Adverse Drug Reaction Information Research Articles

Predicting Drugs Suspected of Causing Adverse Drug Reactions Using Graph Features and Attention Mechanisms.

Adverse drug reactions (ADRs) refer to an unintended harmful reaction that occurs after the administration of a medication for therapeutic purposes, which is unrelated to the intended pharmacological action of the drug. In the United States, ADRs account for 6% of all hospital admissions annually. The cost of ADR-related illnesses in 2016 was estimated at USD 528.4 billion. Increasing the awareness of ADRs is an effective measure to prevent them. Assessing suspected drugs in adverse events helps to enhance the awareness of ADRs. In this study, a suspect drug assisted judgment model (SDAJM) is designed to identify suspected drugs in adverse events. This framework utilizes the graph isomorphism network (GIN) and an attention mechanism to extract features based on patients' demographic information, drug information, and ADR information. By comparing it with other models, the results of various tests show that this model performs well in predicting the suspected drugs in adverse reaction events. ADR signal detection was conducted on a group of cardiovascular system drugs, and case analyses were performed on two classic drugs, Mexiletine and Captopril, as well as on two classic antithyroid drugs. The results indicate that the model can accomplish the task of predicting drug ADRs. Validation using benchmark datasets from ten drug discovery domains shows that the model is applicable to classification tasks on the Tox21 and SIDER datasets. This study applies deep learning methods to construct the SDAJM model for three purposes: (1) identifying drugs suspected to cause adverse drug events (ADEs), (2) predicting the ADRs of drugs, and (3) other drug discovery tasks. The results indicate that this method can offer new directions for research in the field of ADRs.

ADVERSE DRUG REACTIONS OF BACLOFEN, NALTREXONE, AND ACAMPROSATE IN PATIENTS HAVING ALCOHOL DEPENDENCE: A CROSS-SECTIONAL PHARMACOVIGILANCE STUDY

Objectives: The research aims to investigate the prevalence and patterns of adverse drug reactions (ADRs), gauge the severity of these reactions, establish causality in ADR cases, and assess the preventability of such adverse reactions. Methods: ADR information was gathered through personal interviews with patients or their relatives. Causality was assessed using the Naranjo algorithm, and a modified Hartwig and Siegel Severity Assessment Scale was used for estimating the severity of ADR. ADRs were grouped into various preventability categories based on the modified Schumock and Thornton criteria. Results: The total number of patients evaluated in the baclofen, naltrexone, and acamprosate groups was 65, 28, and 42, respectively. The most commonly reported adverse events with baclofen were nausea (31.25%), followed by fatigue (18.75%) and headache (12.50%). The majority of patients receiving acamprosate reported nausea (57.14%), followed by diarrhea (28.57%). Patients receiving naltrexone most commonly reported nausea (35.71%), followed by abdominal pain (21.43%) and headache (14.28%). Conclusion: This study shed light on the prevalence, severity, causality, and preventability of ADRs associated with anti-craving agents used to treat patients with alcohol withdrawal syndrome, providing valuable insights into the safety profiles of these medications.

A Web-Based Tool to Report Adverse Drug Reactions by Community Pharmacists in Australia: Usability Testing Study.

Adverse drug reactions (ADRs) are unintended and harmful events associated with medication use. Despite their significance in postmarketing surveillance, quality improvement, and drug safety research, ADRs are vastly underreported. Enhanced digital-based communication of ADR information to regulators and among care providers could significantly improve patient safety. This paper presents a usability evaluation of the commercially available GuildCare Adverse Event Recording system, a web-based ADR reporting system widely used by community pharmacists (CPs) in Australia. We developed a structured interview protocol encompassing remote observation, think-aloud moderating techniques, and retrospective questioning to gauge the overall user experience, complemented by the System Usability Scale (SUS) assessment. Thematic analysis was used to analyze field notes from the interviews. A total of 7 CPs participated in the study, who perceived the system to have above-average usability (SUS score of 68.57). Nonetheless, the structured approach to usability testing unveiled specific functional and user interpretation issues, such as unnecessary information, lack of system clarity, and redundant data fields-critical insights not captured by the SUS results. Design elements like drop-down menus, free-text entry, checkboxes, and prefilled or auto-populated data fields were perceived as useful for enhancing system navigation and facilitating ADR reporting. The user-centric design of technology solutions, like the one discussed herein, is crucial to meeting CPs' information needs and ensuring effective ADR reporting. Developers should adopt a structured approach to usability testing during the developmental phase to address identified issues comprehensively. Such a methodological approach may promote the adoption of ADR reporting systems by CPs and ultimately enhance patient safety.

