UNASSIGNED: Few studies have analysed oxaliplatin-induced adverse events (ADEs) in the immune system and skin and subcutaneous tissues through pharmacovigilance. We used this approach to analyse the risk of such ADEs when oxaliplatin combined with immune checkpoint inhibitors (ICIs).
UNASSIGNED: We evaluated the association between oxaliplatin and ADEs in the immune system and skin and subcutaneous tissues using the reporting odd ratio (ROR) for mining the ADE report signals in the FDA Adverse Event Reporting System database. Risk factors were analyzed using a binary logistic regression analysis using the sex and age of the patients.
UNASSIGNED: There were 40,474 reports of oxaliplatin as primary suspect drug or second suspect drug. The signal intensities of ADEs such as type II hypersensitivity, type I hypersensitivity, type III immune complex-mediated reaction, anaphylactoid shock and cytokine release syndrome were high in PTs classified by SOC as immune system disorders; in the PTs classified as skin and subcutaneous tissue disorders by SOC, the signal intensities of ADEs such as skin toxicity, skin reaction, rash maculo-papular and skin fissures were higher. In the risk assessment between the two groups, rash showed an increased risk in the oxaliplatin-ICI group, with an OR of 1.96. Nivolumab in combination with oxaliplatin had an OR of 2.196 and an adjusted OR of 2.231. Combined with pembrolizumab, OR was 2.762 and the adjusted OR was 2.678.
UNASSIGNED: Type II hypersensitivity shows a stronger pharmacovigilance signal. Oxaliplatin in combination with nivolumab or pembrolizumab has been shown to increase the risk of rash.

OBJECTIVE: To explore adverse event (AE) signals of Ceftazidime/avibactam (CZA) based on the FDA Adverse Event Reporting System (FAERS) database.
METHODS: AE reports primarily associated with CZA were retrieved from the FAERS database from the second quarter of 2015 to the second quarter of 2023. Signal detection was conducted using the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) methods.
RESULTS: A total of 750 AEs reports with CZA as the preferred suspected drug were obtained, identifying 66 preferred terms (PTs) involving 24 system organ classes (SOCs). Besides, the AEs already mentioned in the drug label, this study also revealed some new, clinically valuable potential AEsignals, such as Cholestasis (n = 14, ROR 29.39, PRR 29.15, IC 3.34, EBGM 29.11), Drug-induced liver injury (n = 8, ROR 9.05, PRR 9.01, IC 2.25, EBGM 9.01), Hepatocellular injury (n = 7, ROR 13.90, PRR 13.84, IC 2.41, EBGM 13.63), Haemolytic anaemia (n = 5, ROR 24.29, PRR 24.22, IC 2.42, EBGM 40.53), etc. Additionally, AE signals with higher intensity were identified, such as Hypernatraemia (n = 5, ROR 40.73, PRR 40.61, IC 2.31, EBGM 24.19), Toxic epidermal necrolysis (n = 4, ROR 11.58, PRR 11.55, IC 1.89, EBGM 11.54). Therefore, special vigilance for these potential AEs is warranted when using CZA clinically.
CONCLUSIONS: This study highlights the potential AEs and risks associated with the clinical use of CZA, particularly the risks related to Cholestasis, Drug-induced liver injury, Haemolytic anaemia, Hypernatraemia, and Toxic epidermal necrolysis.

OBJECTIVE: This study aims to conduct an exhaustive evaluation of Vilazodone\'s safety in clinical application and to unearth the potential adverse event (AE) risks associated with its utilization based on FDA Adverse Event Reporting System (FAERS) database.
METHODS: This research employed data spanning from the first quarter of 2011 to the third quarter of 2023 from the FAERS database. Various signal detection methodologies, including the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM), were utilized to ascertain the correlation between Vilazodone and specific AEs.
