Reporting System Research Articles

Real-world analysis of levetiracetam-associated rhabdomyolysis: insights from the FDA adverse event reporting system

ABSTRACT Background Levetiracetam, a widely prescribed antiseizure medication, is recognized for its broad-spectrum efficacy, good tolerability, and minimal drug interactions. This study examines the association between levetiracetam and rhabdomyolysis, utilizing real-world data from the FDA Adverse Event Reporting System (FAERS) database to further elucidate its safety profile. Methods This study extracted adverse events related to levetiracetam from the FAERS database (Q1 2013 to Q1 2024). Four types of disproportionality analysis identified rhabdomyolysis as a significant adverse even. Logistic regression assessed risk factors, including gender, age, and severity. A Gaussian Mixture Model analyzed the time-to-onset distribution of rhabdomyolysis, while the impact of concomitant medications on its risk was evaluated using Reporting Odds Ratio (ROR). Results Levetiracetam significantly increased rhabdomyolysis risk (ROR = 13.5). Males showed a higher incidence (OR = 2.60). Most adverse events occurred within the first 30 days, with a bimodal onset distribution. Co-administration of antibiotics, antipsychotics, and PPIs elevated the risk while other antiseizure medications did not. Conclusion This study found a significant association between levetiracetam and the risk of rhabdomyolysis, highlighting the need for increased clinical vigilance in this patient population. Future research should focus on elucidating the underlying mechanisms and optimizing clinical guidelines.

Safety assessment of sapropterin dihydrochloride: real-world adverse event analysis based on the FDA adverse event reporting system (FAERS)

ObjectiveSapropterin dihydrochloride is the first drug for the therapy of phenylketonuria, which is a rare disease that occurs one of 10,000–15,000 newborns. As a result, detailed and comprehensive reports on the safety of sapropterin in large, real-world populations are required. The purpose of this study is to undertake a complete analysis of sapropterin’s adverse events (AEs) using the FDA Adverse Event Reporting System (FAERS) database.MethodsWe retrieved reports of adverse events with sapropterin as the principal suspect from FAERS between the first quarter of 2008 and the first quarter of 2024. The Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN) were utilized to detect AE signals.ResultsThe study collected 4,953 suspected AE cases from the FAERS database, with sapropterin as the major suspect. A total of 130 positive signals were obtained utilizing the ROR, PRP, and BCPNN. The FAERS database revealed that common clinical AEs of sapropterin included vomiting, upper respiratory infection, rhinorrhea, and a reduction in amino acid concentrations. Furthermore, we detected probable unexpected adverse events (AEs) using disproportionality analysis, including gastroesophageal reflux disease, flatulence, influenza, ear infection, viral infection, pharyngitis streptococcal, spontaneous abortion, and nephrolithiasis.ConclusionBy analyzing huge amounts of real-world data from the FAERS database, we found potential novel AEs of sapropterin using disproportionate analysis. It is advantageous for healthcare professionals and pharmacists to focus on efficiently managing sapropterin’s high-risk adverse events, improving drug levels in clinical settings, and ensuring patient medication safety.

TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines

BackgroundRecently, machine learning (ML), deep learning (DL), and natural language processing (NLP) have provided promising results in the free-form radiological reports’ classification in the respective medical domain. In order to classify radiological reports properly, a high-quality annotated and curated dataset is required. Currently, no publicly available breast imaging-based radiological dataset exists for the classification of Breast Imaging Reporting and Data System (BI-RADS) categories and breast density scores, as characterized by the American College of Radiology (ACR). To tackle this problem, we construct and annotate a breast imaging-based radiological reports dataset and its benchmark results. The dataset was originally in Spanish. Board-certified radiologists collected and annotated it according to the BI-RADS lexicon and categories at the Breast Radiology department, TecSalud Hospitals Monterrey, Mexico. Initially, it was translated into English language using Google Translate. Afterwards, it was preprocessed by removing duplicates and missing values. After preprocessing, the final dataset consists of 5046 unique reports from 5046 patients with an average age of 53 years and 100% women. Furthermore, we used word-level NLP-based embedding techniques, term frequency-inverse document frequency (TF-IDF) and word2vec to extract semantic and syntactic information. We also compared the performance of ML, DL and large language models (LLMs) classifiers for BI-RADS category classification.ResultsThe final breast imaging-based radiological reports dataset contains 5046 unique reports. We compared K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient-Boosting (GB), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT) and Biomedical Generative Pre-trained Transformer (BioGPT) classifiers. It is observed that the BioGPT classifier with preprocessed data performed 6% better with a mean sensitivity of 0.60 (95% confidence interval (CI), 0.391-0.812) compared to the second best performing classifier BERT, which achieved mean sensitivity of 0.54 (95% CI, 0.477-0.607).ConclusionIn this work, we propose a curated and annotated benchmark dataset that can be used for BI-RADS and breast density category classification. We also provide baseline results of most ML, DL and LLMs models for BI-RADS classification that can be used as a starting point for future investigation. The main objective of this investigation is to provide a repository for the investigators who wish to enter the field to push the boundaries further.

Multiple points of system failure underpin continuous subcutaneous infusion safety incidents in palliative care: A mixed methods analysis.

About 25% of palliative medication incidents involve continuous subcutaneous infusions. Complex structural and human factor issues make these risk-prone interventions. Detailed analysis of how this safety-critical care can be improved has not been undertaken. Understanding context, contributory factors and events leading to incidents is essential. (1) Understand continuous subcutaneous infusion safety incidents and their impact on patients and families; (2) Identify targets for system improvements by learning from recurrent events and contributory factors. Following systematic identification and stratification by degree of harm, a mixed methods analysis of palliative medication incidents involving continuous subcutaneous infusions comprising quantitative descriptive analysis using the PatIent SAfety (PISA) classification system and qualitative narrative analysis of free-text reports. Palliative medication incidents (n = 7506) reported to the National Reporting and Learning System, England and Wales (2016-2021). About 1317/7506 incidents involved continuous subcutaneous infusions with 943 (72%) detailing harms. Primary incidents (most proximal to patient outcomes) leading to inappropriate medication use (including not using medication when it was needed) were underpinned by breakdowns in three major medication processes: monitoring and supply (405, 31%), administration (383, 29%) and prescribing (268, 20%). Recurring contributory factors included discontinuity of care within and between settings, inadequate time, inadequate staffing and unfamiliarity with protocols. Psychological harms for patients and families were identified. System infrastructure is needed to enable timely supply of medication and equipment, effective coordinated use of continuous subcutaneous infusions, communication and continuity of care. Training is needed to improve incident descriptions so these pinpoint precise targets for safer care.

Lessons learned: strategies for implementing and the ongoing use of LI-RADS in your practice.

The establishment of the Liver Imaging Reporting and Data System (LI-RADS) in 2011 provided a comprehensive approach to standardized imaging, interpretation, and reporting of liver observations in patients diagnosed with or at risk for hepatocellular carcinoma (HCC). Each set of algorithms provides criteria pertinent to the various components of HCC management including surveillance, diagnosis, staging, and treatment response supported by a detailed lexicon of terms applicable to a wide range of liver imaging scenarios. Before its widespread adoption, the variability in the terminology of diagnostic criteria and definitions of imaging features led to significant challenges in patient management and made it difficult to replicate findings or apply them consistently. The integration of LI-RADS into the clinical setting has enhanced the efficiency and clarity of communication between radiologists, referring providers, and patients by employing a uniform language that averts miscommunications. LI-RADS has been strengthened with its integration into the American Association for Study of Liver Diseases practice guidelines. We will provide the background on the initial development of LI-RADS and reasons for development to serve as a starting point for conveying the system's benefits and evolution over the years. We will also suggest strategies for the implementation and maintenance of a LI-RADS program will be discussed.

Diagnostic Performance and Clinical Impact of Photon-Counting Detector Computed Tomography in Coronary Artery Disease.

