Adverse Drug Reaction Signals Research Articles

Abstract 4923: A systematic framework for predicting adverse drug reaction signals using medical data

Abstract Prescribing a combination of medications is a common strategy to enhance the effectiveness of disease treatment. While this approach aims to improve therapeutic outcomes, it may lead to unintended adverse drug reactions (ADRs) due to interactions between prescribed drugs and individual factors. Recognizing the importance of ADRs, various machine learning models have been developed. However, these models face limitations, particularly in predicting reactions induced by three or more drugs and in accounting for individual patient factors, such as age and medical history. To address these limitations, we propose a systematic framework that leverages medical data for predicting ADR signals. In the framework, the MIMIC-IV database was systematically preprocessed, and a series of machine learning models were developed that predict the ADR signals. The model predictions were validated using the eICU collaborative research database. The machine learning models process multiple inputs, including information on administered drugs, age, gender, ethnicity, and underlying medical conditions to predict ADR signals. The ADR signals are determined by classifying abnormalities in 20 specific laboratory test values, such as hematocrit, creatinine, and hemoglobin. The machine learning models developed in this study hold promise as a valuable tool for assessing potential risks, such as ADRs, associated with the concurrent use of multiple drugs. Citation Format: Junhyeok Jeon, Eujin Hong, Hyun Uk Kim. A systematic framework for predicting adverse drug reaction signals using medical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4923.

New Adverse Drug Reaction Signals from 2017 to 2021-Genuine Alerts or False Alarms?

Spontaneous adverse events reporting systems are used internationally to flag new or unexpected adverse drug reactions (ADRs). Disproportionality analysis is a recognised technique, but false alarms may arise. We aimed to determine whether these new ADR signals had subsequently been followed-up with detailed hypothesis-testing studies. We searched PubMed to identify published studies (years 2017-2021) where the authors reported findings of new ADR signals from disproportionality analyses. We used PubMed and forward citation tracking (Google Scholar) to identify any subsequent confirmatory studies of these ADR signals. We screened 414 titles and abstracts and checked the full-text articles of 57 studies. We found signals for 56 suspected new ADRs from 24 drugs. Google Scholar showed that the ADR studies had been cited a median of seven times (range 0-61). However, none of the suspected new ADRs had undergone detailed evaluation in the citing literature. Similarly, our PubMed search did not find any confirmation studies for the 56 suspected new ADRs. Although many suspected new ADR signals have been identified through disproportionality analysis, most signals have not been further verified as being either genuine ADRs or false alarms. Researchers must focus on follow-up studies for these new signals.

Detection of novel drug-adverse drug reaction signals in rheumatoid arthritis and ankylosing spondylitis: analysis of Korean real-world biologics registry data

This study aimed to detect signals of adverse drug reactions (ADRs) associated with biological disease-modifying antirheumatic drugs (DMARDs) and targeted therapies in rheumatoid arthritis (RA) and ankylosing spondylitis (AS) patients. Utilizing the KOrean College of Rheumatology BIOlogics & Targeted Therapy Registry (KOBIO) data, we calculated relative risks, excluded previously reported drug-ADR pairs, and externally validated remaining pairs using US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and single centre’s electronic health records (EHR) data. Analyzing data from 2279 RA and 1940 AS patients, we identified 35 significant drug-ADR pairs in RA and 26 in AS, previously unreported in drug labels. Among the novel drug-ADR pairs from KOBIO, 15 were also significant in the FAERS data. Additionally, 2 significant drug-laboratory abnormality pairs were found in RA using CDM MetaLAB analysis. Our findings contribute to the identification of 14 novel drug-ADR signals, expanding our understanding of potential adverse effects related to biological DMARDs and targeted therapies in RA and AS. These results emphasize the importance of ongoing pharmacovigilance for patient safety and optimal therapeutic interventions.

