Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions

Harpaz, Rave; Vilar, Santiago; DuMouchel, William; Salmasian, Hojjat; Haerian, Krystl; Shah, Nigam H.; Chase, Herbert; Friedman, Carol

doi:10.1136/amiajnl-2012-000930

Cited by 143 publications

(122 citation statements)

References 39 publications

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“…2,6,8,14,17,18 Despite such limitations, accumulating evidence from several hundred published studies of dozens of drugs confirms the utility of FAERS data mining for yielding new insights about drug safety signals. 2,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] Nevertheless, FAERS limitations and other qualifications noted here should always be considered when using the RxFilter. We strongly recommend that patients consult with their prescribing physician before taking any action that relates to information they find in FAERS or our platform.…”

Section: Limitationsmentioning

confidence: 99%

Development of a drug safety ePlatform for physicians, pharmacists, and consumers based on post-marketing adverse events

Hoffman¹,

Overstreet²,

Doraiswamy

2013

Drugs Ther Studies

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Rigorous clinical trials under the watchful eye of regulators remain the cornerstone of drug safety. However, the emergence of serious and life-threatening Adverse Events (AEs) across best-selling drug classes [sometimes many years after winning Food and Drug Administration (FDA) approval] underscores the limitations of current clinical trial processes and reinforces the need for careful postapproval pharmacovigilance. The FDA's sizeable repository of patient case reports linking AEs to approved drugs is the Adverse Event Reporting System (FAERS). We believe that open and user-friendly access to the millions of case reports in FAERS would help advance the field of post-marketing pharmacovigilance. However, FAERS data are virtually inaccessible to most physicians, pharmacists, and consumers. Accordingly, we have recently launched a big data platform (www.AdverseEvents.com) that, unlike previous efforts, provides on-demand, user-friendly, and highimpact access to FAERS data. Bringing the power of big data to regular users, such as clinicians, pharmacists, and patients, is the logical next step in the transformation of health care to a model of shared decision making between consumers and the system. Public health impact of adverse eventsRigorous clinical trials and toxicology studies under the watchful eye of regulators remain the cornerstone of drug safety. However, the emergence of serious and lifethreatening Adverse Events (AEs) across bestselling drug classes (severe cardiac complications from sibutramine, fatal muscle-wasting syndrome from cerivastatin, increased heart attack and stroke risk from rofecoxib, etc.) many years after they won Food and Drug Administration (FDA) approval, and were taken by tens of thousands of consumers underscores the limitations of current clinical trial processes and reinforces the need for careful post-approval pharmacovigilance. [1][2][3][4] In fact, more than 770,000 injuries or deaths occur each year as a result of AEs linked to FDA approved drugs. 5 It has been estimated that approximately 28% of such events could potentially be prevented through computerized monitoring systems. 1 The FDA's repository of drugrelated AEs is the Adverse Event Reporting System (FAERS) while their AE database for medical devices is the Manufacturer and User Facility Device Experience (MAUDE). 6,7 We believe that open and user-friendly access to the millions of case reports in FAERS would help advance the field of post-marketing pharmacovigilance. However, FAERS data are virtually inaccessible to most physicians, pharmacists, and consumers. Here we provide details concerning a newly launched big data platform (www.AdverseEvents.com) that, unlike previous efforts, provides physicians, pharmacists, and consumers with on-demand, user-friendly, and high-impact access to FAERS data. Current state of the FAERS databaseApproximately 700,000 AEs are logged into FAERS each year, across multiple therapeutic categories and ~4500 drugs. 5 Despite the limitations of FAERS (e.g. variable quality of re...

