Drug Safety Signals Research Articles

A Clustering Ensemble Method for Drug Safety Signal Detection in Post-Marketing Surveillance.

Post-marketing surveillance refers to the process of monitoring the safety of drugs once they reach the market, after the successful completion of clinical trials. In this work, we investigate a computational approach using data mining tools to detect safety signals from post-market safety databases, or in other words, to identify adverse events (AEs) with disproportionately high reporting rates compared to other AEs, associated with a particular drug or a drug class. Essentially, we view this as a problem of cluster analysis-based anomaly detection on post-market safety data, where the goal is to 'unsupervisedly' detect the anomalous or the signal AEs. Our findings demonstrate the potential of using a clustering ensemble method to detect drug safety signals. It employs multiple clustering or anomaly detection algorithms, followed by a performance comparison of the candidate algorithms based on a collection of appropriate measures of goodness of clustering results. The method is general enough to include any number of clustering or anomaly detection algorithms and goodness measures, and performs better than any of the candidate algorithms in identifying the signal AEs. Extensive simulation studies illustrate that the ensemble method detects the AE signals from synthetic post-market safety datasets pretty accurately across the different scenarios explored. Based on the cases reported to the FDA Adverse Event Reporting System (FAERS) between 2013 and 2022, we further demonstrate that the ensemble method successfully identifies and confirms most of the adverse events that are known to occur most frequently in reaction to antiepileptic drugs and -lactam antibiotics.

Simultaneous Confidence Intervals for Signal Detection and Ascertaining Precision of Adverse Event Rates in Clinical Trials

The marketing authorization of a medicinal product is contingent upon demonstration of safety and efficacy in support of the product’s labeled conditions of use. To demonstrate safety, one group of adverse events that requires detailed consideration is common adverse reactions (ARs). ARs and their frequency are reported in prescription drug labeling in the US and EU. The determination of these adverse reactions generally takes a simple approach—usually, inclusion is through the frequency of reporting and whether the adverse event (AE) rate for the drug exceeds the placebo rate. This standard method does not account for confounders or multiplicity. To overcome these limitations, we propose a Monte Carlo approach to detect drug safety signals in clinical trials. We fit regression models incorporating covariates to assess the drug effect on the rate of AEs. Adjustment for multiplicity is carried out through the construction of simultaneous confidence intervals accounting for arbitrary correlations. A computationally efficient multiplier bootstrap approach using the Rademacher sequences is developed to generate random samples from the joint distribution of the estimators for all the AE rates. Compared to Bonferroni-based methods, the proposed method leads to narrower simultaneous confidence intervals and is more powerful in detecting potential safety signals.

Pharmacovigilance in the Age of Legalized Cannabis: Using Social Media to Monitor Drug-Drug Interactions Between Immunosuppressants and Cannabis-Derived Products.

A clinical trial of Epidiolex®, the only US FDA-approved cannabis-derived consumer product (CDP), discovered an interaction with an immunosuppressant (tacrolimus) that led to drug toxicity, highlighting the unique intersection of prescription and commonly unregulated consumer products. We aimed to identify if similar drug-drug interactions (DDIs) are occurring among the consumer CDP market, even though they cannot be identified through trials. We searched Reddit for subreddits related to CDPs or health, resulting in 63,561,233 posts. From these, we identified 190 posts discussing both immunosuppressants and CDPs. Two blinded investigators evaluated the following. (1) Was there a concern about a potential DDI between consumer CDPs and immunosuppressants? (2) Was there a unique adverse event attributed to a DDI between consumer CDPs and immunosuppressants? Of these, 66 posts (35%) expressed concern about a potential DDI, such as "Hey, my partner wants to try my edibles … she's on Prograf [tacrolimus] and wants to talk to a stoner who's had a heart transplant." Four posts (2%) reported a unique DDI, such as "I have clinical results that are semi-anecdotal, showing the coordination to my halting substance use … It's the CBD. Shot my prograf to 30 at like 4mg." Two of the four reported DDIs are similar to those first reported for Epidiolex. The remaining two reported DDIs include a potential cannabidiol (CBD)/sirolimus or delta-9-tetrahydrocannabinol (THC)/sirolimus interaction and a THC/tacrolimus interaction, both resulting in drug toxicity. This case study is the first to report on DDIs involving consumer CDPs, including both CBD and THC products, as well as a broader class of immunosuppressants. This demonstrates the risks associated with using consumer CDPs alongside prescription medications while highlighting the need for development of increased surveillance to monitor consumer CDPs for drug safety signals, as well as comprehensive regulations that take into account the unique characteristics of the consumer marketplace.

