Angioedema is a non-pitting edema that involves the subcutaneous and submucosal layers of the face, lips, neck, oral cavity, larynx, and gut. It may become life-threatening when it involves tissues of the larynx. Angioedema can be triggered by exposure to drugs such as angiotensin-converting enzyme inhibitors (ACE inhibitors), opioid drugs, and nonsteroidal anti-inflammatory drugs (NSAIDs). Tramadol is an opioid analgesic medication that may also induce angioedema, but the incidence of tramadol-induced angioedema is very rare in literature to date. It has been postulated that tramadol may cause fatal angioedema in the presence of underlying diseases such as systemic lupus erythematosus (SLE) or concomitant drugs such as NSAIDs. We describe the case of a patient with SLE who experienced fatal angioedema following tramadol intake.

Toxic epidermal necrolysis (TEN) is a severe and potentially fatal adverse drug reaction. This case report presents a 19-year-old male with pulmonary tuberculosis undergoing anti-tubercular therapy who developed TEN. The patient had multiple comorbidities including type 1 diabetes mellitus and multisystem atrophy. ChatGPT was utilized alongside conventional methods to assess causality. While conventional scoring systems estimated mortality at 58.3% (SCORTEN) and 12.3% (ABCD-10), ChatGPT yielded divergent scores. Causality assessment using WHO-Uppsala Monitoring Centre (UMC) and Naranjo\'s scale indicated rifampicin and isoniazid as probable causative agents. However, ChatGPT provided ambiguous results. The study underscores the potential of AI in pharmacovigilance but emphasizes caution due to discrepancies observed. Collaborative utilization of artificial intelligence (AI) with clinical judgment is advocated to enhance diagnostic accuracy and treatment decisions in adverse drug reactions. This case highlights the importance of integrating AI into drug safety systems while acknowledging its limitations to ensure optimal patient care.

The pharmacovigilance program of India (PvPI), after its inception, has been reliably acquiring force in bringing issues to light among the masses, healthcare professionals, the pharma industry, and clinical staff at hospitals. Adverse drug reactions are unintended events that occur after exposure to a drug, biological product, or medical device, and they may result in morbidity and mortality. It is critical to monitor the safety of drugs during the post-marketing phase to find long-term and rare ADRs, as well as ADRs in special populations and patients with co-morbidities that are not usually included during clinical trials. The definitive objective of pharmacovigilance is to collate data and analyze it. Assessing the causality between ADRs and drugs is necessary to decrease the occurrence of ADRs and to reduce the risk of drug-related ADRs. ADRs may lead to increased morbidity, increased hospital stays, and increased cost of treatment, resulting in compromised patient safety. Causality assessment is the evaluation of the likelihood that a particular treatment is the cause of an observed adverse event and establishing a causal association between a drug and a drug reaction is necessary to prevent further recurrences. Numerous methods available for establishing a causal association between the drug and adverse events have been broadly classified into clinical judgment or global introspection, algorithms, and probabilistic methods. These include the Swedish method, World Health Organization-Uppsala Monitoring Centre (WHO-UMC) scale, Naranjo\'s algorithm, Kramer algorithm, Jones algorithm, Karch algorithm, Bégaud algorithm, Adverse Drug Reactions Advisory Committee guidelines, Bayesian Adverse Reaction Diagnostic Instrument, and so on. Despite various methods available, none of the causality assessment tools have been universally accepted as the gold standard. Naranjo\'s algorithm and WHO-UMC scales are, however, the most commonly used. Similarly, for preventability and severity assessment of ADRs, the Schumock and Thornton scale and Hartwig and Siegel\'s scale are most commonly used. Hence, we reviewed different tools and methods available to assess the causality, preventability, and severity of ADRs.

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.

In modern practice viral parotitis is unlikely to be due to mumps. Case and surveillance studies have detected a host of other viruses in mumps-negative viral parotitis, but because of their weak association with viral parotitis, it has been difficult to establish causality. This case report is unique because a familial pair presented in tandem with different manifestations of an infection with the parainfluenza virus. These circumstances allowed the strong association of the parainfluenza virus with the mother\'s croup to be substituted for the normally weak association of the parainfluenza virus with the son\'s viral parotitis. This strongly inferred that the parainfluenza virus caused the patient\'s viral parotitis and provides the best evidence to date of a virus other than mumps causing viral parotitis.