Metformin adverse event profile: a pharmacovigilance study based on the FDA Adverse Event Reporting System (FAERS) from 2004 to 2022

ABSTRACT Background Metformin has the potential for treating numerous diseases, but there are still many unrecognized and unreported adverse events (AEs). Methods We selected data from the United States FDA Adverse Event Reporting System (FAERS) database from the first quarter (Q1) of 2004 to the fourth quarter (Q4) of 2022 for disproportionality analysis to assess the association between metformin and related adverse events. Results In this study 10,500,295 case reports were collected from the FAERS database, of which 56,674 adverse events related to metformin were reported. A total of 643 preferred terms (PTs) and 27 system organ classes (SOCs) that were significant disproportionality conforming to the four algorithms simultaneously were included. The SOCs included metabolic and nutritional disorders (p = 0.00E + 00), gastrointestinal disorders (p = 0.00E + 00) and others. PT levels were screened for adverse drug reaction (ADR) signals such as acute pancreatitis (p = 0.00E + 00), melas syndrome, pemphigoid (p = 0.00E + 00), skin eruption (p = 0.00E + 00) and drug exposure during pregnancy (p = 0.00E + 00). Conclusion Most of our results were consistent with the specification, but some new signals of adverse reactions such as acute pancreatitis were not included. Therefore, further studies are needed to validate unlabeled adverse reactions and provide important support for clinical monitoring and risk identification of metformin.

Enhancing antidepressant safety surveillance: comparative analysis of adverse drug reaction signals in spontaneous reporting and healthcare claims databases.

Background/Objective: Spontaneous reporting systems (SRS) such as the Korea Adverse Event Reporting System (KAERS) are limited in their ability to detect adverse drug reaction (ADR) signals due to their limited data on drug use. Conversely, the national health insurance claim (NHIC) data include drug use information for all qualifying residents. This study aimed to compare ADR signal profiles for antidepressants between KAERS and NHIC, evaluating the extent to which detected signals belong to common ADRs and labeling information. Materials and Methods: ADR signal detection in KAERS and NHIC databases, spanning January to December 2017, employed disproportionality analysis. Signal classes were determined based on System Organ Class (SOC) of the Medical Dictionary for Regulatory Activities (MedDRA). Also, Common ADR Coverage (CAC), the proportion of detected signals deemed common ADRs, and labeling information coverage (LIC) represented by mean average precision (mAP) were calculated. Additionally, protopathic bias and relative risk (RR) evaluation were performed to check for signal robustness. Results: Signal detection revealed 51 and 62 signals in KAERS and NHIC databases, respectively. Both systems predominantly captured signals related to nervous system disorders, comprising 33.3% (N = 17) in KAERS and 50.8% (N = 31) in NHIC. Regarding the type of antidepressants, KAERS predominantly reported signals associated with tricyclic antidepressants (TCAs) (N = 21, 41.2%), while NHIC produced most signals linked to selective serotonin reuptake inhibitors (SSRIs) (N = 22, 35.5%). KAERS exhibited higher CAC (68.63% vs. 29.03%) than NHIC. LIC was also higher in KAERS than in NHIC (mAP for EB05: 1.00 vs. 0.983); i.e., NHIC identified 5 signals not documented in drug labeling information, while KAERS found none. Among the unlabeled signals, one (Duloxetine-Myelopathy) was from protopathic bias, and two (duloxetine-myelopathy and tianeptine-osteomalacia) were statistically significant in RR. Conclusion: NHIC exhibited greater capability in detecting ADR signals associated with antidepressant use, encompassing unlabeled ADR signals, compared to KAERS. NHIC also demonstrated greater potential for identifying less common ADRs. Further investigation is needed for signals detected exclusively in NHIC but not covered by labeling information. This study underscores the value of integrating different sources of data, offering substantial regulatory insights and enriching the scope of pharmacovigilance.

Toxicity Spectrum of Anti-GD2 Immunotherapy: A Real-World Study Leveraging the US Food and Drug Administration Adverse Event Reporting System.

