Pharmacovigilance Experts Research Articles

Inter-rater agreement between WHO- Uppsala Monitoring Centre system and Naranjo algorithm for causality assessment of adverse drug reactions

Determining the causality of Adverse Drug Reactions (ADRs) is essential for management and prevention of future occurrences. The WHO-Uppsala Monitoring Centre (UMC) system is recommended under the Pharmacovigilance Program of India whereas Naranjo's algorithm is commonly utilized by clinicians, but their agreement remains a subject of investigation. This study aims to compare the inter-rater agreement between these two scales for causality assessment of ADRs. In this cross-sectional study, two groups of pharmacovigilance experts were given a set of total 399 anonymized individual case safety reports, collected over six months. The raters were blinded to each other's assessments and applied the WHO-UMC system and Naranjo algorithm to each case independently. Inter-rater agreement was then evaluated utilizing Cohen's kappa. The suspected ADRs were also comprehensively analysed on parameters like age, sex, route of administration, speciality, organ system affected, most common drug categories and individual drugs, outcome of ADRs. Analysis of 399 suspected ADRs revealed that mean age of patients was 36.8 ± 18.0 years, females were more frequently affected, highest proportion of reports were from psychiatry inpatients, seen with antipsychotic drugs, involved the central nervous system, with oral administration, and 91% resolved. On causality assessment by the WHO-UMC system, 53.3% were “Certain” whereas Naranjo's algorithm categorized 96.74% of ADRs as “Probable”. Cohen's kappa showed a “Minimal” agreement (0.22) between WHO-UMC and Naranjo system of causality assessment. The considerable lack of agreement between the two commonly employed systems of causality assessment of ADRs warrants further investigation into specific factors influencing the disagreement to improve the accuracy of causality assessments.

The Increasing Demand for Skilled Professionals in the Field of Pharmacovigilance

ABSTRACT The pharmaceutical sector is experiencing rapid advancements in drug discovery, strict regulations, and a commitment to patient safety. This has led to an increased demand for skilled pharmacovigilance experts who can ensure the safety and effectiveness of medicinal products. Several factors contribute to this demand. The expanding pharmaceutical landscape leads to the need for thorough investigation of the safety profile of new medications and treatments. The increasing volume of pharmaceutical products also requires close monitoring for safety and efficacy. Strict regulatory requirements from global agencies further emphasize the need for a robust pharmacovigilance system. The industry’s focus on patient safety drives the demand for professionals who can effectively manage and analyze safety data. The rise of biologics and biosimilars presents additional challenges in monitoring and assessing safety profiles, creating a need for specialized expertise. Furthermore, the globalization of clinical trials and technological advancements, such as artificial intelligence (AI) and machine learning (ML), require experts who can integrate these tools into pharmacovigilance methods. There are certain skill sets that are crucial for pharmacovigilance professionals. These include regulatory knowledge, data analysis and interpretation, case processing, signal detection and risk management, medical and scientific expertise, communication skills, and technological proficiency. There are various career opportunities in pharmacovigilance, including pharmacovigilance officer, pharmacovigilance scientist, medical reviewer, pharmacovigilance team lead/manager, regulatory affairs specialist, and consultant and trainer. Future trends in the field include the convergence of AI and ML, the use of real-world evidence for postmarketing surveillance and drug safety monitoring, collaboration, and outsourcing for PV projects, and the need to tailor pharmacovigilance approaches to personalized medicine. In conclusion, the pharmaceutical industry’s evolution and commitment to patient safety have created a growing need for skilled professionals in pharmacovigilance. Investments in the development and education of these professionals are essential for the advancement of drug safety and public health.

Validation of a Novel Method to Assess the Clinical Quality of Information in Pregnancy-Related Pharmacovigilance Case Reports: A ConcePTION Project.