Identification of adverse drug reactions that may be related to pharmacogenetics in a public hospital in the South of Brazil

ABSTRACT Background Adverse drug reactions (ADRs) are of great concern in clinical practice. Pharmacogenetics can identify individuals and groups at increased risk of developing ADRs, enabling treatment adjustments to improve outcomes. The study aimed to determine the prevalence of ADRs related to drugs with pharmacogenetic evidence level 1A in a public hospital in Southern Brazil. Research design and methods ADR information was collected from the pharmaceutical registries from 2017 to 2019. Drugs that have pharmacogenetic evidence level 1A were selected. Public genomic databases were used to estimate the genotypes/phenotypes frequency. Results During the period, 585 ADRs were spontaneously notified. Most were moderate (76.3%), whereas severe reactions accounted for 33.8%. Additionally, 109 ADRs caused by 41 drugs presented pharmacogenetic evidence level 1A, representing 18.6% of all notified reactions. Depending on the drug-gene pair, up to 35% of individuals from Southern Brazil could be at risk of developing ADRs. Conclusions Relevant amount of ADRs were related to drugs with pharmacogenetic recommendations on drug labels and/or guidelines. Genetic information could guide and improve clinical outcomes, decreasing ADR incidence and reducing treatment costs.

Research on named entity recognition of adverse drug reactions based on NLP and deep learning.

Introduction: Adverse drug reactions (ADR) are directly related to public health and become the focus of public and media attention. At present, a large number of ADR events have been reported on the Internet, but the mining and utilization of such information resources is insufficient. Named entity recognition (NER) is the basic work of many natural language processing (NLP) tasks, which aims to identify entities with specific meanings from natural language texts. Methods: In order to identify entities from ADR event data resources more effectively, so as to provide valuable health knowledge for people, this paper introduces ALBERT in the input presentation layer on the basis of the classic BiLSTM-CRF model, and proposes a method of ADR named entity recognition based on the ALBERT-BiLSTM-CRF model. The textual information about ADR on the website "Chinese medical information query platform" (https://www.dayi.org.cn) was collected by the crawler and used as research data, and the BIO method was used to label three types of entities: drug name (DRN), drug component (COM), and adverse drug reactions (ADR) to build a corpus. Then, the words were mapped to the word vector by using the ALBERT module to obtain the character level semantic information, the context coding was performed by the BiLSTM module, and the label decoding was using the CRF module to predict the real label. Results: Based on the constructed corpus, experimental comparisons were made with two classical models, namely, BiLSTM-CRF and BERT-BiLSTM-CRF. The experimental results show that the F 1 of our method is 91.19% on the whole, which is 1.5% and 1.37% higher than the other two models respectively, and the performance of recognition of three types of entities is significantly improved, which proves the superiority of this method. Discussion: The method proposed can be used effectively in NER from ADR information on the Internet, which provides a basis for the extraction of drug-related entity relationships and the construction of knowledge graph, thus playing a role in practical health systems such as intelligent diagnosis, risk reasoning and automatic question answering.

