Dataset Information

Adverse event reports in patients taking psychiatric medication during pregnancy from spontaneous reports in Japan and the United States: an approach using latent class analysis.

ABSTRACT: BACKGROUND:Little is known regarding the association between adverse events (AEs) and psychiatric medications administered to pregnant women in clinical trials during the pre-marketing period. This study analyzes reports of AE association with psychiatric medication administrated during pregnancy using post-marketing spontaneous reports of AE from the Japanese Adverse Drug Event Report (JADER) database and Food and Drug Administration Adverse Event Reporting System in the United States (FAERS-US). METHODS:We summarized AE reports of psychiatric medication administrated during pregnancy by comparing data obtained from JADER and FAERS-US databases with medication patterns determined as classes via latent class analysis. The odds ratios (ORs) of AE reports categorized into system organ classes in which each class was compared with those without psychiatric medications. RESULTS:The proportions of AE reports under psychiatric medication in pregnancy among all AE reports were 22.0% and 16.6% in JADER and FAERS-US, respectively. The 10,389 reports of psychiatric medication during pregnancy were classified into 11 classes. The proportion of patients receiving four or more psychiatric drugs in JADER was larger than that in FAERS-US. The maximum number of reports in combinations of AE and medication pattern in JADER was 169, for 'general disorders and administration site conditions' from the class of four or more medications (OR = 9.1), while that in FAERS-US was 1,654, for 'injury, poisoning, and procedural complications' from the class of single psychiatric medication (OR = 2.8). CONCLUSIONS:The main AE reports and associated AE differed depending on medication patterns in pregnant women taking psychiatric medication. This study may provide a prediction of AEs that are likely to be reported with each medication pattern. Our findings of the association between AE reports and medication patterns could help improve the administration of psychiatric medications during pregnancy, though further research on additional datasets is needed to clarify these results.

SUBMITTER: Anzai T

PROVIDER: S-EPMC7068895 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Adverse event reports in patients taking psychiatric medication during pregnancy from spontaneous reports in Japan and the United States: an approach using latent class analysis.

Anzai Tatsuhiko T Takahashi Kunihiko K Watanabe Michiko M Mochizuki Mayumi M Murashima Atsuko A

BMC psychiatry 20200312 1

<h4>Background</h4>Little is known regarding the association between adverse events (AEs) and psychiatric medications administered to pregnant women in clinical trials during the pre-marketing period. This study analyzes reports of AE association with psychiatric medication administrated during pregnancy using post-marketing spontaneous reports of AE from the Japanese Adverse Drug Event Report (JADER) database and Food and Drug Administration Adverse Event Reporting System in the United States ( ...[more]

PMID: 32164630

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Project description:BackgroundThe development of anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors (TKIs) has improved the survival outcomes of patients with advanced ALK-rearranged non-small-cell lung cancer (NSCLC). The adverse events (AEs) related to ALK inhibitors are fairly well known; notably, about 20% of patients receiving lorlatinib experienced cognitive effects and behavioral alterations in pivotal trials. Therefore, psychiatric disorders could represent AEs of special interest for all ALK TKIs, deserving careful assessment in the post-marketing setting.ObjectiveWe conducted a real-world pharmacovigilance study on psychiatric AEs with marketed ALK inhibitors in subjects with advanced NSCLC.Patients and methodsWe performed an observational, retrospective analysis of spontaneous reports submitted to the Food and Drug Administration Adverse Events Reporting System (FAERS, as of December 2020), selecting psychiatric AEs to ALK TKIs approved in NSCLC (crizotinib, ceritinib, alectinib, brigatinib, lorlatinib). These AEs were independently scrutinized by three oncologists applying predefined exclusion criteria, described in terms of clinical/demographic features and assessed for drug-related causality according to an adaptation of the WHO-UMC system, a standardized probabilistic algorithm.ResultsAmong 584 reported psychiatric AEs, 95 cases were selected as potentially treatment related, with higher reporting frequency for lorlatinib (26, 2.8%), followed by brigatinib (10, 1.2%), alectinib (18, 0.7%), ceritinib (12, 0.6%), and crizotinib (29, 0.3%). Reported psychiatric symptoms were mood disorders (39), psychotic disorders (24), and anxiety, agitation, and irritability (25). In the majority (74%) of cases, psychiatric AEs were serious and required hospitalization in about 32% of patients; 15.8% of retained cases were considered as highly probable and 69.5% as probable. Drug discontinuation was recorded in 31.6% of the reported cases, with the highest proportion for lorlatinib (65.4%).ConclusionNotwithstanding limitations, our study found a higher proportion of psychiatric AEs with lorlatinib, but also raised the hypothesis of psychiatric reactions as a class effect of ALK TKIs.

Project description:BackgroundMedication events in clinical settings are significant threats to patient safety. Analyzing and learning from the medication event reports is an important way to prevent the recurrence of these events. Currently, the analysis of medication event reports is ineffective and requires heavy workloads for clinicians. An automated pipeline is proposed to help clinicians deal with the accumulated reports, extract valuable information and generate feedback from the reports. Thus, the strategy of medication event prevention can be further developed based on the lessons learned.MethodsIn order to build the automated pipeline, four classic machine learning classifiers (i.e., support vector machine, Naïve Bayes, random forest, and multi-layer perceptron) were compared to identify the event originating stages, event types, and event causes from the medication event reports. The precision, recall and F-1 measure were calculated to assess the performance of the classifiers. Further, a strategy to measure the similarity of medication event reports in our pipeline was established and evaluated by human subjects through a questionnaire.ResultsWe developed three classifiers to identify the medication event originating stages, event types and causes, respectively. For the event originating stages, a support vector machine classifier obtains the best performance with an F-1 measure of 0.792. For the event types, a support vector machine classifier exhibits the best performance with an F-1 measure of 0.758. And for the event causes, a random forest classifier reaches an F-1 measure of 0.925. The questionnaire results show that the similarity measurement is consistent with the domain experts in the task of identifying similar reports.ConclusionWe developed and evaluated an automated pipeline that could identify three attributes from the medication event reports and calculate the similarity scores between the reports based on the attributes. The pipeline is expected to improve the efficiency of analyzing the medication event reports and to learn from the reports in a timely manner.

Dataset Information

Adverse event reports in patients taking psychiatric medication during pregnancy from spontaneous reports in Japan and the United States: an approach using latent class analysis.

Publications

Adverse event reports in patients taking psychiatric medication during pregnancy from spontaneous reports in Japan and the United States: an approach using latent class analysis.

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