To assess the causal relationship between a medicinal product and a reported event, relevant information needs to be present. Information elements for assessing cases of exposure to medicinal products during pregnancy were predefined and used in a new tool to assess the quality of information. However, the extent in which the presence or absence of these predefined information elements is associated with the overall clinical quality of these cases, as evaluated by pharmacovigilance experts, remains uncertain. We aimed to validate a novel method to assess the clinical quality of information in real-world pregnancy pharmacovigilance case reports. The clinical quality of case reports regarding medicinal product exposure and pregnancy-related outcomes was appraised from spontaneous reports, literature, Teratology Information Services (UK and Switzerland), The Dutch Pregnancy Drug Register, the Gilenya pregnancy registry and the Enhanced PV programme of Novartis. Assessment was done by means of the novel standardised tool based on the presence and relevance of information, and by expert judgement. The novel tool was validated compared to the expert assessment as the gold standard expressed as the area under the receiver operating characteristic curves, after which the sensitivity and specificity were calculated using cross-tabulations. Inter-rater variability was determined by means of weighted Cohen's kappa. One hundred and eighty-six case reports were included. The clinical quality score as assessed by the novel method was divided into three categories with cut-off values of 45% (poor to intermediate) and 65% (intermediate to excellent). Sensitivity was 0.93 and 0.96 for poor to intermediate and intermediate to excellent, respectively. Specificity was respectively 0.52 and 0.73. Inter-rater variability was 0.65 (95% confidence interval 0.53-0.78) for the newly developed approach, and 0.40 (95% confidence interval 0.28-0.52) for the gold standard assessment. The tool described in this study using the presence and relevance of elements of information is the first designed, validated and standardised method for the assessment of the quality of information of case reports in pregnancy pharmacovigilance data. This method confers less inter-rater variability compared with a quality assessment by experts of pregnancy-related pharmacovigilance data.

Finding Needles in the Haystack: Clinical Utility Score for Prioritisation (CUSP), an Automated Approach for Identifying Spontaneous Reports with the Highest Clinical Utility.

Spontaneous reporting of adverse events has increased steadily over the past decades, and although this trend has contributed to improving post-marketing surveillance pharmacovigilance activities, the consequent amount of data generated is challenging to manually review during assessment, with each individual report requiring review by pharmacovigilance experts. This highlights a clear need for alternative or complementary methodologies to help prioritise review. Here, we aimed to develop and test an automated methodology, the Clinical Utility Score for Prioritisation (CUSP), to assist pharmacovigilance experts in prioritising clinical assessment of safety data to improve the rapidity of case series review when case volumes are large. The CUSP method was tested on a reference dataset of individual case safety reports (ICSRs) associated to five drug-event pairs that led to labelling changes. The selected drug-event pairs were of varying characteristics across the portfolio of GSK's products. The mean CUSP score for 'key cases' and 'cases of low utility' was 19.7 (median: 21; range: 7-27) and 17.3 (median: 19; range: 4-27), respectively. CUSP distribution for 'key cases' were skewed toward the higher range of scores compared with 'all cases'. The overall performance across each individual drug-event pair varied considerably, showing higher predictive power for 'key cases' for three of the drug-event pairs (average CUSP between these three: 22.8; range: 22.5-23.0) and lesser power for the remaining two (average CUSP between these two: 17.6; range: 14.5-20.7). Although several tools have been developed to assess ICSR completeness and regulatory utility, this is the first attempt to successfully develop an automated clinical utility scoring system that can support the prioritisation of ICSRs for clinical review.

Current career situations of Chinese pharmacovigilance professionals working for pharmaceutical companies: an exploratory survey