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.

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement.

Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration.

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database.

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

Role of Social Media for Drug Safety Signal Detection

Role of Social Media for Drug Safety Signal Detection

An artificial intelligence algorithm for co‐clustering to help in pharmacovigilance before and during the COVID‐19 pandemic

AbstractAimsMonitoring drug safety in real‐world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post‐marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID‐19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co‐clustering method to help the monitoring of drug safety.MethodsA dynamic latent block model (dLBM), based on a time‐dependent co‐clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR).ResultsOur model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID‐19 vaccines. The dLBM also highlighted some specific drug–ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding.ConclusionsCo‐clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, ‘unsupervisedly’, the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports.

Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.

In refining drug safety signals, defining the object of study is crucial. While research has explored the effect of different event definitions, drug definition is often overlooked. The US FDA Adverse Event Reporting System (FAERS) records drug names as free text, necessitating mapping to active ingredients. Although pre-mapped databases exist, the subjectivity and lack of transparency of the mapping process lead to a loss of control over the object of study. We implemented the DiAna dictionary, systematically mapping individual free-text instances to their corresponding active ingredients and linking them to the World Health Organization Anatomical Therapeutic Chemical (WHO-ATC) classification. We retrieved all drug names reported to the FAERS (2004-December 2022). Using existing vocabularies and string editing, we automatically mapped free text to ingredients. We manually revised the mapping and linked it to the ATC classification. We retrieved 18,151,842 reports, with 74,143,411 drug entries. We manually checked the first 14,832 terms, up to terms occurring over 200 times (96.88% of total drug entries), to 6282 unique active ingredients. Automatic unchecked translations extend the standardization to 346,854 terms (98.94%). The DiAna dictionary showed a higher sensitivity compared with RxNorm alone, particularly for specific drugs (e.g., rimegepant, adapalene, drospirenone, umeclidinium). The most prominent drug classes in the FAERS were immunomodulating (37.40%) and neurologic drugs (29.19%). The DiAna dictionary, as a dynamic open-source tool, provides transparency and flexibility, enabling researchers to actively shape drug definitions during the mapping phase. This empowerment enhances accuracy, reproducibility, and interpretability of results.

High-Throughput Computing to Automate Population-Based Studies to Detect the 30-Day Risk of Adverse Outcomes After New Outpatient Medication Use in Older Adults with Chronic Kidney Disease: A Clinical Research Protocol.

Safety issues are detected in about one third of prescription drugs in the years following regulatory agency approval. Older adults, especially those with chronic kidney disease, are at particular risk of adverse reactions to prescription drugs. This protocol describes a new approach that may identify credible drug-safety signals more efficiently using administrative health care data. To use high-throughput computing and automation to conduct 700+ drug-safety cohort studies in older adults in Ontario, Canada. Each study will compare 74 acute (30-day) outcomes in patients who start a new prescription drug (new users) to a group of nonusers with similar baseline health characteristics. Risks will be assessed within strata of baseline kidney function. The studies will be population-based, new-user cohort studies conducted using linked administrative health care databases in Ontario, Canada (January 1, 2008, to March 1, 2020). The source population for these studies will be residents of Ontario aged 66 years or older who filled at least one outpatient prescription through the Ontario Drug Benefit (ODB) program during the study period (all residents have universal health care, and those aged 65+ have universal prescription drug coverage through the ODB). We identified 3.2 million older adults in the source population during the study period and built 700+ initial medication cohorts, each containing mutually exclusive groups of new users and nonusers. Nonusers were randomly assigned cohort entry dates that followed the same distribution of prescription start dates as new users. Eligibility criteria included a baseline estimated glomerular filtration rate (eGFR) measurement within 12 months before the cohort entry date (median time was 71 days before cohort entry in the new user group), no prior receipt of maintenance dialysis or a kidney transplant, and no prior prescriptions for drugs in the same subclass as the study drug. New users and nonusers will be balanced on ~400 baseline health characteristics using inverse probability of treatment weighting on propensity scores within 3 strata of baseline eGFR: ≥60, 45 to <60, <45 mL/min per 1.73 m2. We will compare new user and nonuser groups on 74 clinically relevant outcomes (17 composites and 57 individual outcomes) in the 30 days after cohort entry. We used a prespecified approach to identify these 74 outcomes. In each cohort, we will obtain eGFR-stratum-specific weighted risk ratios and risk differences using modified Poisson regression and binomial regression, respectively. Additive and multiplicative interaction by eGFR category will be examined. Drug-outcome associations that meet prespecified criteria (identified signals) will be further examined in additional analyses (including survival, negative-control exposure, and E-value analyses) and visualizations. The initial medication cohorts had a median of 6120 new users per cohort (interquartile range: 1469-38 839) and a median of 1 088 301 nonusers (interquartile range: 751 697-1 267 009). Medications with the largest number of new users were amoxicillin trihydrate (n = 1 000 032), cephalexin (n = 571 566), prescription acetaminophen (n = 571 563), and ciprofloxacin (n = 504,374); 19% to 29% of new users in these cohorts had an eGFR <60 mL/min per 1.73 m2. Despite our use of robust techniques to balance baseline indicators and to control for confounding by indication, residual confounding will remain a possibility. Only acute (30-day) outcomes will be examined. Our data sources do not include nonprescription (over-the-counter) drugs or drugs prescribed in hospitals and do not include outpatient prescription drug use in children or adults <65 years. This accelerated approach to conducting postmarket drug-safety studies has the potential to more efficiently detect drug-safety signals in a vulnerable population. The results of this protocol may ultimately help improve medication safety.