Anti-disialoganglioside (anti-GD2) monoclonal antibodies are effective immunotherapeutic drugs for treating neuroblastoma, yet their toxicity spectrum is unclear. This study aimed to assess the toxicity profiles of three anti-GD2 monoclonal antibodies (dinutuximab, dinutuximab β, and naxitamab) in clinical applications by mining and evaluating the adverse drug reaction (ADR) signals from the US Food and Drug Administration Adverse Event Reporting System. Data in the US Food and Drug Administration Adverse Event Reporting System from the time anti-GD2 monoclonal antibodies became available in the market to the first quarter of 2023 were searched. The signals of anti-GD2 monoclonal antibody-associated ADRs were quantified using four types of algorithms, including the reporting odds ratio, the proportional reporting ratio, the combination of the proportional reporting ratio and χ2 statistic method used by the UK Medicines and Healthcare Products Regulatory Agency, and the Bayesian confidence propagation neural network. The ADRs were categorized by System Organ Class based on the Medical Dictionary for Regulatory Activities, and were sorted according to the frequency and signal strength of ADRs. A total of 370 adverse drug event reports with anti-GD2 monoclonal antibodies listed as the 'primary suspected drugs' were identified, with 116 ADR signals detected, of which 22 were not in the drug labels. Among the adverse drug event reports, 276 reports concerned dinutuximab/dinutuximab β as primary suspected drugs with 90 ADR signals, involving 19 System Organ Classes, of which 21 signals were not in the label; 94 adverse drug event reports concerned naxitamab as the primary suspected drug with 26 ADR signals, involving 11 System Organ Classes, of which one was not in the label. For dinutuximab/dinutuximab β-related ADRs, the top five most frequent were "fever", "abdominal pain", "elevated aspartate aminotransferase (AST)", "elevated alanine aminotransferase (ALT)" and "hypotension"; the top five most intensive signals were "hypoalbuminemia", "elevated AST", "capillary leakage syndrome", "hypoxia" and "elevated ALT". For naxitamab-related ADRs, the top five most frequent were "hypotension", "pain", "urticarial", "hypertension" and "rash"; the top five most intensive signals were "hypotension", "urticaria", "hypoxemia", "bronchospasm" and "hypertension". Involved System Organ Classes included "investigations" and "respiratory, thoracic and mediastinal disorders" containing the most types of ADR signals in dinutuximab/dintuximab β-related ADRs and naxitamab-related ADRs, respectively. Our study comprehensively analyzed the toxicity profiles of anti-GD2 monoclonal antibodies and provides an important reference for clinical monitoring and ADR identification of these drugs.

Safety profile of methotrexate used off-label in ectopic pregnancy: an active monitoring study based on a Chinese hospital pharmacovigilance system

Objective Methotrexate (MTX) is characterized as first-line therapy although its indication of ectopic pregnancy is off-label use. We aimed to conduct a retrospective cohort study to investigate the incidence, characteristics of adverse drug reactions (ADRs) of MTX, provide valuable insights for medical workers. Methods Basing on China Hospital Pharmacovigilance System (CHPS), a retrospective analysis was performed to evaluate the safety of MTX (n = 672). An active monitoring model was set to detect ADR signals from the hospital information system. Frequency, Common Terminology Criteria for Adverse Events (CTCAE) grade proportion and association of dose exposure with ADRs were presented as outcomes. Results The total incidence of ADRs was 54.0%. Anaemia (37.6%) was the most frequent ADR, followed by hepatic function abnormal (11.3%), hyperuricemia (6.1%), neutropenia (4.6%), leukopenia (4.0%), and dyslipidaemia (2.5%). For the composition of all ADRs, CTCAE grade one, two and three dominated for 86.3%, 12.1% and 1.6%, respectively. The severity of hepatic function abnormal was more serious in the two-dose exposed group (p = .021), while other types of ADRs had no statistical or clinical differences. Logistic regression analysis showed the incidence of any ADRs (OR 1.87 [1.31–2.64]; p = .001), hepatic function abnormal (OR 2.75 [1.69–4.48]; p < .001), dyslipidaemia (OR 5.15 [1.87–14.13]; p = .001) were significantly higher in the two-dose exposed group. After adjusted, the positive associations were still maintained. Conclusions MTX is quite safe in ectopic pregnancy, despite its mild to moderate hematotoxicity, hepatotoxicity and nephrotoxicity. Taking CHPS can present the accurate denominator of the incidence of adverse drug reactions into account, our study advocates that it may have great potential to be used as an active monitoring tool for off-label drug use risk management.

Signal detection statistics of adverse drug events in hierarchical structure for matched case-control data.