BackgroundPharmacovigilance in China has experienced rapid development in the past 30 years. The implementation of Good Pharmacovigilance Practice in China since the end of 2021 heralds a new era of pharmacovigilance affairs, which puts forward higher requirements for the quantity and quality of pharmacovigilance personnel. This study aimed to preliminarily explore the current career situations of pharmacovigilance professionals working in China for pharmaceutical companies.MethodsA questionnaire was adapted from research in the USA and Europe with the help of several pharmacovigilance experts. Snowball sampling was used to conduct an exploratory survey to obtain the frequency of basic demographic information, work status, and career expectations of pharmacovigilance professionals working for pharmaceutical companies.ResultsThe personnel engaged in pharmacovigilance work for pharmaceutical companies were mainly medical or pharmaceutical undergraduates within 3 years of graduation. Their work intensity and pressure were relatively high. The training provided by their universities and enterprises could not well meet their needs to improve their job competence. Although they were optimistic about pharmacovigilance and will not change their career, most of them were planning to change their employers.ConclusionThere was a gap between the demand and supply of pharmacovigilance personnel. Relevant regulatory authorities and industry associations should guide higher education institutions to collaborate with pharmacovigilance specialists to strengthen pharmacovigilance education for medical or pharmaceutical students, on the basis of which pharmacovigilance certification courses and continuing education courses can be developed. Meanwhile, pharmaceutical enterprises should consider reasonably adjusting work intensity and income to avoid a high turnover rate.

Disproportionality analysis of quinolone safety in children using data from the FDA adverse event reporting system (FAERS).

Quinolones are widely prescribed for the treatment or prevention of infectious diseases in children. To gain further insight into quinolone-associated adverse event (AE) in children and better protect pediatric patients, continued surveillance of safety data is essential. The purpose of this study was to characterize the safety profiles of quinolone-associated AEs in children by mining the FDA adverse event reporting system (FAERS). FAERS reports from quarter 1 of 2004 to quarter 1 of 2022 were included in the study. The Medical Dictionary for Regulatory Activities (MedDRA) was used to identify adverse events. Reporting odds ratios (ROR) corresponding 95% confidence intervals (CIs) and information component (IC) along with 95% CIs were calculated to detect drug-AE pairs with higher-than-expected reporting rates within the FAERS from System Organ Classes (SOCs) to Preferred Terms (PTs). Reports were considered as signals if the 95% confidence interval did not contain the null value. After inclusion criteria were applied, a total of 4,704 reports associated with quinolones were considered. Most FAERS reports associated with ciprofloxacin (N = 2,706) followed by levofloxacin (N = 1,191), moxifloxacin (N = 375), oflaxacin (N = 245) and ozenoxacin (N = 187). The most common age group was 12-18 years. The median weight was 39.0 kilogram. The adverse effects of quinolones emerging for SOCs primarily included Infections and infestations, gastrointestinal symptoms, blood and lymphatic system disorders, cardiac disorders, nervous system disorders, musculoskeletal and connective tissue disorders and psychiatric disorders. The most frequently AE signals at the PT level were pyrexia (N = 236), febrile neutropenia (N = 120), off label use (N = 48), drug resistance (N = 18) and cardiac arrest (N = 22) following the use of ciprofloxacin, levofloxacin, moxifloxacin, ofloxacin, and ozenoxacin, respectively. Serious oznoxacin-associated AE signals were found and have not been documented in the package insert. They included cardiac arrest (N = 22; ROR = 19.83; IC = 3.68), overdose (N = 21; ROR = 4.98; IC = 2.07), seizure (N = 16; ROR = 6.01; IC = 2.29), small for dates baby (N = 9; ROR = 14.7; IC = 3.05), completed suicide (N = 15, ROR = 18.87; IC = 3.51), asthma (N = 9; ROR = 6.69; IC = 2.24;) and hypotension (N = 9; ROR = 3.83; IC = 1.68). This study provided additional evidence with respect to quinolones-related AEs for children. Generally, the findings of this study are compatible with AEs recorded in package inserts. The unexpected signals of ozenoxacin justify active vigilance by clinicians and timely monitoring by pharmacovigilance experts.

Immune Checkpoint Inhibitors and Pregnancy: Analysis of the VigiBase® Spontaneous Reporting System.