Jordanians' knowledge, attitude and practice regarding adverse drug reactions reporting

ObjectivesThe purpose of the current study was to evaluate the general public knowledge, attitudes, and practice regarding Adverse Drug Reactions (ADRs) reporting and pharmacovigilance in Jordan. MethodsA cross‐sectional study was conducted between July 16, 2022, and July 30, 2022, in Jordan. During the study period, an electronic survey consisting of 4 sections was administered to a convenience sample of Jordanians (aged 18 or above) using 2 social media platforms (Facebook and WhatsApp). Logistic regression analysis was used to screen the predictors of ADRs reporting by the participants. ResultsA total of 441 participants completed the survey. The majority of the participants (67.6%) were females, 53.1% between 26 and 45 years old. Almost all participants (96.3%) were always aware of the indication of the medications they take, the time and frequency (87.8%), and the duration of medications (84.4%). Nearly one-third of the participants (37.4%) asked about their medications' ADRs. However, the drug information leaflet was the most frequently used source of ADR information (33.3%). The majority of responders believed that both healthcare providers and consumers should report ADRs (93.4% and 80.3%, respectively). Only one-quarter of respondents (27.2%) believed that consumers could directly report ADRs through the Jordan pharmacovigilance program. The majority of patients who had experienced ADRs (70.3%) were aware that ADRs should be reported, and among them, 91.9% had reported the ADRs to healthcare providers. Furthermore, few participants (8.1%) reported it to the Jordan National Pharmacovigilance Centre (JNCP). Linear regression revealed that none of the demographic characteristics (age, gender, education, job, and social status) were affecting public reporting practice of the ADRs (P > 0.05 for all). ConclusionRespondents showed fair knowledge about adverse drug reactions and their reporting. However, there is a need to initiate educational activities and intervention programs to raise awareness about the JNPC, which will have a positive impact on public health and ensure safe medication use in Jordan.

Adverse Drug Reaction Detection From Social Media Based on Quantum Bi-LSTM With Attention

Drug combination is very common in the course of disease treatment. However, it inevitably increases the overall risk of adverse drug reactions (ADRs). It is very important to early and accurately detect and identify the potential ADRs for combined medication safety and public health. Social media is an important pharmacovigilance data source for ADR detection. But the data are complex, mass, clutter, highly sparse, so it is difficult to detect the ADR information from these data. Deep learning stands out in terms of increased accuracy. However, it takes a lot of training time and requires a lot of computing power. Quantum computing has strong parallel computing capability, and requires less computing power. By introducing attention mechanism and quantum computing into Bi-directional Long Short-Term Memory (Bi-LSTM), a quantum Bi-LSTM with attention (QBi-LSTMA) model is constructed for ADR detection from social media big data. QBi-LSTMA is composed of 6 variable component subcircuits (VQC) stacked. Under the condition that the main topology of Bi-LSTM remains unchanged, the biases of QBi-LSTMA in input gate, forgetting gate, candidate memory unit and output gate are removed to simplify the network structure, and the weight and active value qubits of the model are used to update the network weight. The performance of the proposed method is evaluated on the SMM4H dataset, comparing with one traditional ADR detection method and three deep learning based ADR detection approaches. The experiment results show that the proposed method has great potential in ADR detection.

Disease-specific ADRs of TNF-α inhibitors as reported by patients with inflammatory rheumatic diseases: a registry-based prospective multicenter cohort study

ABSTRACT Background The extent to which adverse drug reactions (ADRs) of biologics differ per immune-mediated inflammatory disease (IMID), and the relevance of tailoring ADR information per IMID is not fully investigated. We aimed to compare patient-reported ADRs attributed to adalimumab and etanercept between different inflammatory rheumatic diseases (IRDs). Research design and methods ADR reports from IRD patients were extracted from the Dutch Biologic Monitor. ADR frequencies were compared using Fischer–Freeman–Halton exact test and the influence of covariates was assessed using binomial logistic regression. Results A total, of 729 participants were included, of which 354 participants reported 887 unique ADRs. ADR frequencies were not significantly different between the IRDs. Rheumatoid arthritis and ankylosing spondylitis including axial spondyloarthritis patients had an increased risk of ADRs related to ‘Respiratory, thoracic and mediastinal disorders’ and as compared to psoriatic arthritis patients. Etanercept use, combination therapy with methotrexate and/or corticosteroids, and age also influenced the risk of reporting specific ADRs. Conclusions There were no differences in frequencies and nature of patient-reported ADRs attributed to adalimumab and etanercept between different IRDs. However, more research is needed to align patients’ and health-care professionals’ perspectives to improve knowledge on disease-specific ADRs.