OBJECTIVE: In 2021, the US Centers for Disease Control and Prevention reported increased cases of myocarditis and pericarditis in the United States after mRNA COVID-19 vaccines. Our study aims to estimate the incidence of myocarditis in Apulia (Southern Italy) and the cause-effect relationship between COVID-19 mRNA vaccines and the risk of myocarditis.
METHODS: The Apulian regional archive of hospital discharge forms was used to define the cases of myocarditis in Apulia, considering data from 2017 to 2022. The overall vaccination status of patients was assessed via data collected from the Regional Immunization Database. The history of SARS-CoV-2 infection was extracted from the Italian Institute of Health platform.
RESULTS: Since 2017, 5687 cases of myocarditis have been recorded in Apulian subjects; the overall incidence described a decreasing trend, with a slight increase in 0-40 years-old subjects. From 2021 to 2022, 2,930,276 doses of COVID-19 mRNA vaccines were administered; a diagnosis of myocarditis after the second dose of the mRNA vaccine was reported for 894 (0.03%) of Apulian inhabitants, with an incidence rate of 17.9 × 1,000,000 persons-month. The multivariate analysis, adjusted for age, sex, underlying medical conditions, and diagnosis of COVID-19, showed that mRNA vaccination is a protective factor for myocarditis even in younger subjects (aOR = 0.4; 95% CI = 0.3-0.5).
CONCLUSIONS: A temporal association between an exposure and an outcome is not equivalent to a causal association. Our study underlines how an approach that considers the other potential causes of myocarditis (primarily COVID-19) and a causality assessment must be prioritized in the study of the topic.

OBJECTIVE: In 2019, the International Working Group (IWG), focusing on New Developments in Pharmacovigilance, was established. This group is coordinated by the Drug Safety Research Unit in the United Kingdom, and the mission of the IWG is to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines, thereby further protecting patients. Novel therapeutics are continuously being developed to alleviate medical conditions, but with advancing technologies, innovative pharmacovigilance methodologies need to be developed to effectively monitor the use and safety of these products. With reduced timelines proposed for premarketing clinical trials and increased application of real-world evidence supporting regulatory approvals, products may be used in real-world clinical practice in shorter timeframes than before. Therefore, the need for effective methods of monitoring medicines and collecting safety data in real-time is of paramount importance to public health.
METHODS: The IWG aims to advance existing methodologies used in the detection, monitoring, and analysis of safety data in pharmacovigilance and to communicate best practice proposals to support decision making in health care. The IWG will identify areas requiring review of current processes or methodologic research and will communicate the output of the IWG through peer-reviewed publications, reports, and presentation of findings at relevant conferences and scientific meetings.
RESULTS: The IWG is currently reviewing two areas in pharmacovigilance; case-level causality assessment and the strengths and limitations of data sources. The IWG is advancing these areas by producing two scoping reviews which will be easily accessible to regulatory agencies, industry, academia, and interested persons or organizations.
CONCLUSIONS: The scoping reviews comply with the IWGs mission to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines. The present article shares details of the objectives of the IWG and provides an overview on the status of IWG activities.

The timely and accurate adverse drug reactions (ADR) assessment is vital for effective patient management and healthcare delivery. The Naranjo Algorithm is a widely recognized tool for determining the probability that a drug induces a given ADR. However, the process can be time-consuming and susceptible to human error. This study introduces a Python-based console application (Python Software Foundation, Wilmington, Delaware, United States) designed to automate the Naranjo Algorithm for ADR causality assessment. The application was developed using Python 3.11.4 on a Windows 11 system (Microsoft Corporation, Redmond, Washington, United States) and compiled in Notepad (Microsoft Corporation), a basic text editor, highlighting its accessibility and ease of use in various settings. User input is solicited for each question in the Naranjo Algorithm, validated for acceptable entries, and subsequently scored. The final score categorizes the reaction into Doubtful, Possible, Probable, or Definite ADR, facilitating rapid clinical decision-making. Preliminary validation shows promising reliability and effectiveness, making it a valuable asset in research and clinical settings for assessment.

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The core motive of pharmacovigilance is the detection and prevention of adverse drug reactions (ADRs), to improve the risk-benefit balance of the drug. However, the causality assessment of ADRs remains a major challenge among clinicians, and none of the available tools of causality assessment used for assessing ADRs have been universally accepted.
To provide an up-to-date overview of the different causality assessment tools.
We conducted electronic searches in MEDLINE, EMBASE, and the Cochrane database. The eligibility of each tool was screened by three reviewers. Each eligible tool was then scrutinized for its domains (the reported specific set of questions/areas used for calculating the likelihood of cause-and-effect relation of an ADR) to discover the most comprehensive tool. Finally, we subjectively assessed the tool\'s ease-of-use in a Canadian, Indian, Hungarian, and Brazilian clinical context.
Twenty-one eligible causality assessment tools were retrieved. Naranjo\'s tool and De Boer\'s tool appeared the most comprehensive among all the tools, covering 10 domains each. Regarding \"ease-of-use\" in a clinical setting, we judged that many tools were hard to implement in a clinical context because of their complexity and/or lengthiness. Naranjo\'s tool, Jones\'s tool, Danan and Benichou\'s tool, and Hsu and Stoll\'s tool appeared to be the easiest to implement into various clinical contexts.
Among the many tools identified, 1981 Naranjo\'s scale remains the most comprehensive and easy to use for performing causality assessment of ADRs. Upcoming analysis should compare the performance of each ADR tool in clinical settings.