Exploring the association between weight loss-inducing medications and multiple sclerosis: insights from the FDA adverse event reporting system database.

Several studies have demonstrated that early childhood and adolescent obesity are risk factors for multiple sclerosis (MS) susceptibility. Obesity is thought to share inflammatory components with MS through overproduction of pro-inflammatory adipokines (e.g., leptin) and reduction of anti-inflammatory adipokines (e.g, adiponectin). Recently, drug repurposing (i.e. identifying new indications for existing drugs) has garnered significant attention. The US Food and Drug Administration Adverse Event Reporting System (FAERS) database serves not only as a resource for mining adverse drug reactions and safety signals but also for identifying inverse associations and potential medication repurposing opportunities. We aimed to explore the association between weight-loss-inducing drugs and MS using real-world reports from the FAERS database. Secondary analysis of existing data from the FAERS database. We conducted a disproportionality analysis using the FAERS database between the fourth quarter of 2003 and the second quarter of 2023 to explore associations between MS and weight loss-inducing drugs. Disproportionality was quantified using the reporting odds ratio (ROR). An inverse association was defined when the upper limit of the 95% confidence interval for ROR was <1. We found an inverse association between MS and anti-diabetic weight loss-inducing drugs including semaglutide (ROR: 0.238; 95% CI: 0.132-0.429), dulaglutide (ROR: 0.165; 95% CI: 0.109-0.248), liraglutide (ROR: 0.161; 95% CI: 0.091-0.284), empagliflozin (ROR: 0.234; 95% CI: 0.146-0.377), and metformin (ROR: 0.387; 95% CI: 0.340-0.440). No inverse associations were found for other weight loss-inducing drugs such as phentermine, bupropion, topiramate, zonisamide, and amphetamine. An exception was naltrexone (ROR: 0.556; 95% CI: 0.384-0.806). Our findings suggest a potential consideration for repurposing anti-diabetic weight loss-inducing drugs including semaglutide, dulaglutide, and liraglutide (glucagon-like peptide-1 receptor agonists), empagliflozin (sodium-glucose cotransporter-2 inhibitor), and metformin (biguanide), for MS. This warrants validation through rigorous methodologies and prospective studies.

Alignment of Later Phase Clinical Trial Eligibility Criteria with Drug Safety in Acute Leukemia: Minimizing Unnecessary Patient Risk and Exclusion