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors.

Objective Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions (nu = 0.5649 × e(− (case/6984)), gamma = 9.005 × 10−4 × case − 4.877 × 10−8 × case2). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.

An Adverse Drug Reaction Database for Clinical Use - Potential of and Difficulties with the Summary of Product Characteristics.

Adverse drug reactions (ADRs) for all drugs in Europe are described in the legally approved Summary of Product Characteristics (SmPC). An overview of all ADRs of the patients' drug list can support healthcare staff to link patient symptoms to possible ADRs. We review the possibilities and challenges to extract ADR information from SmPCs and present the development of our semi-automated procedure for extraction of ADRs from the tabulated section of the SmPCs to create a database, named Bikt, which is regularly updated and used at point of care in Sweden. The existence of five major table formats for ADRs used in the SmPCs required the development of different parsing scripts. Manual checks for correctness for all content has to be performed. The quality of extraction was investigated for all SmPCs by measuring precision, recall and F1 scores (i.e. the weighted harmonic mean of precision and recall) and compared with other methods published. We conclude that it is possible to semi-automatically extract ADR information from SmPCs. However, clear technical and content guidelines and standards for ADR tables and terms from drug registration authorities would lead to improved extraction and usability of ADR information at point of care.

Evaluation of adverse drug reaction formatting in drug information mobile phone applications.

Objective:To evaluate the differences in presentation (formatting) of adverse drug reaction (ADR) information within drug monographs in commonly used drug information (DI) mobile device applications.Methods:A cross-sectional analysis of ADR formatting of twenty commonly prescribed oral medications within seven DI mobile applications was performed. Databases were assessed for ADR information, including presence of placebo comparisons, severity of ADR, onset of ADR, formatting of ADRs in percentile (quantitative) format or qualitative format, whether references were used to cite information, and whether ADRs are grouped by organ system. Data was collected by two study investigators and discrepancies were resolved via consensus.Results:The seven DI mobile applications varied significantly on every analyzed ADR variable with the exception of ADR onset, which was absent in all databases. Significant differences were found for variables known to impact clinical judgment such as placebo comparisons and qualitative versus quantitative formatting. Placebo comparisons were most common among monographs in Lexicomp (30%) but were absent among monographs within other applications. Quantitative information was commonly used in most databases but was absent in Epocrates. Qualitative formatting was present in all Epocrates and Micromedex applications but absent in the majority of other applications. Substantial variations were also found in severity and grouping information.Conclusion:Substantial variation in ADR formatting exists among the most common DI mobile applications. These differences may translate into alternative interpretations of medical information and thus impact clinical judgment. Health care librarians and clinicians should consider ADR formatting when choosing between DI applications.

Assessment of Information Sharing on Adverse Drug Reactions by Community Pharmacies with Other Medical Institutions.

Widespread coordination and sharing of information regarding adverse drug reactions (ADRs) are important for drug safety assessment. However, the actual status of coordination and sharing of information on ADRs in community pharmacies remains unclear. Therefore, a survey was conducted at community pharmacies to analyze the status. In this cross-sectional study conducted from 31 March 2021 to 9 April 2021, a request letter with the uniform resource locator of the questionnaire form was sent to 302 community pharmacies affiliated with Tsuruha Holdings Inc., and the responses were obtained online. The response rate for the questionnaires was 80.8% (n = 244). In total, 20.9% of the community pharmacies provided information on patients’ ADRs to hospitals or clinics prescribing drugs. None of the community pharmacies provided patient ADR information to other community pharmacies. Of the community pharmacies, 98.8% felt that insufficient information was available to monitor ADRs from hospitals or clinics prescribing drugs. For example, the name of the disease (67.6%), considered to be the most common information, was insufficiently provided. Overall, the existing system for providing information on ADRs between community pharmacies and other medical institutions is insufficient and needs to be developed further.

