Drug-drug interactions (DDIs) are an important cause of adverse drug reactions (ADRs). In literature most of studies focus only on potential DDIs, while detailed data on serious ADRs associated with ...DDIs are limited. Our aim is to identify and characterize serious ADRs caused by DDIs using a spontaneous reporting database.
All serious ADR reports, not related to vaccines and with a "definite", "probable" or "possible" causality assessment, inserted into the National Pharmacovigilance database from Veneto Region (January 1, 2015 to May 31, 2020) were analyzed. A list of drug pairs was created by selecting the reports containing at least two suspected or concomitant drugs. We verified which drug pairs potentially interacted according to the online version of DRUGDEX
system. For each potential DDI we controlled whether the ADR description in the report corresponded to the interaction effect as described in Micromedex. A detailed characterization of all serious reports containing an occurring DDI was performed.
In the study period a total of 31,604 reports of suspected ADRs from the Veneto Region were identified, of which 2,195 serious reports (6.9% of all ADR reports) containing at least two suspected or concomitant drugs were analyzed. We identified 1,208 ADR reports with at least one potential DDI (55.0% of 2,195) and 381 reports (17.4% of 2,195 reports) with an occurring ADR associated with a DDI. The median age of patients and the number of contraindicated or major DDIs were significantly higher in reports with an occurring DDI. Warfarin was the most frequently reported interacting drug and the most common ADRs were gastrointestinal or cerebral hemorrhagic events. The proton pump inhibitors/warfarin, followed by platelet aggregation inhibitors/warfarin were the drug-drug combinations most frequently involved in ADRs caused by DDIs. The highest proportion of fatal reports was observed with platelet aggregation inhibitors/warfarin and antidepressants/warfarin.
Our findings showed that about one-third of patients exposed to a potential DDI actually experienced a serious ADR. Furthermore, our study confirms that a spontaneous reporting database could be a valuable resource for identifying and characterizing ADRs caused by DDIs and the drugs leading to serious ADRs and deaths.
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•The NLP software MagiCoder is introduced.•MagiCoder automatically maps spontaneous reports into MedDRA terminology.•We tested MagiCoder against a gold standard of about 1800 manually ...revised reports.•We measured an average recall and precision of 86.9% and 91.8%, respectively.•MagiCoder reduces the time required for encoding ADR reports.
The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs). In such task qualified domain experts manually revise a huge amount of narrative descriptions and then encode texts according to MedDRA standard terminology. The manual annotation of narrative documents with medical terminology is a subtle and expensive task, since the number of reports is growing up day-by-day.
Natural Language Processing (NLP) applications can support the work of people responsible for pharmacovigilance. Our objective is to develop NLP algorithms and tools for the detection of ADR clinical terminology. Efficient applications can concretely improve the quality of the experts’ revisions. NLP software can quickly analyze narrative texts and offer an encoding (i.e., a list of MedDRA terms) that the expert has to revise and validate.
MagiCoder, an NLP algorithm, is proposed for the automatic encoding of free-text descriptions into MedDRA terms. MagiCoder procedure is efficient in terms of computational complexity. We tested MagiCoder through several experiments. In the first one, we tested it on a large dataset of about 4500 manually revised reports, by performing an automated comparison between human and MagiCoder encoding. Moreover, we tested MagiCoder on a set of about 1800 reports, manually revised ex novo by some experts of the domain, who also compared automatic solutions with the gold reference standard. We also provide two initial experiments with reports written in English, giving a first evidence of the robustness of MagiCoder w.r.t. the change of the language.
For the current base version of MagiCoder, we measured an average recall and precision of 86.9% and 91.8%, respectively.
From a practical point of view, MagiCoder reduces the time required for encoding ADR reports. Pharmacologists have only to review and validate the MedDRA terms proposed by the application, instead of choosing the right terms among the 70 K low level terms of MedDRA. Such improvement in the efficiency of pharmacologists’ work has a relevant impact also on the quality of the subsequent data analysis. We developed MagiCoder for the Italian pharmacovigilance language. However, our proposal is based on a general approach, not depending on the considered language nor the term dictionary.
Introduction
Evidence is lacking on withdrawal syndrome related to individual antidepressants and relevant risk factors for severe reactions.
Objective
To ascertain whether antidepressants are ...associated with an increased reporting of withdrawal syndrome as compared with other medications, and to investigate risk factors for severe reactions.
Methods
This is a case/non-case pharmacovigilance study, based on the VigiBase
®
, the WHO global database of individual case safety reports of suspected adverse drug reactions. We performed a disproportionality analysis of reports of antidepressant-related withdrawal syndrome (calculating reporting odds ratio ROR and Bayesian information component IC). We compared antidepressants to all other drugs, to buprenorphine (positive control), and to each other within each class of antidepressants (selective serotonin reuptake inhibitors SSRIs, tricyclics and other antidepressants). Antidepressants with significant disproportionate reporting were ranked in terms of clinical priority. Serious versus non-serious reactions were compared.
Results
There were 31,688 reports of antidepressant-related withdrawal syndrome were found. A disproportionate reporting was detected for 23 antidepressants. The estimated ROR for antidepressants altogether, compared to all other drugs, was 14.26 (95% CI 14.08–14.45), 17.01 for other antidepressants (95% CI 16.73–17.29), 13.65 for SSRIs (95% CI 13.41–13.90) and 2.8 for tricyclics (95% CI 2.59–3.02). Based on clinical priority ranking, the strongest disproportionate reporting was found for paroxetine, duloxetine, venlafaxine and desvenlafaxine, being comparable to buprenorphine. Withdrawal syndrome was reported as severe more often in males, adolescents, persons in polypharmacy, and with a longer antidepressant treatment duration (
p
< 0.05).