Introduction Clinical trial eligibility criteria can unfairly exclude patients or expose patients to risk if they are not justified by drug safety. Our prior analysis of acute myeloid leukemia (AML) trials identified unjustified variability in the use of criteria and in the limits placed when criteria were used (ASCO 2023 Abstract #6525). As additional safety data are available in later phase studies, we sought to assess if criteria were more justified in phase III versus phase II trials, and we extended our analysis to include acute lymphoblastic leukemia (ALL). Methods Trials and criteria were identified using ClinicalTrials.gov. Trials were included if they treated patients in the United States, were interventional, therapeutic, phase II or III, treated previously untreated adults with AML and/or ALL, were not cellular therapy trials, and had study start dates from 1/1/2010-12/31/2019. Drug safety data from labels, trial protocols, and previous study results available as of study start dates, as identified in the ClinicalTrials.gov record, were compared to systematically coded eligibility criteria following FDA publications.Primary endpoints were differences in discordant criteria use (presence of criteria without a known, relevant drug safety signal or absence of criteria with a signal) and limits (numerical values of drug safety- or criteria-based limits) between phase II and III studies assessed via Fisher's exact, rank sum, and median absolute deviation (MAD) testing. Regression modeling assessed differences in infectious disease (ID) exclusions by phase adjusted for disease type and trial sponsor, year, size, and drug safety profiles (drug-drug interaction and association with viral reactivation). Results There were 250 eligible trials, of which 203 were phase II and 47 phase III; 190 trials were in AML and 60 ALL. Similar proportions of phase II and III trials had exclusion criteria (with specific limits when applicable) for low left ventricular ejection fraction (LVEF; 36 vs 25%), high corrected QT interval (QTc; 21 vs 27%), presence of human immunodeficiency virus (HIV; 57 vs 47%), and hepatitis B (43 vs 38%) and C (42 vs 38%). More phase II than phase III trials had renal function (70 vs 53%; p=0.04), bilirubin (73 vs 49%; p&amp;lt;0.01), and alanine transaminase/aspartate transaminase (AST/ALT; 26 vs 64%; p&amp;lt;0.01) based exclusions. Table 1 shows proportions and odds ratios (OR) of discordant criteria by trial phase. OR of discordance for renal function (1.90), AST/ALT (2.30), and bilirubin (2.45) were significantly higher for phase III studies (all p&amp;lt;0.05); no criteria were less discordant for phase III studies. Drug safety- and trial-based limits for renal and liver function are shown in Figure 1. Median differences between limits numerically improved from phase II to phase III (renal: 0.62 to 0.20; AST/ALT: 0.50 to 0; bilirubin: 0.20 to 0); only the change in renal function was significant (rank sum p=0.03). The dispersion of these differences was numerically smaller for renal function (phase III to III MAD ratio: 0.65) and bilirubin (0.91) and higher for AST/ALT (1.17); no changes in dispersion were significant. In multivariable regressions adjusted for other trial and drug safety factors, phase III trials were equally likely to exclude persons diagnosed with HIV and hepatitis B and C as phase II (adjusted OR 1.48, 1.37, 1.41; all p&amp;gt;0.05). Conclusions Despite increasing understanding of drug safety profiles in later phase trials, the use of eligibility criteria for phase III acute leukemia clinical trials were at least as discordant with known drug safety as earlier phase studies. When drug safety- and trial-based limits were present, phase III criteria were not consistently more aligned with drug safety and were equally likely to exclude persons diagnosed with HIV and hepatitis. Future phase III trials in acute leukemia should standardize and rigorously justify eligibility criteria to enhance patient representativeness and welfare.

Impact of the COVID-19 pandemic on the spontaneous reporting and signal detection of adverse drug events

AbstractExternal factors severely affecting in a short period of time the spontaneous reporting of adverse events (AEs) can significantly impact drug safety signal detection. Coronavirus disease 2019 (COVID-19) represented an enormous challenge for health systems, with over 767 million cases and massive vaccination campaigns involving over 70% of the worldwide population. This study investigates the potential masking effect on certain AEs caused by the substantial increase in reports solely related to COVID-19 vaccines within various spontaneous reporting systems (SRSs). Three SRSs were used to monitor AEs reporting before and during the pandemic, namely, the World Health Organisation (WHO) global individual case safety reports database (VigiBase®), the United States Food and Drug Administration Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report database (JADER). Findings revealed a sudden over-reporting of 35 AEs (≥ 200%) during the pandemic, with an increment of the RRF value in 2021 of at least double the RRF reported in 2020. This translates into a substantial reduction in signals of disproportionate reporting (SDR) due to the massive inclusion of COVID-19 vaccine reports. To mitigate the masking effect of COVID-19 vaccines in post-marketing SRS analyses, we recommend utilizing COVID-19-corrected versions for a more accurate assessment.