Photon-counting detector-computed tomography (PCD-CT) has emerged as a promising technology, offering improved spatial resolution. This study aimed to evaluate the clinical impact and diagnostic performance of PCD-CT vs conventional energy-integrating detector computed tomography (EID-CT) for obstructive coronary artery disease (CAD). From 2022 to 2023, we retrospectively identified 7,833 consecutive patients who underwent clinically indicated coronary computed tomography angiography (CCTA) at a single center, with either PCD-CT (n=3,876; NAEOTOM Alpha [Siemens Healthineers]) or EID-CT (n=3,957; Revolution Apex 256 [GE HealthCare] or Aquilion ONE ViSION 320 [Canon Medical Systems]) scanners. Subsequent invasive coronary angiography (ICA) and percutaneous or surgical revascularization were performed as part of routine clinical care. Among those referred for ICA after coronary CTA, the presence of obstructive CAD in each vessel was determined by coronary CTA (severe stenosis on visual assessment per the Coronary Artery Disease Reporting and Data System) and ICA (≥50% diameter stenosis on quantitative coronary angiography) in a blinded fashion. The diagnostic performance of EID-CT and PCD-CT was compared by using quantitative coronary angiography as the reference standard. Patients who underwent PCD-CT were less frequently referred to subsequent ICA than those undergoing EID-CT (9.9% vs 13.1%; P< 0.001). Among those who underwent ICA, revascularization was more frequently performed in the PCD-CT group than in the EID-CT group (43.4% vs 35.5%; P=0.02). In the vessel-level analysis (n=1,686), specificity (98.0% vs 93.0%; P< 0.001), positive predictive value (83.3% vs 63.0%; P=0.002), and diagnostic accuracy (97.2% vs 92.8%; P< 0.001) were improved by PCD-CT. Sensitivity (90.9% vs 90.7%; P=0.95) and negative predictive value (98.9% vs 98.7%; P=0.83) for obstructive CAD were similar between the PCD-CT and EID-CT groups, respectively. PCD-CT exhibited excellent diagnostic performance for detecting obstructive CAD. Compared with patients undergoing conventional EID-CT, fewer patients were referred to ICA after PCD-CT, but those referred were more likely to undergo revascularization.

Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography

ObjectiveThis study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).MethodsA retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.ResultsThe T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p &amp;gt; 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p &amp;lt; 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466–11.667, cutoff = 3, p &amp;lt; 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003–1.009, cutoff = 29.65 mm3, p &amp;lt; 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387–1.918).ConclusionAI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.

Comparison in prostate cancer diagnosis with PSA 4–10 ng/mL: radiomics-based model VS. PI-RADS v2.1

BackgroundTo evaluate accuracy of MRI-based radiomics in diagnosing prostate cancer (PCa) in patients with PSA levels between 4 and 10 ng/mL and compare it with the latest Prostate Imaging Reporting and Data System (PI-RADS v2.1) score.Methods221 patients with prostate lesions and PSA levels in 4–10 ng/mL, including 154 and 67 cases in the training and validation groups. Pathological confirmation of all patients was accomplished by the use of MRI-TRUS fusion targeted biopsy or systematic transrectal ultrasound (TRUS) guided biopsy. 851 radiomic features were extracted from each lesion of ADC and T2WI images. The least absolute shrinkage and selection operator (LASSO) regression algorithm and logistic regression were employed to select features and build the ADC and T2WI model. The combined model was obtained based on the ADC and T2WI features. The clinical benefit and diagnostic accuracy of the three radiomics models and PI-RADS v2.1 score were evaluated.Results10 radiomic features were ultimately selected from the ADC images, 13 from the T2WI images and 7 from the combined models. The ADC, T2WI and combined models achieved satisfactory diagnostic accuracy in the training [AUC:0.945 (ADC), 0.939 (T2WI), 0.979 (combined)] and validation groups [AUC: 0.942 (ADC), 0.943 (T2WI), 0.959 (combined)], which was significantly higher than those in PI-RADS v2.1 model (0.825 for training cohort and 0.853 for validation cohort). Compared with the PI-RADS v2.1 score, the three radiomics models generated superior PCa diagnostic performance in both the training (p = 0.002, p = 0.005, p < 0.001) and validation groups (p = 0.045, p = 0.035, p = 0.015).ConclusionRadiomics based on ADC and T2WI images can better identify PCa in patients with PSA 4–10 ng/mL, and MRI-based radiomics significantly outperforms the PI-RADS v2.1 score.Clinical trial numberNot applicable.