The tree-based scan statistic is a data mining method used to identify signals of adverse drug reactions in a database of spontaneous reporting systems. It is particularly beneficial when dealing with hierarchical data structures. One may use a retrospective case-control study design from spontaneous reporting systems (SRS) to investigate whether a specific adverse event of interest is associated with certain drugs. However, the existing Bernoulli model of the tree-based scan statistic may not be suitable as it fails to adequately account for dependencies within matched pairs. In this article, we propose signal detection statistics for matched case-control data based on McNemar's test, Wald test for conditional logistic regression, and the likelihood ratio test for a multinomial distribution. Through simulation studies, we demonstrate that our proposed methods outperform the existing approach in terms of the type I error rate, power, sensitivity, and false detection rate. To illustrate our proposed approach, we applied the three methods and the existing method to detect drug signals for dizziness-related adverse events related to antihypertensive drugs using the database of the Korea Adverse Event Reporting System.

MetaLAB-HOI: Template standardization of health outcomes enable massive and accurate detection of adverse drug reactions from electronic health records.

This study aimed to advance the MetaLAB algorithm and verify its performance with multicenter data to effectively detect major adverse drug reactions (ADRs), including drug-induced liver injury. Based on MetaLAB, we created an optimal scenario for detecting ADRs by considering demographic and clinical records. MetaLAB-HOI was developed to identify ADR signals using common model-based multicenter electronic health record (EHR) data from the clinical health outcomes of interest (HOI) template and design for drug-exposed and nonexposed groups. In this study, we calculated the odds ratio of 101 drugs for HOI in Konyang University Hospital, Seoul National University Hospital, Chungbuk National University Hospital, and Seoul National University Bundang Hospital. The overlapping drugs in four medical centers are amlodipine, aspirin, bisoprolol, carvedilol, clopidogrel, clozapine, digoxin, diltiazem, methotrexate, and rosuvastatin. We developed MetaLAB-HOI, an algorithm that can detect ADRs more efficiently using EHR. We compared the detection results of four medical centers, with drug-induced liver injuries as representative ADRs. MetaLAB-HOI's strength lies in fully utilizing the patient's clinical information, such as prescription, procedure, and laboratory results, to detect ADR signals. Considering changes in the patient's condition over time, we created an algorithm based on a scenario that accounted for each drug exposure and onset period supervised by specialists for HOI. We determined that when a template capable of detecting ADR based on clinical evidence is developed and manualized, it can be applied in medical centers for new drugs with insufficient data.

Comparing adverse events of tenecteplase and alteplase: a real-world analysis of the FDA adverse event reporting system (FAERS).

The aim of this study is to monitor, identify, and compare the adverse events (AEs) related to tenecteplase and alteplase, with the objective of exploring the potential safety of tenecteplase for acute ischemic stroke (AIS) and guiding its use to enhance patient safety. In order to evaluate the disproportionality of AEs associated with tenecteplase and alteplase in real-world data, four algorithms (ROR, PRR, BCPNN, EBGM) were utilized as measures to detect signals of AEs related to both drugs. Subsequently, Breslow-Day statistical analysis was applied to compare the RORs of the main system organ classes (SOCs) and key preferred terms (PTs) between tenecteplase and alteplase. A statistical analysis was performed utilizing data gleaned from the Food and Drug Administration Adverse Event Reporting System (FAERS) database, encompassing 19,514,140 case reports from 2004Q1 to 2023Q1. There were 1,004 cases where tenecteplase was reported as the primary suspected (PS) and 2,363 tenecteplase-related adverse drug reactions (ADRs) at the PTs level were identified, the two data of alteplase were 10,945 and 25,266, respectively. The occurrence of drug-induced ADRs was analyzed across 27 organ systems, The analysis revealed several expected ADRs, such as Haemorrhage, Hypersensitivity which were consistent with the two drug-labels. It is of note that the signal strengths of 'death,' 'ventricular fibrillation,' 'cardiogenic shock' and 'pneumonia aspiration' at the PT level were markedly higher for tenecteplase than for alteplase, whereas the signal strength of 'angioedema' at the PT level was significantly higher for alteplase in comparison to tenecteplase. Additionally, unexpected significant ADRs associated with ocular adverse reactions and pneumonia aspiration at the PT level were identified, indicating potential AEs not currently mentioned in the drug instructions. This study identified and compared signals of ADRs associated with tenecteplase and alteplase, although tenecteplase is as effective as alteplase and has advantages such as ease of use and affordability, it cannot replace alteplase in the treatment of AIS until its safety profile is fully recognized. Additionally, previously unreported ocular ADRs and pneumonia were identified, providing valuable insights into the relationship between ADRs and the use of these thrombolytic drugs. These findings underscore the importance of continuous monitoring and effective detection of AEs to ultimately enhance the safety of AIS patients undergoing thrombolytic therapy.

