A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs

The identification of associations between drugs and adverse drug events (ADEs) is crucial for drug safety surveillance. An increasing number of studies have revealed that children and seniors are susceptible to ADEs at the population level. However, the comprehensive explorations of age risks in drug-ADE pairs are still limited. The FDA Adverse Event Reporting System (FAERS) provides individual case reports, which can be used for quantifying different age risks. In this study, we developed a statistical computational framework to detect age group of patients who are susceptible to some ADEs after taking specific drugs. We adopted different Chi-squared tests and conducted disproportionality analysis to detect drug-ADE pairs with age differences. We analyzed 4,580,113 drug-ADE pairs in FAERS (2004 to 2018Q3) and identified 2,523 pairs with the highest age risk. Furthermore, we conducted a case study on statin-induced ADE in children and youth. The code and results are available at https://github.com/Zhizhen-Zhao/Age-Risk-Identification

Reported Cases of Multisystem Inflammatory Syndrome in Children (MIS-C) Aged 12–20 Years in the United States Who Received COVID-19 Vaccine, December 2020 through August 2021

BackgroundMultisystem inflammatory syndrome in children (MIS-C) is a hyperinflammatory condition associated with antecedent SARS-CoV-2 infection. In the United States, reporting of MIS-C after vaccination is required under COVID-19 vaccine emergency use authorizations. This case series describes persons aged 12–20 years with MIS-C following COVID-19 vaccination reported to passive surveillance systems or through clinician outreach to CDC.MethodsWe investigated potential cases of MIS-C after COVID-19 vaccination reported to CDC’s health department-based national MIS-C surveillance, the Vaccine Adverse Event Reporting System (VAERS, co-administered by CDC and the U.S. FDA), and CDC’s Clinical Immunization Safety Assessment Project (CISA) from December 14, 2020, to August 31, 2021. We describe cases meeting the CDC MIS-C case definition. Any positive SARS-CoV-2 serology test satisfied the case criteria although anti-nucleocapsid antibody indicates SARS-CoV-2 infection, while anti-spike protein antibody indicates either infection or COVID-19 vaccination.FindingsWe identified 21 persons with MIS-C after COVID-19 vaccination. Of these 21 persons, median age was 16 years (range, 12–20 years); 13 (62%) were male. All were hospitalized; 12 (57%) had intensive care unit admission, and all were discharged home. Fifteen (71%) of the 21 had laboratory evidence of past or recent SARS-CoV-2 infection, and six (29%) did not. Through August 2021, 21,335,331 persons aged 12–20 years had received ≥1 dose of COVID-19 vaccine, making the overall reporting rate for MIS-C following vaccination 1·0 case per million persons receiving ≥1 vaccine dose in this age group. The reporting rate for those without evidence of SARS-CoV-2 infection was 0·3 cases per million vaccinated persons.InterpretationIn our case series, we describe a small number of persons with MIS-C who had received ≥1 COVID-19 vaccine dose before illness onset. Continued reporting of potential cases and surveillance for MIS-C illnesses after COVID-19 vaccination is warranted.FundingThis work was supported by the Centers for Disease Control and Prevention Clinical Immunization Safety Assessment (CISA] Project contracts 200-2012-50430-0005 to Vanderbilt University Medical Center and 200-2012-53661 to Cincinnati Children’s Hospital Medical Center.Research in context panelEvidence before this studyMultisystem inflammatory syndrome in children (MIS-C), also known as paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS), is an uncommon, but serious, complication described after SARS-CoV-2 infection that is characterized by a generalized hyperinflammatory response. A review of the literature using PubMed identified reports of six persons aged 12–20 years who developed MIS-C following COVID-19 vaccination. Search terms used to identify these reports were: “multisystem inflammatory syndrome in children”, “MIS-C”, “MISC”, “multisystem inflammatory syndrome in adults”, “MIS-A”, “MISA”, “paediatric inflammatory multisystem syndrome”, and “PIMS-TS” each with any COVID-19 vaccine type. There were no exclusion criteria (i.e., all ages and languages).Added value of this studyWe conducted integrated surveillance for MIS-C after COVID-19 vaccination using two passive surveillance systems, CDC’s MIS-C national surveillance and the Vaccine Adverse Event Reporting System (VAERS), and clinician or health department outreach to CDC, including through Clinical Immunization Safety Assessment (CISA) Project consultations. We investigated reports of potential MIS-C occurring from December 14, 2020, to August 31, 2021, in persons aged 12–20 years any time after receipt of COVID-19 vaccine to identify those that met the CDC MIS-C case definition. Any positive serology test was accepted as meeting the CDC MIS-C case definition, although anti- nucleocapsid antibody is indicative of SARS-CoV-2 infection, while anti-spike protein antibody may be induced either by SARS-CoV-2 infection or by COVID-19 vaccination. We investigated 47 reports and identified 21 persons with MIS-C after receipt of COVID-19 vaccine. Of the 21 persons with MIS-C, median age was 16 years (range 12–20 years), and 13 (62%) were male. Fifteen (71%) had laboratory evidence of past or recent SARS-CoV-2 infection (positive SARS-CoV-2 nucleic acid amplification test [NAAT], viral antigen, or serology test before or during MIS-C illness evaluation), and 5 (33%) of those 15 had illness onset after their second vaccine dose. Six (29%) of 21 persons had no laboratory evidence of past or recent SARS-CoV-2 infection, and five of those six (83%) had onset of MIS-C after the second vaccine dose.Implications of all the available evidenceDuring the first nine months of the COVID-19 vaccination program in the United States, >21 million persons aged 12 to 20 years received ≥1 dose of COVID-19 vaccine as of August 31, 2021. This case series describes MIS-C in 21 persons following vaccine receipt during this time period; the majority of persons reported also had evidence of SARS-CoV-2 infection. The surveillance has limitations, but our findings suggest that MIS-C as identified in this report following COVID-19 vaccination is rare. In evaluating persons with a clinical presentation consistent with MIS-C after COVID-19 vaccination it is important to consider alternative diagnoses, and anti-nucleocapsid antibody testing may be helpful. Continued surveillance for MIS-C illness after COVID-19 vaccination is warranted, especially as pediatric COVID-19 vaccination expands. Providers are encouraged to report potential MIS-C cases after COVID-19 vaccination to VAERS.