Oral complications during the two waves of COVID-19 pandemic in China: a prospective cross-section analysis of 510 cases using questionnaire

BackgroundOral complications such as parageusia, xerostomia, and oral ulcers are frequently observed in patients with COVID-19. The aim of this study was to understand the oral complications and their influencing factors during the two waves of the COVID-19 pandemic in China between December 2022 and May 2023.MethodsA total of 347 patients during the COVID-19 pandemic from December 2022 and January 2023 and 163 patients during the second COVID-19 pandemic were recruited through the Disease Prevention and Control Reporting System. Data on oral complications were collected via researcher-made questionnaires.ResultsDuring both pandemic periods, more than 50% of the subjects developed oral complications. Specifically, the incidence of parageusia, pathological tongue coating, and xerostomia all exceeded 20%, while the incidence of oral ulcers surpassed 7%. Patients with allotriosmia and xerostomia had a significantly higher likelihood of developing other oral complications. Hospitalized patients requiring supplemental oxygen had a significantly higher incidence of oral complications.ConclusionsThe results of our study underline that oral complications during the COVID-19 pandemic are prevalent, and their incidence has not decreased during the subsequent pandemic. In the context of the ongoing global spread of COVID-19, this study lays the foundation for physicians in recognizing and managing oral complications, which will improve the oral health of communities globally.

Using data fusion with multiple imputation to correct for misclassification in self-reported exposure: a case-control study of cannabis use and homicide victimization

BackgroundCannabis use has been causally linked to violent behaviors in experimental and case studies, but its association with homicide victimization has not been rigorously assessed through epidemiologic research.MethodsWe performed a case-control analysis using two national data systems. Cases were homicide victims from the National Violent Death Reporting System (NVDRS), and controls were participants from the National Survey on Drug Use and Health (NSDUH). While the NVDRS contained toxicological testing data on cannabis use, the NSDUH only collected self-reported data, and thus the potential misclassification in the self-reported data needed to be corrected. We took a data fusion approach by concatenating the NSDUH with a third data system, the National Roadside Survey of Alcohol and Drug Use by Drivers (NRS), which collected toxicological testing and self-reported data on cannabis use for drivers. The data fusion approach provided multiple imputations (MIs) of toxicological testing results on cannabis use for the participants in the NSDUH, which were then used in the case-control analysis. Bootstrap was used to obtain valid statistical inference.ResultsThe analyses revealed that cannabis use was associated with 3.55-fold (95% CI: 2.75–4.35) increased odds of homicide victimization. Alcohol use, being Black, male, aged 21–34 years, and having less than a high school education were also significantly associated with increased odds of homicide victimization.ConclusionsCannabis use is a major risk factor for homicide victimization. The data fusion with MI method is useful in integrative data analysis for harmonizing measures between different data sources.

The Association Between Dextromethorphan/Bupropion with Alcohol and Substance Misuse: Reports to the Food and Drug Administration Adverse Event Reporting System (FAERS).