A real-world data analysis of acetylsalicylic acid in FDA Adverse Event Reporting System (FAERS) database

ABSTRACT Background Acetylsalicylic acid (Aspirin), one of the oldest medicines, is widely used in various clinical fields. However, numerous adverse events (AEs) have been reported. In this study, we aimed to investigate adverse drug reactions (ADRs) of aspirin using real-worlddata from the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Methods We assessed the disproportionality of aspirin-related AEs by calculating measures such as reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagationneural network (BCPNN), and Gamma-Poisson Shrinker (GPS). Results Out of 7,510,564 casereports in the FAERS database, 18644 reports of aspirin as the ‘primary suspected (PS)’ AEs were recorded. Disproportionality analyses identified 493 aspirin-related preferred terms (PTs) across 25 organ systems. Notably, unexpected significant AEs such as pallor (p=5.66E–33), dependence (p=6.45E–67), and compartment syndrome (p=1.95E–28) were observed, which were not mentioned in the drug’s instructions. Conclusion Our findings align with clinical observations, highlighting potential new and unexpected ADR signals associated with aspirin. Further prospective clinical studies are necessary to confirm and elucidate the relationship between aspirin and these ADRs. This study offers a fresh and unique perspective for studying drug-AEs.

Toxicities associated with sacituzumab govitecan: Data from clinical trials and real-word pharmacovigilance database.

e24093 Background: This study aimed to analyze the adverse effect of sacituzumab govitecan (SG) with multiple source data, to provide reference for clinical medication safety management. Methods: Clinical trials of SG with available safety data were searched and included in the pooled analysis (up to January 5th, 2023). The adverse drug reaction (ADR) signals of SG were collected from the FDA Adverse Event Reporting System (FAERS) database up to January 1st, 2023. The ADR signals were presented and sorted by incidence frequency and reporting odds ratio (ROR) strength, respectively. We also searched and summarized drug interactions with SG in DDInter database. Results: A total of 6 clinical trials enrolled 1737 patients were included in pooled analysis, the most common adverse events of ≥3 grade were neutropenia (51.55%), leukopenia (14.50%), anemia (10.69%), diarrhea (7.30%), fatigue and asthenia (4.23%). In pharmacovigilance study, 1024 AE reports were extracted, the most common toxicities of SG are hematologic and gastrointestinal. AEs not included in the instructions also showed high signals, such as meningitis, colitis and lymphedema. A total of 40 drugs identified can induce drug-drug interaction when concomitant administration with SG. Conclusions: This study provides a comprehensive profile of SG based on clinical trial, FRAES and DDInter database, attention should be paid not only to the common ADRs, but also to the ADRs not reported in the drug instructions and potential drugs that induce drug-drug interactions. [Table: see text]

Safety profile of clobazam in the real world: an analysis of FAERS database and systematic review of case reports

ABSTRACT Background Most of the safety data of clobazam came from well-designed clinical trials, while the real-world information is insufficient. Research design and methods We performed a disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database through OpenVigil 2 and conducted a systematic review of case reports regarding adverse drug reactions (ADR) linked to clobazam. Results The analysis of FAERS identified 595 ADR signals. Nervous system disorders cantains the most positive signals among all system organ classes (SOCs). Except for seizure (n = 1696) and somnolence (n = 813), drug interactions (n = 492) were the most frequently reported positive signals. A total of 502 unique citations were initially retrieved and 31 individual cases from 28 publications were included. Skin reactions were the most reactions (n = 9), containing three types of severe reactions not alerted in the instruction. Five cases were caused by interactions between clobazam and other antiepileptic drugs, etravirine-based antiretroviral therapy, omeprazole, or meropenem. One patient died of aspiration pneumonia. Conclusions Clinicians must pay attention to severe skin reactions and monitor the signs of suspicious respiratory infections/inflammations and central sedation. Patients with skin reactions will benefit from the withdrawal of clobazam and the treatment with glucocorticoids. The drug reactions between clobazam with severe or moderate cytochrome P450 (CYP) 3A4 or CYP2C19 inhibitors or other antiepileptic drugs should also be alerted.