A Text Mining Protocol for Extracting Drug-Drug Interaction and Adverse Drug Reactions Specific to Patient Population, Pharmacokinetics, Pharmacodynamics, and Disease.

Drug-drug interactions (DDIs) and adverse drug reactions (ADR) are experienced by many patients, especially by elderly population due to their multiple comorbidities and polypharmacy. Databases such as PubMed contain hundreds of abstracts with DDI and ADR information. PubMed is being updated every day with thousands of abstracts. Therefore, manually retrieving the data and extracting the relevant information is tedious task. Hence, automated text mining approaches are required to retrieve DDI and ADR information from PubMed. Recently we developed a hybrid approach for predicting DDI and ADR information from PubMed. There are many other existing approaches for retrieving DDI and ADR information from PubMed. However, none of the approaches are meant for retrieving DDI and ADR specific to patient population, gender, pharmacokinetics, and pharmacodynamics. Here, we present a text mining protocol which is based on our recent work for retrieving DDI and ADR information specific to patient population, gender, pharmacokinetics, and pharmacodynamics from PubMed.

Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding-Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model.

BackgroundWith the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance.ObjectiveThis study describes how to identify ADR-related information from Chinese ADE reports.MethodsOur study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method.ResultsThe NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity.ConclusionsThe proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation.

POS0621 DISEASE-SPECIFIC ADVERSE DRUG REACTION PROFILES OF ADALIMUMAB AND ETANERCEPT AS REPORTED BY IMMUNE-MEDIATED INFLAMMATORY DISEASE PATIENTS

Background: Information on adverse drug reactions (ADRs) is generally clustered for all indications of a drug in the patient information leaflet. However, previous research has shown that participants of the Dutch Biologic Monitor (DBM) that use a biologic for their immune-mediated inflammatory disease (IMID) prefer to receive ADR information tailored to their own biologic an IMID (1). Currently, it is unclear whether the ADR profile of a specific biologic may differ between patients with different IMIDs, which would be vital information for health care providers (HCPs) in their patient guidance. Objectives: To determine whether the profiles of ADRs attributed to adalimumab (ADA) and etanercept (ETN) reported by patients in the DBM differ between IMIDs. Methods: The DBM is a prospective cohort event monitoring system for patient-reported ADRs attributed to biologics (2). Study data was extracted from the DBM for the period Jan 2017 – Oct 2020. ADRs were coded according to their corresponding Preferred Term (PT) following MedDRA terminology. Unique PTs were selected per participant and grouped under System Organ Classes (SOCs) (Figure 1) for ADA and ETN. SOCs contributing for Differences in ADR profiles between IMIDs were tested using the Fisher-Freeman-Halton’s Exact Test with Monte Carlo simulation. SOCs of interest were separately tested with the Fisher-Freeman-Halton’s Exact Test (no simulation) and subsequently corrected for multiple comparisons using the Benjamini-Hochberg (BH) correction. Results: A total of 572 ADR reports from 218 participants using ADA and 450 ADR reports from 185 participants using ETN were analyzed (Table 1). Overall, a statistically significant difference in patient-reported ADR profile between the assessed indications was found for ADA (p=0.011), but not for ETN (p=0.057). The following separate tests for selected SOCs of interest showed a significant difference in the frequencies of ‘respiratory, thoracic and mediastinal disorders’ and ‘musculoskeletal and connective tissue disorders’ between the different IMIDs for ADA after BH correction, but none for ETN. Conclusion: Although only ADA shows a statistically significant difference in ADR profile between different IMIDs, more research with a larger sample size might show similar results for ETN. Furthermore, explanations for the differences found, such as disease-drug interactions, must be examined. This would help HCPs in providing disease-specific information and patient guidance.