RESULTS: The study compiled a total of 17,439,268 reports of drug AEs, out of which 5,375 were related to Vilazodone. Through signal mining, 125 Preferred Terms (PTs) encompassing 27 System Organ Classes (SOCs) were identified. The findings indicated a higher prevalence among females and patients within the 45 to 65 age bracket. The principal categories of AEs included Psychiatric disorders, Nervous system disorders, and Gastrointestinal disorders, with prevalent incidents of Diarrhoea, Nausea, and Insomnia. Moreover, the study identified robust signals of novel potential AEs, notably in areas such as sleep disturbances (Sleep paralysis, Hypnagogic hallucination, Rapid eye movements sleep abnormal, Sleep terror, Terminal insomnia, Tachyphrenia), sexual dysfunctions (Female orgasmic disorder, Orgasm abnormal, Disturbance in sexual arousal, Spontaneous penile erection, Anorgasmia, Sexual dysfunction, Ejaculation delayed), and other symptoms and injuries (Electric shock sensation, Violence-related symptom, Gun shot wound).
CONCLUSIONS: Although Vilazodone presents a positive prospect in the management of MDD, the discovery of AEs linked to its use, particularly the newly identified potential risks such as sleep and sexual dysfunctions, necessitates heightened vigilance among clinicians.

UNASSIGNED: Despite its widespread use, the adverse effects (AEs) of memantine have not been well documented, and there is a need to find new ways to analyze the AEs of memantine.
UNASSIGNED: AEs in which the primary suspected drug was memantine were retrieved from the FAERS database. The proportional report ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) were used to detect potential positive signals between memantine and AEs. SAS, MySQL, EXCEL, and R language software were used for data processing and statistical analysis.
UNASSIGNED: This study gathered a total of 5808 reports of AEs associated with memantine. Of these reports, a greater proportion of female patients (51.17%) than male patients (36.33%) had AEs. The AEs reported by FAERS were mainly in psychiatric category (n = 2157, IC025 = 2.69), various neurologic disorders (n = 1608, IC025 = 2.04), systemic disorders and various site reactions (n = 842, IC025 = 1.29). Unexpected ocular adverse events have been reported, ophthalmic vein thrombosis (n = 4, IC025 = 3.47) and scleral discolouration (n = 7, IC025 = 3.1), which may worsen glaucoma.
UNASSIGNED: This study observed conceivable new AEs signals and may supply important assist for scientific monitoring and threat identification of memantine.

UNASSIGNED: This study aimed to explore a comprehensive empirical investigation and assess SCARs related to valaciclovir or acyclovir based on FAERS database from FDA, thus providing a theoretical foundation for the rational application of drugs in clinic.
UNASSIGNED: SCARs reports relevant to valaciclovir or acyclovir were searched in FAERS database from the 2004 Q1 to 2023 Q2. These data were further mined by a proportional analysis and Bayesian approach to detect signals of SCARs caused by two drugs. Meanwhile, the clinical characteristics, onset time, correlation, and stratification analysis of the two drugs in SCARs were analyzed.
UNASSIGNED: Both drugs exhibited positive signals for drug reaction with DRESS, AGEP, TEN, SJS-TEN overlap and SJS. The median onset time of SCARs caused by valaciclovir or acyclovir was 30 days vs 10 day for DRESS, 11 days vs 9 days for AGEP, 17 days vs 12 days (TEN) and 12 days vs 8 days (SJS). Excluding the effect of combinational drugs, there was an association between the two antiviral drugs and SCARs.
UNASSIGNED: By analyzing the FAERS database, the risk trends of SCARs caused by valaciclovir or acyclovir have been identified, providing valuable insights to recognize various types of SCARs in clinics.

OBJECTIVE: Central nervous system adverse events (AEs) occur when oxycodone is used in combination with benzodiazepines, antidepressants and anticonvulsants. There have been no reports of central nervous system AEs with oxycodone alone or in combination with oxycodone. Based on USA Food and Drug Administration Adverse Event Reporting System (FAERS) data, this study aims to explore the risk signals of central nervous system AEs with oxycodone alone or in combination with benzodiazepines, antidepressants and anticonvulsants, and to provide a reference for the safe and rational use of this drug.