Abstract 3170: Predicting the signals of adverse drug reactions on the basis of medical data

Abstract Multiple drugs are often prescribed for the better treatment of a disease. While the use of multiple drugs is to improve the overall therapeutic efficacy, it can also lead to unintended adverse drug reactions (ADRs) from drug-drug interactions (DDIs). More than 30% of ADRs have been reported to be caused by DDIs. Because of the importance of DDIs and ADRs, many computational models have been developed to predict DDIs and/or ADRs. However, these computational models mostly make predictions on the basis of molecular features of drugs without considering clinical factors, which consequently does not allow predictions in a person-specific manner. To address this problem, we present a machine learning model that predicts the signals of ADRs potentially caused by DDIs on the basis of medical data. Medical data used for model development come from MIMIC-IV, and include age, ethnicity, gender, patients’ disease information (i.e., ICD-10) and information on drugs administered (e.g., molecular structure and dose). The medical data were subjected to extensive preprocessing based on a domain knowledge, and multiple machine learning methods were examined to predict the ADR signals using the preprocessed dataset. ADR signals are captured by classifying whether each of the selected laboratory measurements (e.g., hematocrit, creatinine, and hemoglobin) is normal. The best-performing model showed the average AUROC of more than 0.8. The machine learning model developed in this study can be useful for examining potential harms when considering multiple drugs, including anticancer drugs. Citation Format: Junhyeok Jeon, Eujin Hong, Hyun Uk Kim. Predicting the signals of adverse drug reactions on the basis of medical data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3170.

A real-world data analysis of topotecan in the FDA Adverse Event Reporting System (FAERS) database

ABSTRACT Objectives The objective of this study was to monitor and identify adverse events (AEs) associated with topotecan, a medication used for the treatment of solid tumors, in order to improve patient safety and guide medication usage. Methods To assess the disproportionality of topotecan-related AEs in real-world data, four algorithms (ROR, PRR, BCPNN, and EBGM) were employed as measures to detect signals of topotecan-associated AEs. Results A statistical analysis was conducted using data from the FAERS database, encompassing 9,511,161 case reports from 2004Q1 to 2021Q4. Among these reports, 1,896 were identified as primary suspected (PS) AEs related to topotecan, and 155 topotecan-related adverse drug reactions (ADRs) at the preferred terms (PTs) level were selected. The occurrence of topotecan-induced ADRs was analyzed across 23 organ systems. The analysis revealed several expected ADRs, such as anemia, nausea, and vomiting, which were consistent with the drug labels. Additionally, unexpected significant ADRs associated with eye disorders at the system organ class (SOC) level were identified, indicating potential adverse effects not currently mentioned in the drug instructions. Conclusion This study identified new and unexpected signals of adverse drug reactions (ADRs) related to topotecan, providing valuable insights into the relationship between ADRs and topotecan usage. The findings highlight the importance of ongoing monitoring and surveillance to detect and manage AEs effectively, ultimately improving patient safety during topotecan treatment.

Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data.

Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. This study aimed to identify methods for the early detection of a wide range of ADR signals. First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. ARM of claims data may be effective in the early detection of a wide range of ADR signals.

Assessment of knowledge, attitude, and practice towards adverse drug reaction reporting among healthcare students of Namakkal District, Tamil Nadu