In pregnancy, immune checkpoint pathways are involved in the maintenance of fetomaternal immune tolerance. Preclinical studies have shown that immune checkpoint inhibitors (ICIs) increase the risk of fetal death. Despite the fact that using ICIs in pregnant women and women of childbearing potential is not recommended, some case reports of ICI exposure in pregnancy have been published showing favorable fetal outcomes. This study aimed to gain further insight into ICI safety in pregnancy by querying VigiBase®, the World Health Organization's spontaneous reporting system. We performed raw and subgroup disproportionality analyses using the reporting odds ratio and comparing ICIs with the entire database, other antineoplastic agents, and other antineoplastic agents gathered in VigiBase® since 2011. Across 103 safety reports referring to ICI exposure during the peri-pregnancy period, 56 reported pregnancy-related outcomes, of which 46 were without concomitant drugs as potential confounding factors. No signals of disproportionate reporting were found for spontaneous abortion, fetal growth restriction, and prematurity. In light of the expanding indications of ICIs, continuous surveillance by clinicians and pharmacovigilance experts is warranted, along with pharmacoepidemiological studies on other sources of real-world evidence, such as birth records, to precisely assess ICI exposure during the peri-pregnancy period and further characterize relevant outcomes.

Lost in Translation: A Multilingual Survey of Interlinguistic Variations in Terms Used in Pharmacovigilance.

Although English is the universal language of science, it is nevertheless the first language of only about 6% of the world's population, and 75% of people do not speak English at all. It is therefore important that accurate translations of scientific information should be available, not only for professionals but also for the general public. This applies to pharmacovigilance as much as to any other discipline. The aim of this study was to determine how pharmacovigilance terms are translated into other languages, in order to judge the extent to which differences between languages might impair communication in pharmacovigilance. I surveyed the translation of 26 pharmacovigilance terms into 26 languages via a panel of 83 pharmacovigilance experts. Three types of terms emerged: Type 1-those that are similar in form across all, or almost all, of the languages surveyed (e.g. 'signal' and 'risk'); Type 2-terms that are similar in form across some languages but not all (e.g. 'pharmacovigilance' and 'surveillance'); Type 3-terms for which there are major differences across languages (e.g. 'hazard'). Misconceptions in the communication of pharmacovigilance information may arise through difficulties in translation. Metaphorical expressions are best avoided in serious scientific publications, in order to reduce the difficulties of translation. A multilingual glossary of terms and definitions, which could be used to program a dedicated machine translator, would be of value. Published guidelines offer guidance to methods of translation, but they are complex and time-consuming and are mostly used in translating instruments for eliciting patient-reported outcomes.

A Qualitative Study of Stakeholders’ Views on Pharmacovigilance System, Policy, and Coordination in Pakistan

Objectives: Due to the absence of necessary rules, poor coordination, and various challenges, the pharmacovigilance system of Pakistan is not optimally functional at all levels of the health system. The objective of the study was to assess the stakeholders’ perceptions of the current ADR reporting system and to identify the pharmacovigilance policy issues and problems of effective coordination. Methodology: Stakeholders from a broad range of disciplines, academia, regulatory authorities, the pharmaceutical industry, international health organizations, as well as pharmacovigilance experts, and healthcare professionals were included in the study. A total of 25 stakeholders throughout Pakistan were interviewed during exploratory semi-structured interviews. The interviews were recorded digitally, transcribed, coded, compared, and grouped according to their similarity of themes. Participants provided insights into gaps, limitations, and challenges of Pakistan’s current ADR reporting system, issues with proposed pharmacovigilance rules, and coordination difficulties. Results: The majority of the participants considered the ADR reporting system in Pakistan to be improving but in a nascent phase. The identified gaps, challenges, limitations of the system, and barriers to reporting were labeled as reasons for limited functioning. Almost all stakeholders were aware of the existence of draft pharmacovigilance rules; however, participants in the industry were familiar with the contents and context of draft pharmacovigilance rules. Bureaucratic red tape and lack of political will appeared to be the top reasons for delaying the approval of the pharmacovigilance rules. Wider consultation, advocacy, and awareness sessions of policymakers and HCPs were suggested for early approval of rules. Participants unanimously agreed that the approval of rules shall improve the quality of life and reduce the economic burden along with morbidity and mortality rates. The need for greater and collaborative coordination among the stakeholders in promoting medicines’ safety was highlighted. All participants suggested the use of media and celebrities to disseminate the safety information. Conclusion: Participants showed partial satisfaction with the way pharmacovigilance in Pakistan is moving forward. However, stakeholders believed that engagement of multi-stakeholders, approval of pharmacovigilance rules, and the establishment of pharmacovigilance centers in provinces, hospitals, and public health programs (PHPs) shall support in achieving the desired results.