Qualitative Study of General Public Views towards Adverse Drug Reactions in Lithuania.

Direct patient reporting of adverse drug reactions (ADRs) is available in many countries, as patients are often knowledgeable about their health conditions and medicines. This study aimed to assess whether patients can recognize ADRs and whether they know how to proceed with ADR reporting. The study also assesses ADR information sources and the main barriers to reporting. Through the purposive and snowball sampling techniques, 42 consumers participated in focus group discussions. All discussions were audio recorded, transcribed verbatim, and analyzed for thematic content analysis. The thematic content analysis yielded four major themes: knowledge about medication safety, ADRs, and pharmacovigilance; information sources about medicines and ADRs; knowledge about ADR reporting; attitudes towards ADR reporting; benefits of ADR reporting; barriers to ADR reporting. Participants were able to identify ADRs and used different information sources about medicines and ADRs to confirm their beliefs. However, the poor communication between consumers, pharmacists, and physicians is the main barrier to ADR reporting. This study identified the challenges in relation to pharmacovigilance in Lithuania from patients’ perspectives. Our study indicated a lack of clearly set standards and communication guidelines between patients, physicians, and pharmacists. Active pharmacovigilance might help develop consumer habits regarding the reporting of ADRs in the presence of spontaneous pharmacovigilance.

Customization of information on adverse drug reactions according to patients’ needs – A qualitative study

ObjectivesInterviewing patients, we evaluated how information on adverse drug reactions (ADRs) could be customized to best meet the patients’ expectations. MethodsRecruitment followed an approach of variation sampling. Data collection was paper-based (questionnaires) and through semi-structured interviews. Participants were asked to evaluate different ADR characteristics (e.g. frequency, seriousness, lay perceptibility, and management strategies) as suitable filters to customize information and subsequently how they would apply these to meet their information wishes. Their wishes were then verified by presenting them accordingly customized information. ResultsForty-one participants (mean age 44.9 ± 20.1 years) were recruited. Overall, information needs were highly diverse. Therefore, it was not possible to identify one single characteristic that was generally considered suitable. Initially, participants often wished for a maximum of information (e.g. ‘all’ ADRs). The actual presentation of comprehensive information often surprised the recipients and consequently changed the desired information. ConclusionsBy simply supplying patients with ADR information they request, it is not possible to guarantee their satisfaction with such information and their understanding of it; initial wishes might be uttered without actually comprehending their practical meaning. Practice ImplicationsIt is crucial to precisely assess, question, and verify information wishes in order to customize ADR information successfully.

Benefits of Patient/Caregiver Engagement in Adverse Drug Reaction Reporting Compared With Other Sources of Reporting in the Inpatient Setting: A Systematic Review.

Clinicians learn from prior adverse events through pharmacovigilance allowing for improved medication safety in the medication use process; therefore, adverse drug reaction (ADR) reporting needs to be maximized. This systematic review was conducted to determine whether engaging patients/caregivers in ADR reporting during a patient's hospitalization provides further information about ADRs not obtained from traditional sources of reporting (i.e., voluntary reporting, medical record review). This review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. A literature search was conducted from January 2018 to June 2019 in PubMed, CINAHL, and Embase. Studies were included if they were (i) conducted in the inpatient setting, (ii) surveyed patients/caregivers, (iii) compared patient/caregiver reporting with another source of reporting, and (iv) evaluated ADRs. Studies completed in an outpatient setting or nursing home were excluded. A total of 11 studies were included. Sources of ADR information from patient/caregiver were obtained through interviews, surveys, questionnaires, or open-ended responses. Patient reporting was compared with medical record reports (7 articles) and health care professional reporting (4 articles). Approximately 11% to 35% of ADRs reported from patients were not identified through voluntary reporting by health care professionals, and 5.6% to 66% of ADRs obtained from patient reporting were not provided in the medical record. Patients/caregivers are important sources of safety information to improve system and practice of medication use that may not be recorded by other surveillance methods. Administrators and clinicians need to determine the best approach to integrate patients/caregivers into routine reporting for optimal engagement.