Background: Adverse drug reactions (ADRs) are recognised causes of increased mortality, morbidity, and high healthcare costs. The contribution of healthcare students to ADR databases is essential and has enabled continued drug detection to such an extent that it has led to identifying unsuspected and rare ADR signals.    Objectives: The study aims to evaluate healthcare students’ knowledge, attitude, and practice (KAP) toward ADR reporting.    Methods: This descriptive, cross-sectional study was conducted among 265 healthcare students of Namakkal district, Tamil Nadu, from various departments. A three-section questionnaire was developed in English and distributed online from July to October 2020. Each participant received a score for each KAP section. Results: Of the 265 healthcare students included in the study, 56.98% were female. The majority were pharmacy students 132 (49.81%), followed by medical 74 (27.92%) and nursing 59 (22.26%) students. There was a significant difference in ADR reporting among healthcare students. KAP of ADR reporting was higher among pharmacy students (88.68%) compared to the medical (19.25%) and nursing students (29.63%), with a p-value of 0.05. Conclusion: This study showed that pharmacy students had more awareness of ADR reporting than other healthcare students due to pharmacovigilance courses in their curriculum and adequate training during clerkships and internships. Hence, it is necessary to include pharmacovigilance and ADR reporting in other healthcare curriculum to reduce ADR underreporting in the future. Periodic educational interventions can improve these parameters of pharmacovigilance.

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature.

Signals of adverse drug reactions (ADRs) can be supported by reports of ADRs and by interventional and non-interventional studies. The evidence base and features of ADR reports that are used to support signals remain to be comprehensively described. To this end, we have undertaken a scoping review. We searched the following databases: PubMed, EMBASE, PsycINFO, Web of Science, and Google Scholar, without language or time restrictions. We also hand searched the bibliographies of relevant studies. We included studies of any design if the results were described as signals. We assessed the levels of evidence using the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria and coded features of reports of ADRs using the Bradford Hill guidelines. Overall, 1974 publications reported 2421 studies of signals; 1683/2421 were clinical assessments of anecdotal reports of ADRs, but only 225 (13%) of these included explicit judgments on which features of the ADR reports were supportive of a signal. These 225 studies yielded 228 signals; these were supported by features, which were: 'experimental evidence' (i.e., positive dechallenge or rechallenge, 154 instances [68%]), 'temporality' (i.e., time to onset, 130 [57%]), 'exclusion of competing causes' (49 [21%]), and others (40 [17%]). Positive dechallenge/rechallenge often co-occurred with temporality (77/228). OCEBM 4 (i.e., case series and case-control studies) was the most frequent level of evidence (2078 studies). Between 2013 and 2019, there was a three-fold increase in clinical assessments of reports of ADRs compared with a less than two-fold increase in studies supported by higher levels of evidence (i.e., OCEBM 1-3). We identified an increased rate between 2013 and 2019 in disproportionality analyses (about 15 studies per year), mostly from academia. Most signals were supported by temporality and dechallenge/rechallenge, but clear reporting of judgments on causality remains infrequent. The number of studies supported only by anecdotal reports of ADRs increased from year to year. The impact of a growing number of signals of disproportionate reporting communicated without an accompanying clinical assessment should be evaluated.

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.

Adverse reactions associated with immune checkpoint inhibitors and bevacizumab: A pharmacovigilance analysis.

Immune checkpoint inhibitors (ICIs) combined with the anti-angiogenesis drug bevacizumab is one of the future directions of immunotherapy. However, the potential adverse drug reactions (ADRs) caused by combination therapy remain unclear. Current research on ADRs of combination therapy in cancer patients is extremely limited. Our study aims to help determine the safety of combination therapy. We downloaded the ADR reports on combination therapy, from the first quarter of 2012 to the fourth quarter of 2021, from the FDA adverse event reporting system (FAERS) database and conducted a large-scale retrospective study. The ADR signals were monitored by reporting odds ratio (ROR) and analyzing the risk of different ADRs in patients with Pan-cancer. A total of 2094 cases were selected, after excluding duplicate data and the use of chemotherapy drugs. We evaluated the risk of ADR in Pan-cancer patients. Combination therapy was an independent risk factor for adverse drug reactions associated with interstitial lung disease (OR: 8.62; 95% CI: 6.14-12.10, P < .0001), hypertension (OR: 1.35; 95% CI: 1.11-1.65, P < .01) and gastrointestinal bleeding (OR: 3.16; 95% CI: 2.21-4.51, P < .0001). A subgroup analysis revealed that the risk of endocrine system-related ADRs was elevated in patients receiving different combination therapies or with certain tumor types. We retrospectively studied the ADR of combination therapy in Pan-cancer patients and analyzed the distribution characteristics of ADR from the perspectives of treatment strategy and cancer types to provide recommendations for the individualized management of patients receiving combination therapy.