Artificial Intelligence in Pharmacovigilance: An Introduction to Terms, Concepts, Applications, and Limitations.

The tools of artificial intelligence (AI) have enormous potential to enhance activities in pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should know enough about AI to explore the possibilities of collaboration with those who are. Modern concepts of AI date from Alan Turing's work, especially his paper on "the imitation game", in the late 1940s and early 1950s. Its scope today includes computational skills, including the formulation of mathematical proofs; visual perception, including facial recognition and virtual reality; decision making by expert systems; aspects of language, such as language processing, speech recognition, creative composition, and translation; and combinations of these, e.g. in self-driving vehicles. Machines can be programmed with the ability to learn, using neural networks that mimic cognitive actions of the human brain, leading to deep structural learning. Limitations of AI include difficulties with language, arising from the need to understand context and interpret ambiguities, which particularly affect translation, and inadequacies of databases, requiring careful preparation and curation. New techniques may cause unforeseen difficulties via unexpected malfunctioning. Relevant terms and concepts include different types of machine learning, neural networks, natural language programming, ontologies, and expert systems. Adoption of the tools of AI in pharmacovigilance has been slow. Machine learning, in conjunction with natural language processing and data mining, to study adverse drug reactions in databases such as those found in electronic health records, claims databases, and social media, has the potential to enhance the characterization of known adverse effects and reactions and detect new signals.

Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data.

IntroductionAdverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients’ adverse drug reaction reports.ObjectivesWe aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal.MethodsReports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period.ResultsInternal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results.ConclusionsOur artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40264-022-01153-8.

Comparison of Hand Eczema in Iraq Before and During SARS-CoV-2 Pandemic Using Frequentist Statistics and Polynomial Models

Introduction. SARS-CoV-2 pandemic spread around the world exponentially. People use disinfectants excessively to protect themselves from the novel coronavirus, which may cause contact eczema; local health authorities can monitor these. Our study explores the internet to detect significant changes in online information search (seeking) behaviors connected with eczema in Iraq during the pandemic. Material and Methods. We searched the internet, via Google Trends, using five search terms; "اكزيما", "الاكزيما", "اكزيما اليد", "كحول", and "مطهر"; these are the Arabic translation for "eczema", "the eczema", "hand eczema", "alcohol", and "disinfectant". We explored the internet covering two years, one before and one during the pandemic. Results. Spatial mapping conveyed data from six governorates in Iraq, including Ninawa, Babil, Al-Najaf, Baghdad, Basrah, and Erbil. Basrah governorate had the highest score (interest) for the search term "the eczema" (الاكزيما), while Al-Najaf had the highest score for the search term "disinfectant" (مطهر). Temporal mapping exhibited high variability, the highest of which was for the search terms "the eczema" (الاكزيما) and "alcohol" (كحول). Exploring the temporal variabilities, using polynomial models showed weak predictive power over the two years. However, during the pandemic year, the predictive models for each search term possessed a moderate power. Conclusions. Changes in human behavior during pandemic events are of prime importance for pharmacovigilance experts. Pandemics can influence medical conditions, including hand eczema, as a manifestation of disinfectants' overuse. Combining data analytics and artificial intelligence facilitates screening, detecting, and collecting pharmacovigilance and safety data.

