfusion Adverse Events in Electronic Health Records

OBJECTIVE

Electronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care). We used International Classification of Diseases, Ninth Revision, codes in electronic records to identify known, and potentially novel, adverse reactions to blood transfusion.

METHODS

We used 49,331 adult admissions involving critical care at a major teaching hospital, 2001-2012, in the Medical Information Mart for Intensive Care III EHRs database. We formed a T (defined as packed red blood cells, platelets, or plasma) group of 21,443 admissions versus 25,468 comparison (C) admissions. The International Classification of Diseases, Ninth Revision, Clinical Modification , diagnosis codes were compared for T versus C, described, and tested with statistical tools.

RESULTS

Transfusion adverse events (TAEs) such as transfusion-associated circulatory overload (TACO; 12 T cases; rate ratio [RR], 15.61; 95% confidence interval [CI], 2.49-98) were found. There were also potential TAEs similar to TAEs, such as fluid overload disorder (361 T admissions; RR, 2.24; 95% CI, 1.88-2.65), similar to TACO. Some diagnoses could have been sequelae of TAEs, including nontraumatic compartment syndrome of abdomen (52 T cases; RR, 6.76; 95% CI, 3.40-14.9) possibly being a consequence of TACO.

CONCLUSIONS

Surveillance for diagnosis codes that could be TAE sequelae or unrecognized TAE might be useful supplements to existing medical product adverse event programs.

UK veterinary professionals' perceptions and experiences of adverse drug reaction reporting

BACKGROUND

Spontaneous reporting of suspected adverse drug reactions (ADRs) is the cornerstone of pharmacovigilance. Despite this, it is believed that there is significant under-reporting in the veterinary setting. Low reporting rates delay marketing authorisation holders (MAHs) and regulators taking mitigating action in the case of safety concerns.

METHOD

We designed a survey to explore the perceptions, attitudes and experiences of UK veterinary professionals towards ADR reporting. The survey was advertised widely through conventional and social media and at several conferences.

RESULTS

In total, 260 respondents completed the survey, including 210 veterinary surgeons, 49 veterinary nurses and one suitably qualified person. Respondents generally understood the need to report ADRs. The main barrier to reporting was the suspected ADR being well known, and the most popular potential facilitator identified was the ability to report via the practice management system. Facilitation via education in the form of a pharmacovigilance themed continuing professional development event was particularly popular among veterinary nurses, who reported time as being less of a barrier to reporting than their veterinary surgeon counterparts.

CONCLUSIONS

Our findings suggest that technological interventions to facilitate reporting and empowerment of veterinary nurses to report through a tailored training event should be explored further.

Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method

BACKGROUND

Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome ("attributed") or state the simple treatment and outcome without an association ("unattributed"). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, "transfusion" and "time-based." Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians' documentation of attributed AEs.

OBJECTIVE

We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs.

METHODS

We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs.

RESULTS

Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination.

CONCLUSIONS

The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use

INTRODUCTION

When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work.

OBJECTIVE

We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects.

METHODS

We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product.

RESULTS

Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene-drug class pairs.

CONCLUSIONS

By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.

Integrating Electronic Health Record Data into the ADEpedia-on-OHDSI Platform for Improved Signal Detection: A Case Study of Immune-related Adverse Events

With widespread adoption of electronic health records (EHRs), Real World Data and Real World Evidence (RWE) have been increasingly used by FDA for evaluating drug safety and effectiveness. However, integration of heterogeneous drug safety data sources and systems remains an impediment for effective pharmacovigilance studies. In an ongoing project, we have developed a next generation pharmacovigilance signal detection framework known as ADEpedia-on-OHDSI using the OMOP common data model (CDM). The objective of the study is to demonstrate the feasibility of the framework for integrating both spontaneous reporting data and EHR data for improved signal detection with a case study of immune-related adverse events. We first loaded the OMOP CDM with both recent and legacy FAERS (FDA Adverse Event Reporting System) data (from the time period between Jan. 2004 and Dec. 2018). We also integrated the clinical data from the Mayo Clinic EHR system for six oncological immunotherapy drugs. We implemented a signal detection algorithm and compared the timelines of positive signals detected from both FAERS and EHR data. We found that the signals detected from EHRs are 4 months earlier than signals detected from FAERS database (depending on the signal detection methods used) for the ipilimumab-induced hypopituitarism. Our CDM-based approach would be useful to provide a scalable solution to integrate both drug safety data and EHR data to generate RWE for improved signal detection.

Undergraduate and postgraduate pharmacovigilance education: A proposal for appropriate curriculum content

BACKGROUND

Adverse drug reactions (ADRs) are common, often preventable, and a leading cause of morbidity and mortality. Pharmacovigilance (PV) involves detection, assessment, understanding, and prevention of adverse effects or any other drug-related problem. Education of healthcare professionals (HCPs) involved in drug prescription, dispensing and administration is essential to help prevent and mitigate both ADRs and medication errors and has to be focused on 3 pivotal aspects: •Awareness: All medicines can produce adverse effects. ADRs should always be considered as part of the differential diagnosis if any new adverse condition, symptoms or signs appear after a drug administration or during or after pharmacological treatment. •Knowledge: HCPs must have a sound understanding of the most frequently prescribed drugs and over-the-counter medications, factors that make patients more likely to benefit or more susceptible to harm, as well as of causes of medication errors. •Reporting: HCPs should know how to report ADRs and the role of reporting on regulatory aspects and scientific knowledge. Undergraduate curricula must provide, at a minimum, sufficient skills that warrant the appropriate and safe prescription/dispensing/administration of medications in clinical practice, focusing both on therapeutic effects and prevention of harm. Clinical appraisal skills must include ADRs as differential diagnosis, taking accurate medication history, basic individual causality assessment, identification and proper management of ADRs, and informing patients of possible ADRs. Postgraduate periodic PV training should be mandatory as part of continuing education. Specialised postgraduate education should include advanced contents.

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.