Dextromethorphan/bupropion (DXM/BUP) received Food and Drug Administration (FDA) approval for the treatment of adults with major depressive disorder (MDD) in August 2022. This combination is not known to have abuse liability and is not currently scheduled by the Drug Enforcement Administration (DEA). Notwithstanding, dextromethorphan is a drug of abuse. Herein, we sought to determine whether DXM/BUP has alcohol and/or substance misuse liability. We evaluated spontaneous reports of terms such as "alcohol problem, alcoholism, alcohol abuse, substance dependence, substance use disorder (SUD), substance abuse, drug dependence, drug use disorder and drug abuse" in the FDA Adverse Event Reporting System (FAERS). The FAERS is a spontaneous reporting database of adverse events submitted to the FDA. We performed a comparative assessment of the alcohol and/or substance misuse liability of DXM/BUP since its market authorization in August 2022, using acetaminophen as the control. Dextromethorphan served as the upper-bound reference point. Our findings showed that, since August 2022, dextromethorphan had a significant reporting odds ratio (ROR) for "drug abuse." In contrast, DXM/BUP did not have a significant ROR for any of the categories of alcohol and/or substance misuse evaluated. Limitations of our findings derive largely from the limitations of the FAERS and its data capture method. The absence of alcohol or substance misuse reported to the FAERS with DXM/BUP accords with the lack of evidence of abuse liability prior to FDA approval and its non-scheduling by the DEA.

Identifying facilitators and barriers to implementing the Say Something Anonymous Reporting System in Miami-Dade County, USA: a qualitative study

BackgroundMultiple anonymous reporting systems (ARS) have been implemented to prevent school violence and improve school climate. This study examines the facilitators and barriers to implementation and the role and usability...

Characterising fire or burn-related fatalities in the USA using the National Violent Death Reporting System, 2003–2020

ObjectivesIntentional fire or burn-related deaths are rare in the USA compared with some countries, but do occur, and our knowledge of their epidemiology is limited. The objective of this study...

CT/MRI technical pitfalls for diagnosis and treatment response assessment using LI-RADS and how to optimize.

Hepatocellular carcinoma (HCC), the most common primary liver cancer, is a significant global health burden. Accurate imaging is crucial for diagnosis and treatment response assessment, often eliminating the need for biopsy. The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation and reporting of liver imaging for diagnosis and treatment response assessment, categorizing observations using defined categories that are based on the probability of malignancy or post-treatment tumor viability. Optimized imaging protocols are essential for accurate visualization and characterization of liver findings by LI-RADS. Common technical pitfalls, such as suboptimal postcontrast phase timing, and MRI-specific challenges like subtraction misregistration artifacts, can significantly reduce image quality and diagnostic accuracy. The use of hepatobiliary contrast agents introduces additional challenges including arterial phase degradation and suboptimal uptake in advanced cirrhosis. This review provides radiologists with comprehensive insights into the technical aspects of liver imaging for LI-RADS. We discuss common pitfalls encountered in routine clinical practice and offer practical solutions to optimize imaging techniques. We also highlight technical advances in liver imaging, including multi-arterial MR acquisition and compressed sensing. By understanding and addressing these technical aspects, radiologists can improve accuracy and confidence in the diagnosis and treatment response assessment for hepatocellular carcinoma.

Adverse events analysis of Relugolix (Orgovyx®) for prostate cancer based on the FDA Adverse Event Reporting System (FAERS).

Due to the limitations of clinical trials, some delayed and rare adverse events (AEs) may remain undetected, and safety information can be supplemented through post-market data analysis. This study aims to comprehensively analyze the AEs associated with Relugolix (Orgovyx®) using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Relugolix (Orgovyx®) therapy. Data of Relugolix (Orgovyx®) were collected from the FAERS database covering the period from the fourth quarter of 2020 to the third quarter of 2023. Disproportionality analysis was performed by calculating the reporting odds ratios (ROR), proportional reporting ratio (PRR), Bayesian analysis confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS) to detect positive signals. Totally, 5,382,189 reports were collected from the FAERS database, 4,397 reports of Relugolix (Orgovyx®) were identified as the 'primary suspected (PS)' AEs. Relugolix (Orgovyx®) induced AEs occurred in 26 organ systems. 58 significant disproportionality preferred terms (PTs) satisfying with the four algorithms were retained at the same time. Unexpected significant AEs such as Pollakiuria, and Prostatic specific antigen increased also occur. The median time of onset was 60 days. The majority of the AEs occurred within the first 30 days after Relugolix (Orgovyx®) initiation. Common AEs included Hot flush, Fatigue, Asthenia, Constipation, and Myalgia. These AEs should be focused on when using the drug to avoid serious consequences. In addition, the study results also suggested that the drug may exist Pollakiuria, Prostatic specific antigen increased and other AEs not mentioned in the manual, to supplement the AEs in the manual. This study is helpful for clinicians and pharmacists to improve their understanding of Relugolix (Orgovyx®) related AEs, and take timely prevention and treatment measures to ensure drug safety for patients.