Organisation of the Russian Pharmacovigilance System: Survey of Pharmacovigilance Officers

Ensuring the safety of pharmacotherapy is a priority of the national regulatory health policy and is enshrined in the international and federal legislation. Survey of qualified pharmacovigilance experts on current problems in pharmacovigilance is one of the tools for improving the pharmacovigilance system efficiency. The aim of the study was to analyse the Russian pharmacovigilance system organisation based on the results of a survey of pharmacovigilance officers. Materials and methods: the authors carried out a survey of 26 pharmacovigilance officers from pharmaceutical companies. Results: the majority of the respondents (50%) had short work experience in this area and combined pharmacovigilance duties with other functions in the company. At the same time, 73.08% of the respondents noted the lack of full-time pharmacovigilance staff in the company. According to the respondents, one of the most important problems in organising pharmacovigilance in the Russian Federation is a low level of staff qualification, which stems from the lack of specific pharmacovigilance courses at medical universities. The lack of qualified staff hinders the preparation of periodic and routine pharmacovigilance reports (according to 67.1% of the respondents) as well as the implementation of critical pharmacovigilance processes, e.g., risk management, and organisation of the pharmacovigilance quality management system. In addition, the paper discusses the issues of state support of private pharmacovigilance activities and the creation of a consolidated system for promoting consumer awareness about pharmacovigilance. Conclusions: the improvement of the pharmacovigilance system should be based on: state oversight of the authorised regulatory and supervisory bodies, marketing authorisation holders, and professional communities; joint efforts on the part of professional communities and educational institutions in order to train staff in practical pharmacovigilance aspects — as part of post-graduate and professional education programmes; adequate reflection of pharmacovigilance problems and potential solutions in the mass media.

Pharmacovigilance network as an additional tool for the surveillance of antimicrobial resistance.

The WHO Programme for International Drug Monitoring (PIDM) is a large Pharmacovigilance network of countries sharing Adverse Drug Reaction (ADR) reports. Pharmacovigilance Experts have suggested that antimicrobial resistance (AMR) is an overlooked adverse event. We undertook this study to investigate the potential role of Pharmacovigilance databases in the surveillance of AMR. Using the AWaRe (Access, Watch and Reserve) list and the WHO Priority Pathogens List, we established a list of antimicrobials and carried out a VigiBase search via VigiAccess, looking for ADR reports with Preferred Terms (PTs) that contained AMR-relevant information. Identified Terms were matched with codes from the Medical Dictionary for Regulatory Activities (MedDRA Version 21.1). Records on 86 drugs were retrieved with a total of 1 170 751 ADR reports submitted between 1968 and 2018. Seventeen PTs suggesting suspected resistance, ineffectiveness, inappropriate use, or medication error were used to code 15 250 reports. The most frequently used PTs were "Drug Ineffective" (45.6%), "Off label use" (9.5%) and "Pathogen Resistance" (8.9%). A group of six agents (Amoxicillin, Cefalotin, Ciprofloxacin, Clarithromycin, Levofloxacin and Daptomycin) accounted for 38% (n=5806) of all 15 250 AMR-relevant ADR reports. The PTs most frequently used in 5806 reports were grouped in 4 categories: drug ineffectiveness (62.5%), resistance (19.2%), off-label use (12.1%) and prescription errors (6.2%). Our findings suggest that Pharmacovigilance databases could serve as a tool in tracking antimicrobial use and resistance especially in settings where laboratory capacity is still in its development stages. National Pharmacovigilance centers could play a proactive role in stimulating the reporting of AMR-relevant ADRs which can serve as a basis for resistance suspicion alerts. Further studies focusing on the narrative and other clinical pharmacology details in ADR reports are required.

The Trigger Tool Method for Routine Pharmacovigilance: A Retrospective Cohort Study of the Medical Records of Hospitalized Geriatric Patients.