Methods for drug safety signal detection using routinely collected observational electronic health care data: a systematic review

Signal detection is a crucial step in the discovery of post-marketing adverse drug reactions. There is a growing interest in using routinely collected data to complement established spontaneous report analyses.The aim. This work aims to systematically review the methods for drug safety signal detection using routinely collected healthcare data and their performance, both in general and for specific types of drugs and outcomes.Metodology. We conducted a systematic review following the PRISMA guidelines, and registered a protocol in PROSPERO.Results. The review included 101 articles, among which there were 39 methodological works, 25 performance assessment papers, and 24 observational studies. Methods included adaptations from those used with spontaneous reports, traditional epidemiological designs, methods specific to signal detection with real-world data. More recently, implementations of machine learning have been studied in the literature. Twenty-five studies evaluated method performances, 16 of them using the area under the curve (AUC) for a range of positive and negative controls as their main measure. Despite the likelihood that performance measurement could vary by drug-event pair, only 10 studies reported performance stratified by drugs and outcomes, in a heterogeneous manner. The replicability of the performance assessment results was limited due to lack of transparency in reporting and the lack of a gold standard reference set.

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study.

The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources. Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA. We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities. Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries. The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.

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

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. 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. 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. 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.

Drug safety signal detection in a regional healthcare database using the tree-based scan statistic and comparison to 3 other mining methods.

To evaluate and compare the relative performance of the tree-based scan statistic (TreeScan) with the crude cohort study, Bayesian confidence propagation neural network (BCPNN) and Gamma Poisson Shrinker (GPS) in detecting statin-related adverse events (AEs) in an electronic healthcare database. Data from a Chinese healthcare database from 2010 to 2016 were evaluated. We identified statin users based on prescription information in their out-/in-patient records, and AEs were defined according to the ICD-10 codes in patients' diagnosis records. TreeScan was applied to detect AE signals related to statin use and was compared with 3 other methods based on sensitivity, specificity, positive predictive value, negative predictive value, accuracy, the Youden index, area under the precision-recall curve and the area under the receiver operating characteristic curve. A total of 224 187 patients were enrolled and divided into 85 758 statin users and 138 429 nonusers. TreeScan generated 29 positive signals, of which 9 were known AEs. The sensitivities of TreeScan, BCPNN and GPS were all 69.2%, which was higher than that of the crude cohort study (46%). The specificity (82.3%), positive predictive value (31.0%), negative predictive value (95.9%), accuracy (81.0%), Youden index (51.5%) and area under the receiver operating characteristic curve (75.8%) of TreeScan were the highest among the 4 methods. TreeScan outperformed the crude cohort, BCPNN and GPS in detecting statin-related AEs in an electronic healthcare database. Therefore, it can be used as a complementary tool for other signal detection methods in drug safety surveillance.

A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data

Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating data-mining tools, and evaluate adaptability of such system. Based on relevant guidelines and structural frameworks in Taiwan's pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan's National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system. Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation. The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.

Detection of drug safety signal of drug-induced neutropenia and agranulocytosis in all-aged patients using electronic medical records.

We explored the adverse drug reaction signals of drug-induced neutropenia (DIN) and drug-induced agranulocytosis (DIA) in hospitalized patients and evaluated the novelty of these correlations. A two-step method was established to identify the relationship between drugs and DIN or DIA using 5-year electronic medical records (EMRs) obtained from 242 000 patients at Qilu Hospital of Shandong University. First, the drugs suspected to induce DIN or DIA were selected. The associations between suspected drugs and DIN or DIA were evaluated by a retrospective cohort study using unconditional logistic regression analysis and multiple linear regression model. Twelve suspected drugs (vancomycin, meropenem, voriconazole, acyclovir, ganciclovir, fluconazole, oseltamivir, linezolid, compound borax solution, palonosetron, polyene phosphatidylcholine, and sulfamethoxazole) were associated with DIN, and six suspected drugs (vancomycin, voriconazole, acyclovir, ganciclovir, fluconazole, and oseltamivir) were associated with DIA. The multivariate linear regression model revealed that nine drugs (vancomycin, meropenem, voriconazole, ganciclovir, fluconazole, oseltamivir, compound borax solution, palonosetron, and polyene phosphatidylcholine) and four drugs (vancomycin, voriconazole, ganciclovir, and fluconazole) were found to be associated with DIN and DIA, respectively. While logistic regression analysis revealed that palonosetron and ganciclovir were associated with DIN and DIA, respectively. Palonosetron and ganciclovir were found to be correlated with drug-induced granulocytopenia. The results of this study provide an early warning of drug safety signals for drug-induced granulocytopenia, facilitating a quick and appropriate response for clinicians.