METHODS: Extracted AEs data from the FAERS for oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants from Q1 2004 to Q2 2021. The risk signal mining analysis of AEs was performed using the proportional imbalance method and Bayesian method. Number of reports ≥3 and lower 95% CI limit of reporting odds ratio (ROR)>1; number of reports ≥3, proportional reporting ratio (PRR)≥2 and χ2≥4; lower information components (IC) lower 95% CI limit (IC025)>0; empirical Bayes geometric mean (EBGM) lower 95% CI limit (EBGM05)>2, and N>0 were defined as positive signals.
RESULTS: A total of 5 793 reports of central nervous system AEs with oxycodone alone were tapped, and 366, 622, and 740 reports of combined benzodiazepines, antidepressants, and anticonvulsants, respectively. Consumers and physicians were the main reporting population. The age distribution of oxycodone alone was mainly from 61 to 80 years old. The age distribution of oxycodone in combination with related drugs was mainly from 46 to 60 years old. The risk of AEs was greater in women than in men, and the United States was the predominant reporting country. Oxycodone alone was strongly associated with myoclonus [ROR=2.92, 95% CI 2.28 to 3.76); PRR=2.92, χ2(77.49); IC=1.52, IC025(0.65); EBGM=2.89, EBGM05(2.33)], delirium [ROR=4.69, 95% CI 4.24 to 5.21; PRR=4.66, χ2(1 052.64); IC=2.17, IC025(1.81); EBGM=4.50, EBGM05 (4.13)], mental disorder [ROR=2.95, 95% CI 2.53 to 3.44; PRR=2.94, χ2(206.93); IC=1.56, IC025(0.96); EBGM=2.95, EBGM05(2.58)], and acute central respiratory depression [ROR=2.87, 95% CI 2.68 to 3.08); PRR=2.82, χ2(971.62); IC=1.52, IC025(1.33), EBGM=2.87, EBGM05 (2.76)]. Combination of benzodiazepines was most strongly associated with mental disorder [ROR=10.08, 95% CI 9.38 to 10.78; PRR=9.90, χ2(64.06); IC=3.33, IC025 (1.65); EBGM=10.08, EBGM05(5.61)], and tremor [ROR=3.09, 95% CI 2.76 to 3.42); PRR=3.08, χ2(48.93); IC=1.63, IC025 (1.17); EBGM=3.09, EBGM05(2.34)]. Combination of antidepressants was most strongly associated with delirium [ROR=13.23, 95% CI 12.23 to 14.23; PRR=12.87, χ2(43.86); IC=3.69, IC025(1.36); EBGM=12.23, EBGM05 (5.32)] and somnolence [ROR=6.74, 95% CI 6.15 to 7.33); PRR=6.73, χ2(53.42); IC=2.75, IC025(1.52); EBGM=6.73, EBGM05(4.10)]. Combination of anticonvulsants was most strongly associated with myoclonus [ROR=17.89, 95% CI 17.46 to 18.32; PRR=17.72, χ2(971.39); IC=4.16, IC025(2.70); EBGM=17.89, EBGM05(12.46)] and delirium [ROR=4.86, 95% CI 4.45 to 5.27); PRR=4.82, χ2(69.49); IC=2.28, IC025 (1.51); EBGM=4.86, EBGM05(3.44)].
CONCLUSIONS: Based on pharmacovigilance studies of the FAERS database, clinical medication monitoring of oxycodone alone and in combination with benzodiazepines, antidepressants, and anticonvulsants should be strengthened to be alert to the occurrence of central nervous system-related AEs.