Efficiency Tax Reporting System as Variable Moderation for Digitalization of Tax Compliance

The goal of this research is to examine the impact of tax digitalization, such as e-filling, and e-billing, on the efficiency of individual taxpayer reporting and tax compliance in the 4.0 era. The presence of tax digitalization as a type of tax administration modernization makes it easier for the general public, particularly taxpayers, to carry out their obligation. This study is a quantitative research. This study's variables include one dependent variable, two independent variables and one variabel moderating. The dependent variable is the tax compliance, while the independent variables are the use e-filling, and e-billing and the moderating variables is efficiency of taxpayer reporting. The population in this study is Individual taxpayers in Kuningan Regency, West Java. The samples in this research are 50 respondents that take the questionnaire with Likert scale as the measurement. This study used incidental sampling, with the research instrument being an online questionnaire distributed via social media. The data analysis method in this study uses Moderated Regression Analysis (MRA). According to the findings of this study efficiency tax system has not been proven to moderate the relationship between e-filling and e-billing with the tax compliance.

Cardiovascular adverse events associated with triptans for treatment of migraine: a pharmacovigilance study of the FDA adverse event reporting system (FAERS).

The purpose of this study was to determine the relationship between triptans (sumatriptan, rizatriptan, and zolmitriptan) and cardiovascular (CV) adverse events with data from the FDA Adverse Event Reporting System (FAERS). FAERS database was used to collect data on triptans from 1997 to 2023. Disproportionality methods were utilized to quantify triptan-associated CV events and to identify the potential risk. The reporting odds ratio was used to identify the risk signals. CV outcomes related to age, sex, clinical results, and other factors were also examined for triptans; 820 reports involving the triptans were recognized as CV adverse events out of total of 12 699 reports that were gathered from on FAERS database. Women reported more CV adverse events with rizatriptan and zolmitriptan as compared to men. The CV adverse event risk was highest among individuals aged 18-64. Clinical outcome analysis showed that sumatriptan carries a higher CV risk than rizatriptan and zolmitriptan, and most deaths and serious cases have been documented for sumatriptan. The patients prescribed sumatriptan or zolmitriptan were at a higher risk of reporting CV events for chest pain and chest discomfort, compared to rizatriptan. This finding may provide support for the clinical observation and risk evaluation of triptan treatment.

Leveraging Multiple Administrative Data Sources to Reduce Missing Race and Ethnicity Data: A Descriptive Epidemiology Cross-Sectional Study of COVID-19 Case Relative Rates.

Understanding race and ethnicity (RE) differentials improves health outcomes. However, RE data are consistently missing from electronic laboratory reports, the primary source of COVID-19 case metrics. We addressed the missing RE differentials and compared vaccinated and unvaccinated cases from March 1, 2020, to May 30, 2023, in New York State (NYS), excluding New York City. This descriptive epidemiology cross-sectional study linked the NYS Electronic Clinical Laboratory Reporting System (ECLRS) with NYS Immunization Information System (NYSIIS) to address the missing RE data in the ECLRS system. The primary metric was the COVID-19 case relative risk (RR) for each RE relative to white individuals. There were 4,212,741 COVID-19 cases with 39% (1,624,818) missing RE data in ECLRS; missing RE data declined to 17% (726,023) after matching with NYSIIS. For those aged 65years or older (after matching), 42% were missing in 2020, which declined by 17% by 2023. In May 2021, COVID-19 RRs for vaccinated individuals were 1.09 (95% CI 0.90-1.32), 1.11 (95% CI 0.87-1.43), 1.13 (95% CI 0.93-1.39), and 1.89 (95% CI 1.01-3.52), and for unvaccinated individuals were 1.73 (95% CI 1.66-1.82), 0.84 (95% CI 0.78-0.92), 3.10 (95% CI 2.98-3.22), and 3.49 (95% CI 3.05-3.98) respectively for Hispanic, Asian/Pacific Islander, Black people, and American Indian/Alaska Native individuals. Matching case data with vaccine registries reduce missing RE data for COVID-19 cases. Disparity was lower in vaccinated than in unvaccinated individuals indicating that vaccination mitigated RE disparities early in the pandemic. This underscores the value of interoperable systems with automated matching for disparity analyses.