Adverse Events During Pregnancy Associated With Entecavir and Adefovir: New Insights From a Real-World Analysis of Cases Reported to FDA Adverse Event Reporting System

Background: Due to the embryotoxicity found in animal studies and scarce clinical data in pregnant women, it is still controversial whether entecavir (ETV) and adefovir dipivoxil (ADV) are safe during human pregnancy. This is of paramount importance when counseling pregnant women with hepatitis B virus (HBV) on risks and benefits to their offspring.Objective: To quantify the association between administration of ETV and ADV in pregnant women and occurrence of adverse events (AEs) during pregnancy (AEDP).Methods: Pregnancy reports from the FDA Adverse Event Reporting System (FAERS) were used to perform a retrospective analysis of AEDP associated with ETV or ADV. Disproportionality analysis estimating the reporting odds ratio (ROR) was conducted to identify the risk signals. A signal was defined as ROR value >2, and lower limit of 95% confidence interval (CI)> 1.Results: A total of 1,286,367 reports involving AEDP were submitted to FAERS by healthcare professionals. Of these, there were 547 cases reporting ETV and 242 cases reporting ADV as primary suspected drugs. We found a moderate or strong signal for increased risk of spontaneous abortion when comparing ETV with tenofovir disoproxil fumarate (TDF) and telbivudine (LdT), with RORs equal to 1.58 (95% CI, 1.09–2.30) and 2.13 (95% CI, 1.04–4.36), respectively. However, when the included reports were limited to indication containing HBV infection, no signals for increased AEDP were detected. Futhermore, a strong signal for increased risk of spontaneous abortion was identified in patients with HBV infection when comparing ETV or ADV with lamivudine (LAM), with RORs of 3.55 (95% CI, 1.54–8.18) and 2.85 (95% CI, 1.15–7.08), respectively.Conclusion: We found a strong signal for increased risk of spontaneous abortion in patients with HBV infection taking ETV or ADV, in comparison with those prescribed with LAM. Moreover, no obvious signal association of human teratogenicity with exposure to ETV or ADV was identified in fetuses during pregnancy. Nevertheless, owing to the limitations of a spontaneous reporting database, which inevitably contains potential biases, there is a pressing need for well-designed comparative safety studies to validate these results in clinical practice.

Combining a Pharmacological Network Model with a Bayesian Signal Detection Algorithm to Improve the Detection of Adverse Drug Events

Introduction: Improving adverse drug event (ADE) detection is important for post-marketing drug safety surveillance. Existing statistical approaches can be further optimized owing to their high efficiency and low cost.Objective: The objective of this study was to evaluate the proposed approach for use in pharmacovigilance, the early detection of potential ADEs, and the improvement of drug safety.Methods: We developed a novel integrated approach, the Bayesian signal detection algorithm, based on the pharmacological network model (ICPNM) using the FDA Adverse Event Reporting System (FAERS) data published from 2004 to 2009 and from 2014 to 2019Q2, PubChem, and DrugBank database. First, we used a pharmacological network model to generate the probabilities for drug-ADE associations, which comprised the proper prior information component (IC). We then defined the probability of the propensity score adjustment based on a logistic regression model to control for the confounding bias. Finally, we chose the Side Effect Resource (SIDER) and the Observational Medical Outcomes Partnership (OMOP) data to evaluate the detection performance and robustness of the ICPNM compared with the statistical approaches [disproportionality analysis (DPA)] by using the area under the receiver operator characteristics curve (AUC) and Youden’s index.Results: Of the statistical approaches implemented, the ICPNM showed the best performance (AUC, 0.8291; Youden’s index, 0.5836). Meanwhile, the AUCs of the IC, EBGM, ROR, and PRR were 0.7343, 0.7231, 0.6828, and 0.6721, respectively.Conclusion: The proposed ICPNM combined the strengths of the pharmacological network model and the Bayesian signal detection algorithm and performed better in detecting true drug-ADE associations. It also detected newer ADE signals than a DPA and may be complementary to the existing statistical approaches.