The main objective was to assess the feasibility of the trigger tool method for the retrospective detection of adverse drug reactions (ADRs) in the Rennes University Hospital. The secondary objective was to describe the performance of the method in terms of positive predictive values (PPVs) and severity or preventability of ADRs. Using the Rennes University Hospital clinical data warehouse, pharmacovigilance experts performed a retrospective review of a random sample of 30 inpatient hospital medical records per month using the triggers "fall" and "delirium" to identify related ADRs among patients 65 years and older in 2018 in the geriatrics department. Using the Z test, we compared the proportion of medical records with a positive (identified) trigger related to an ADR, which were reviewed within 20 minutes using the reference of 50% reviewed within 20 minutes. Among the 355 medical records reviewed, 222 had at least 1 trigger and 98 at least 1 related ADR. Among the 222 positive trigger medical records, 99.6% were reviewed in under 20 minutes (P < 0.001). The pharmacovigilance assessment took 3 months. The PPVs reached 53.9% (46.0%-61.7%) for falls and 21.0% (14.3%-27.5%) for delirium. Among the ADRs, 80% were serious and 53% were preventable. Given the low PPV of the triggers used and the considerable need for technical and human resources, the trigger tool method cannot be used as a routine tool at the pharmacovigilance center. However, it could be implemented occasionally for specific purposes such as monitoring the impact of risk minimization measures to prevent ADRs.

Relationship between adverse drug reactions and unlicensed/off-label drug use in hospitalized children (EREMI): A study protocol

To date, few studies have shown a significant association between off-label drug use and adverse drug reactions (ADRs). The main aims of this study is to evaluate the relationship between adverse drug reactions and unlicensed or off-label drugs in hospitalized children and to provide more information on prescribing practice, the amplitude, consequences of unlicensed or off-label drug use in pediatric inpatients. In this multicenter prospective study started from 2013, we use the French summaries of product characteristics in Theriaque (a prescription products guide) as a primary reference source for determining pediatric drug labeling. The detection of ADRs is carried out spontaneously by health professionals and actively by research groups using a trigger tool and patients' electronic health records. The causality between suspected ADRs and medication is evaluated using the Naranjo and the French methods of imputability independently by pharmacovigilance center. All suspected ADRs are submitted for a second evaluation by an independent pharmacovigilance experts. For our best knowledge, EREMI is the first large multicenter prospective and objective study in France with an active ADRs monitoring and independent ADRs validation. This study identifies the risk factors that could be used to adjust preventive actions in children's care, guides future research in the field and increases the awareness of physicians in off-label drug use and in detecting and declaring ADRs. As data are obtained through extraction of information from hospital database and medical records, there is likely to be some under-reporting of items or missing data. In this study the field specialists detect all adverse events, experts in pharmacovigilance centers assess them and finally only the ADRs assessed by the independent committee are confirmed. Although we recruit a high number of patients, this observational study is subject to different confounders.

Drug-Induced Acute Kidney Injury: A Study from the French Medical Administrative and the French National Pharmacovigilance Databases Using Capture-Recapture Method.

Background: Acute kidney injury (AKI) is a public health concern. Among the pathological situations leading to AKI, drugs are preventable factors but are still under-notified. We aimed to provide an overview of drug-induced AKI (DIAKI) using pharmacovigilance and medical administrative databases Methods: A query of the PMSI database (French Medical Information System Program) of adult inpatient hospital stays between 1 January 2017 and 31 December 2018 was performed using ICD-10 (International Classification of Diseases 10th revision) codes to identify AKI cases which were reviewed by a nephrologist and a pharmacovigilance expert to identify DIAKI cases. In parallel, DIAKIs notified in the French Pharmacovigilance Database (FPVDB) were collected. A capture-recapture method was performed to estimate the total number of DIAKIs. Results: The estimated total number of DIAKIs was 521 (95%CI 480; 563), representing 20.0% of all AKIs. The notification was at a rate of 12.9% (95%CI 10.0; 15.8). According to the KDIGO classification, 50.2% of the DIAKI cases were stage 1 and 49.8% stage 2 and 3. The mortality rate was 11.1% and 9.6% required hemodialysis. Conclusion: This study showed that drugs are involved in a significant proportion of patients developing AKI during a hospital stay and emphasizes the severity of DIAKI cases.

Developing Crowdsourced Training Data Sets for Pharmacovigilance Intelligent Automation.