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Section: Limitationsmentioning

confidence: 99%

Development of a drug safety ePlatform for physicians, pharmacists, and consumers based on post-marketing adverse events

Hoffman¹,

Overstreet²,

Doraiswamy

2013

Drugs Ther Studies

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“…Examples of the combined use of standard NLP and text-and data-mining are found in [139][140][141] where cTAKES is used with Boolean logic to perform phenotyping and to extract drug-side effects. MedLEE was applied for: 1) adverse drug reaction (ADR) signaling, where the association between a drug and an ADR was obtained by using disproportionality analysis [142,143] or Boolean logic [144], or by building and analyzing statistical distributions of concepts (i.e., diseases, symptoms, medications) extracted from the narrative text [145]; 2) EHR-data driven phenotyping using Boolean logic on MedLEE-extracted concepts [136,146]; 3) automated classification of outcomes from the analysis of emergency department computed tomography imaging reports using machine learning methods, such as decision trees [147]. MetaMap has been used with logistic regression in [148] to discover inappropriate use of emergency room based on information on drugs, psychological characteristics, diagnoses, and symptoms.…”

Section: F Extraction Of Information From Unstructured Clinical Datamentioning

confidence: 99%

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Meystre¹,

Lovis²,

Bürkle³

et al. 2017

Yearb Med Inform

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SummaryObjective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research.

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“…A case in point is the reported signal of acute pancreatitis with rasburicase that emerged from a study by Harpaz and colleagues of combining quantitative signal detection findings from spontaneous reporting system (SRS) data with that of electronic health records (EHRs) [Harpaz et al 2013], herein referred to as the 'index publication' (IP).…”

Section: Introductionmentioning

confidence: 99%

“…The authors of the IP attempted to boost signal detection (SD) performance by overlapping positive data-mining findings from SRS and EHRs [Harpaz et al 2013]. In a manner of speaking, this represents performing the well established SD and initial signal evaluation (SE) steps concurrently.…”

Section: Introductionmentioning

confidence: 99%

Revisiting the reported signal of acute pancreatitis with rasburicase: an object lesson in pharmacovigilance

Hauben

Hung

2016

Therapeutic Advances in Drug Safety

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Introduction:There is an interest in methodologies to expeditiously detect credible signals of drug-induced pancreatitis. An example is the reported signal of pancreatitis with rasburicase emerging from a study [the 'index publication' (IP)] combining quantitative signal detection findings from a spontaneous reporting system (SRS) and electronic health records (EHRs). The signal was reportedly supported by a clinical review with a case series manuscript in progress. The reported signal is noteworthy, being initially classified as a false-positive finding for the chosen reference standard, but reclassified as a 'clinically supported' signal. Objective: This paper has dual objectives: to revisit the signal of rasburicase and acute pancreatitis and extend the original analysis via reexamination of its findings, in light of more contemporary data; and to motivate discussions on key issues in signal detection and evaluation, including recent findings from a major international pharmacovigilance research initiative. Methodology: We used the same methodology as the IP, including the same disproportionality analysis software/dataset for calculating observed to expected reporting frequencies (O/ Es), Medical Dictionary for Regulatory Activities Preferred Term, and O/E metric/threshold combination defining a signal of disproportionate reporting. Baseline analysis results prompted supplementary analyses using alternative analytical choices. We performed a comprehensive literature search to identify additional published case reports of rasburicase and pancreatitis. Results: We could not replicate positive findings (e.g. a signal or statistic of disproportionate reporting) from the SRS data using the same algorithm, software, dataset and vendor specified in the IP. The reporting association was statistically highlighted in default and supplemental analysis when more sensitive forms of disproportionality analysis were used. Two of three reports in the FAERS database were assessed as likely duplicate reports. We did not identify any additional reports in the FAERS corresponding to the three cases identified in the IP using EHRs. We did not identify additional published reports of pancreatitis associated with rasburicase. Discussion: Our exercise stimulated interesting discussions of key points in signal detection and evaluation, including causality assessment, signal detection algorithm performance, pharmacovigilance terminology, duplicate reporting, mechanisms for communicating signals, the structure of the FAERs database, and recent results from a major international pharmacovigilance research initiative.

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Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions

Cited by 143 publications

References 39 publications

Development of a drug safety ePlatform for physicians, pharmacists, and consumers based on post-marketing adverse events

Development of a drug safety ePlatform for physicians, pharmacists, and consumers based on post-marketing adverse events

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Revisiting the reported signal of acute pancreatitis with rasburicase: an object lesson in pharmacovigilance

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