Conclusions
Antidepressants are associated with an increased reporting of withdrawal syndrome compared with other drug classes. When prescribing and discontinuing antidepressants, clinicians should be aware of the potentially different proclivity of withdrawal syndrome across individual antidepressants, and the liability to experience more severe withdrawal symptoms in relation to specific patient characteristics.
Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in ...narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of MagiCoder, an NLP application designed to extract MedDRA terms from narrative clinical text. Given a narrative description, MagiCoder proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested MagiCoder performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about MagiCoder. Moreover, we do a change of language, moving to English documents. In particular, we tested MagiCoder on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.
To assess the extent of off-label drug use and the occurrence of suspected adverse drug reactions (ADRs) among paediatric patients in Italian hospitals.
We conducted a 2-year prospective cohort study ...across 22 Italian hospital wards from September 2020 to September 2022. As part of the surveillance project, we performed a 6-month retrieval of all reported ADRs and evaluated all drug prescriptions for their possible off-label use. Following an educational project on pharmacovigilance addressed to healthcare professionals in participating wards, the same data collection was performed.
Among the 892 patients included in the study, 64% were admitted to paediatric wards and 36% to neonatal wards. Fifty per cent of all drugs prescribed were used off-label and mainly concerned the administration of a different dose from the one authorized. In neonatal wards, off-label prescriptions occurred slightly more often, with antibacterials being the most frequently used off-label drugs. A total of 35 reports of suspected ADRs were collected, five before the educational project and 30 afterwards. Based on product licence, 10 of the total 35 reports concerned at least one off-label drug use.
The off-label use of drugs in treating paediatric patients was extensive in Italian hospitals. Regulatory interventions are needed to promote the use of drugs based on the latest available literature and improve ADR reporting on children. Paediatric indications and dosages of the drugs most commonly used in children should be supported by appropriate ad hoc studies.
To date, four vaccines have been authorised for emergency use and under conditional approval by the European Medicines Agency to prevent COVID-19: Comirnaty, COVID-19 Vaccine Janssen, Spikevax ...(previously COVID-19 Vaccine Moderna) and Vaxzevria (previously COVID-19 Vaccine AstraZeneca). Although the benefit-risk profile of these vaccines was proven to be largely favourable in the general population, evidence in special cohorts initially excluded from the pivotal trials, such as pregnant and breastfeeding women, children/adolescents, immunocompromised people and persons with a history of allergy or previous SARS-CoV-2 infection, is still limited. In this narrative review, we critically overview pre- and post-marketing evidence on the potential benefits and risks of marketed COVID-19 vaccines in the above-mentioned special cohorts. In addition, we summarise the recommendations of the scientific societies and regulatory agencies about COVID-19 primary prevention in the same vaccinee categories.
Introduction
Drug reaction with eosinophilia and systemic symptoms (DRESS) syndrome is gaining attention in pharmacovigilance, but its association with antipsychotics, other than clozapine, is still ...unclear.
Methods
We conducted a case/non-case study with disproportionality analysis based on the World Health Organization (WHO) global spontaneous reporting database, VigiBase®. We analyzed individual case safety reports of DRESS syndrome related to antipsychotics compared to (1) all other medications in VigiBase®, (2) carbamazepine (a known positive control), and (3) within classes (typical/atypical) of antipsychotics. We calculated reporting odds ratio (ROR) and Bayesian information component (IC), with 95% confidence intervals (CIs). Disproportionate reporting was prioritized based on clinical importance, according to predefined criteria. Additionally, we compared characteristics of patients reporting with serious/non-serious reactions.
Results
A total of 1534 reports describing DRESS syndrome for 19 antipsychotics were identified. The ROR for antipsychotics as a class as compared to all other medications was 1.0 (95% CI 0.9–1.1). We found disproportionate reporting for clozapine (ROR 2.3, 95% CI 2.1–2.5; IC 1.2, 95% CI 1.1–1.3), cyamemazine (ROR 2.3, 95% CI 1.5–3.5; IC 1.2, 95% CI 0.5–1.7), and chlorpromazine (ROR 1.5, 95% CI 1.1–2.1; IC 0.6, 95% CI 0.1–1.0). We found 35.7% of cases with co-reported anticonvulsants, and 25% with multiple concurrent antipsychotics in serious compared to 8.6% in non-serious cases (
p
= 0.03). Fatal cases were 164 (10.7%).
Conclusions
Apart from the expected association with clozapine, chlorpromazine and cyamemazine (sharing an aromatic heteropolycyclic molecular structure) emerged with a higher-than-expected reporting of DRESS. Better knowledge of the antipsychotic-related DRESS syndrome should increase clinicians’ awareness leading to safer prescribing of antipsychotics.
Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in ...narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of Formula Omitted, an NLP application designed to extract Formula Omitted terms from narrative clinical text. Given a narrative description, Formula Omitted proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested Formula Omitted performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about Formula Omitted. Moreover, we do a change of language, moving to English documents. In particular, we tested Formula Omitted on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.
Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in ...narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula>, an NLP application designed to extract <inline-formula><tex-math notation="LaTeX">\mathsf{MedDRA}</tex-math></inline-formula> terms from narrative clinical text. Given a narrative description, <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula> proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula> performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula>. Moreover, we do a change of language, moving to English documents. In particular, we tested <inline-formula><tex-math notation="LaTeX">\mathsf{MagiCoder}</tex-math></inline-formula> on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.
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