Machine learning offers an alluring solution to developing automated approaches to the increasing individual case safety report burden being placed upon pharmacovigilance. Leveraging crowdsourcing to annotate unstructured data may provide accurate, efficient, and contemporaneous training data sets in support of machine learning. The objective of this study was to evaluate whether crowdsourcing can be used to accurately and efficiently develop training data sets in support of pharmacovigilance automation. Pharmacovigilance experts created a reference dataset by reviewing 15,490 de-identified social media posts of narratives pertaining to 15 drugs and 22 medically relevant topics. A random sampling of posts from the reference dataset was published on Amazon Turk and its users (Turkers) were asked a series of questions about those same medical concepts. Accuracy, price elasticity, and time efficiency were evaluated. Accuracy of crowdsourced curation exceeded 90% when compared to the reference dataset and was completed in about 5% of the time. There was an increase in time efficiency with higher pay, but there was no significant difference in accuracy. Additionally, having a social media post reviewed by more than one Turker (using a voting system) did not offer significant improvements in terms of accuracy. Crowdsourcing is an accurate and efficient method that can be used to develop training data sets in support of pharmacovigilance automation. More research is needed to better understand the breadth and depth of possible uses as well as strengths, limitations, and generalizability of results.

Adverse events related to ibuprofen treatment for patent ductus arteriosus in premature neonates

ObjectiveOur study aimed to review adverse drug reactions (ADRs) associated with ibuprofen treatment of patent ductus arteriosus (PDA) in premature neonates. MethodWe retrospectively evaluated electronic patient records from neonates treated with ibuprofen for PDA during 5 years in a French neonatal intensive care unit. Full chart review and targeted triggers were used to detect ADRs. The causality between suspected ADRs and medication was evaluated using the WHO causality assessment method by pharmacovigilance experts. Categorical variables were compared using chi-square tests or Fisher's test. Quantitative variables were compared using the Student t test. We explored the risk factors associated with ADR using univariate model analysis. ResultOf 227 infants with a mean gestational age (GA) of 27 weeks (24–33), 12 (5%) developed intestinal perforation and seven, necrotizing enterocolitis (3%). The perforation occurred less frequently in infants older than 27 weeks GA (OR=0.14; 95% CI=0.03–0.66, P=0.01). Other observed ADRs were acute renal failure (25 infants, 11%) and thrombocytopenia (five infants, 2%). ConclusionGastrointestinal complications observed in infants treated with ibuprofen for PDA including gastrointestinal perforations occur in less mature infants. Active chart review of the patient's medical file with a trigger tool should be evaluated for routine ADR monitoring.

Development of a module for the prevention of nonsteroidal anti-inflammatory drugs-associated gastrointestinal adverse reactions in the elderly at a primary health center.

The prevention of nonsteroidal anti-inflammatory drugs (NSAIDs) adverse reactions should start from the primary health center (PHC), as the first gatekeeper in community health services. However, there is no specific module available for health care professionals (HCPs) in Indonesia for the prevention of adverse drug reactions (ADR) at PHCs. NSAID is commonly used for the elderly treated at PHC in Indonesia, even though the ADR risk is well-known. We aimed to develop a module to be used in PHC for preventing NSAID-associated upper gastrointestinal (GI) ADRs in elderly patients treated for musculoskeletal diseases. The module was developed based on inputs from focus group discussions (FGD) among government health officers, PHC representatives, clinical pharmacologists, internal medicine and community medicine clinicians, pharmacovigilance experts, and professional organizations. A pilot implementation was conducted to test its feasibility and its effect on the HCPs' knowledge. Capacity building of HCPs, development of intra-HCP cooperation, as well as standard operating procedure (SOP) for the prescription of NSAID constituted important components of the module. A pilot study of the module in two PHCs showed that it was applicable with some recommendations for improvement in duration, number of participants, room space, presentation, and use of credit points as compliments. The HCPs' knowledge was improved after following the module. Our study showed that the module is feasible in PHC in Indonesia and useful in improving knowledge of HPC.