Cardiovascular adverse events associated with norepinephrine-dopamine reuptake inhibitors: a pharmacovigilance study of the FDA Adverse Event Reporting System.

Norepinephrine-dopamine reputake inhibitors (NDRIs), including bupropion, methylphenidate, atomoxetine, and reboxetine, are commonly prescribed for psychiatric disorders such as narcolepsy, attention-deficit/hyperactivity disorder, and depression. Cardiovascular adverse events have been reported to the FDA despite their effectiveness. This pharmacovigilance study analyzed cardiovascular adverse events associated with NDRIs using the FDA Adverse Event Reporting System data from January 2004 to December 2021. A retrospective analysis of adverse event reports was conducted, employing time-trend analysis and disproportionality evaluation to assess cardiovascular risks. Bupropion had the greatest reported odds ratios (RORs) for tachycardia (ROR=4.2, 95% CI: 4.0-4.4) and hypertension (ROR=3.5, 95% CI: 3.3-3.7), while methylphenidate showed greater ROR for arrhythmias (ROR=2.8, 95% CI: 2.6-3.0) and palpitations (ROR=3.1, 95% CI: 2.9-3.3). Reboxetine had signals for palpitations (ROR=3.0, 95% CI: 2.8-3.2) and myocardial infarction (ROR=2.7, 95% CI: 2.5-2.9), whereas atomoxetine revealed signals for hypertension (ROR=2.9, 95% CI: 2.7-3.1) and syncope (ROR=2.5, 95% CI: 2.3-2.7). Time-trend analysis revealed temporal variability in the cardiovascular risks connected with NDRIs. Our research elucidates cardiovascular safety profiles for NDRIs, highlighting the necessity for continuous pharmacovigilance. The observed variations in adverse events emphasize the need for ongoing surveillance to mitigate potential cardiovascular risks and enhance patient safety and treatment outcomes.

Adverse events associated with carbamazepine: a pharmacovigilance study using the FDA Adverse Event Reporting System

ABSTRACT Introduction Carbamazepine (CBZ) is a commonly used antiseizures medications (ASM), but its adverse drug reactions (ADRs) can impact the successful management of epilepsy. There are currently limited systematic studies on ADRs related to CBZ, necessitating further investigation. Areas covered Using the FDA Adverse Event Reporting System (FAERS) database , we extracted reports where CBZ was the primary suspect, conducting subgroup analyses stratified by sex and age. Four risk signal detection methods ROR, PRR, BCPNN, and EGBM were employed to systematically analyze the ADRs associated with CBZ. Expert opinion In the epilepsy population, ADRs related to CBZ often involve examinations, hereditary disorders, and infections. Subgroup analysis showed differences in ADR signals among male, female, elderly, and young patients. Human Herpesvirus 6 Infection and Dermatitis Exfoliative were consistent CBZ-induced ADRs, unaffected by age or sex. The study also identified previously overlooked ADRs such as bone metabolism abnormalities, ocular toxicity, and ototoxicity. Many ADRs linked to CBZ remain underreported. It is crucial to enhance monitoring and information dissemination about CBZ use in epileptic patients. Adjusting medication regimens for high-risk individuals, potentially incorporating acupuncture, traditional Chinese medicine, or alternative anti-seizure medications, should be considered when necessary.