Evaluation of adverse drug reaction formatting in drug information databases.

Objective:The research evaluated the differences in formatting of adverse drug reaction (ADR) information in drug monographs in commonly used drug information (DI) databases.Methods:A cross-sectional analysis of formatting of ADR information for twenty commonly prescribed oral medications in seven commonly used DI databases was performed. Databases were assessed for presentation of ADR information, including presence of placebo comparisons, severity of ADR, onset of ADR, formatting of ADRs in percentile (quantitative) format or qualitative format, whether references were used to cite information, whether ADRs are grouped by organ system, and word count of the ADR section. Data were collected by two study investigators and discrepancies were resolved via consensus. Chi-square analyses and one-way analysis of variance (ANOVA) were used to evaluate for mean group differences in categorical and continuous data, respectively.Results:The seven DI databases varied significantly on each analyzed ADR variable, including variables known to impact interpretation such as placebo comparisons and qualitative versus quantitative formatting. Placebo comparisons were most common among monographs in Micromedex In-Depth Answers (70%) but were absent among monographs in Epocrates, Lexicomp, and Micromedex. Quantitative information was commonly used in most databases but was absent in Epocrates. Average word counts were higher in Clinical Pharmacology and Micromedex In-Depth answers compared to other databases.Conclusion:Substantial variation in ADR formatting exists between the most common DI databases. These differences may translate into alternative interpretations of medical information and, thus, impact clinical judgment. Further studies are needed to assess whether these differences impact clinical practice.

Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study.

BackgroundLicensed drugs may cause unexpected adverse reactions in patients, resulting in morbidity, risk of mortality, therapy disruptions, and prolonged hospital stays. Officially approved drug package inserts list the adverse reactions identified from randomized controlled clinical trials with high evidence levels and worldwide postmarketing surveillance. Formal representation of the adverse drug reaction (ADR) enclosed in semistructured package inserts will enable deep recognition of side effects and rational drug use, substantially reduce morbidity, and decrease societal costs.ObjectiveThis paper aims to present an ontological organization of traceable ADR information extracted from licensed package inserts. In addition, it will provide machine-understandable knowledge for bioinformatics analysis, semantic retrieval, and intelligent clinical applications.MethodsBased on the essential content of package inserts, a generic ADR ontology model is proposed from two dimensions (and nine subdimensions), covering the ADR information and medication instructions. This is followed by a customized natural language processing method programmed with Python to retrieve the relevant information enclosed in package inserts. After the biocuration and identification of retrieved data from the package insert, an ADR ontology is automatically built for further bioinformatic analysis.ResultsWe collected 165 package inserts of quinolone drugs from the National Medical Products Administration and other drug databases in China, and built a specialized ADR ontology containing 2879 classes and 15,711 semantic relations. For each quinolone drug, the reported ADR information and medication instructions have been logically represented and formally organized in an ADR ontology. To demonstrate its usage, the source data were further bioinformatically analyzed. For example, the number of drug-ADR triples and major ADRs associated with each active ingredient were recorded. The 10 ADRs most frequently observed among quinolones were identified and categorized based on the 18 categories defined in the proposal. The occurrence frequency, severity, and ADR mitigation method explicitly stated in package inserts were also analyzed, as well as the top 5 specific populations with contraindications for quinolone drugs.ConclusionsOntological representation and organization using officially approved information from drug package inserts enables the identification and bioinformatic analysis of adverse reactions caused by a specific drug with regard to predefined ADR ontology classes and semantic relations. The resulting ontology-based ADR knowledge source classifies drug-specific adverse reactions, and supports a better understanding of ADRs and safer prescription of medications.