目的: 羟考酮与苯二氮䓬类药物、抗抑郁药及抗惊厥药联用时，会出现中枢神经系统不良事件(adverse events，AEs)。本研究基于美国食品药品监督管理局不良事件报告系统(Food and Drug Administration Adverse Event Reporting System，FAERS)数据，挖掘羟考酮单用及联用苯二氮䓬类药物、抗抑郁药和抗惊厥药的中枢神经系统AEs的风险信号，为该药的安全合理使用提供参考。方法: 提取FAERS中2004年第1季度至2021年第2季度的单用羟考酮及联用苯二氮䓬类药物、抗抑郁药及抗惊厥药的AEs数据，采用比例失衡法及贝叶斯法进行AEs风险信号挖掘分析。报告数≥3且报告比值比(reporting odds ratio，ROR) 95% CI下限>1；报告数≥3，比例报告比值比(proportional reporting ratio，PRR)≥2且χ2值≥4；信息成分(information components，IC)95% CI下限(IC025)>0；经验贝叶斯几何平均数(empirical Bayes geometric mean，EBGM)95% CI下限(EBGM05)>2，N>0被定义为阳性信号。结果: 共挖掘到单用羟考酮中枢神经系统AEs报告数为5 793例，合用苯二氮䓬类药物、抗抑郁药、抗惊厥药报告数分别为366、622、740例。消费者和医师是主要上报人群。单用羟考酮的年龄主要分布在61~80岁。羟考酮联用相关药物年龄主要分布在46~60岁。女性发生AEs的风险大于男性，报告国家以美国为主。单用羟考酮与肌阵挛[ROR=2.92，95% CI 2.28~3.76；PRR=2.92，χ2(77.49)；IC=1.52，IC025(0.65)；EBGM=2.89，EBGM05(2.33)]、谵妄[ROR=4.69，95% CI 4.24~5.21；PRR=4.66，χ2(1052.64)；IC=2.17，IC025(1.81)；EBGM=4.50，EBGM05(4.13)]、精神异常[ROR=2.95，95% CI 2.53~3.44；PRR=2.94，χ2(206.93)；IC=1.56，IC025(0.96)，EBGM=2.95，EBGM05(2.58)]、急性中枢性呼吸抑制[ROR=2.87，95% CI 2.68~3.08；PRR=2.82，χ2(971.62)；IC=1.52，IC025(1.33)；EBGM=2.87，EBGM05(2.76)]的关联性强；联用苯二氮䓬类药物与精神异常[ROR=10.08，95% CI 9.38~10.78；PRR=9.90，χ2(64.06)；IC=3.33，IC025(1.65)；EBGM=10.08，EBGM05(5.61)]、震颤[ROR=3.09，95% CI 2.76~3.42；PRR=3.08，χ2(48.93)；IC=1.63，IC025(1.17)；EBGM=3.09，EBGM05(2.34)]的关联性最强；联用抗抑郁药与谵妄[ROR=13.23，95% CI 12.23~14.23；PRR=12.87，χ2(43.86)；IC=3.69，IC025(1.36)；EBGM=12.23，EBGM05(5.32)]、嗜睡[ROR=6.74，95% CI 6.15~7.33；PRR= 6.73，χ2(53.42)；IC=2.75，IC025(1.52)；EBGM=6.73，EBGM05(4.10)]的关联性最强；联用抗惊厥药与肌阵挛[ROR=17.89，95% CI 17.46~18.32；PRR=17.72，χ2(971.39)；IC=4.16，IC025(2.70)；EBGM=17.89，EBGM05(12.46)]、谵妄[ROR=4.86，95% CI 4.45~5.27；PRR=4.82，χ2(69.49)；IC=2.28，IC025(1.51)；EBGM=4.86，EBGM05(3.44)]的关联性最强。结论: 基于对FAERS数据库的药物警戒研究，应加强羟考酮单用及联用苯二氮䓬类药物、抗抑郁药、抗惊厥药的临床用药监测，警惕中枢神经系统相关AEs的发生。.

UNASSIGNED: Programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) inhibitors have reformed the treatment landscape for various malignancies and improved prognosis of patients. However, they also lead to events that although rare may prove to be fatal.
UNASSIGNED: Data from July 2014 to June 2022 based on FDA Adverse Event Reporting System (FAERS) were analyzed. The signal index reporting odds ratio (ROR) was used to evaluate the correlation between cardiac AEs and given medications. The indications and the median time to onset (TTO) of different PD-1/PD-L1 inhibitors were compared.
UNASSIGNED: Cardiac AEs are rare but may be fatal with particular profiles in primary tumor, onset time, and especially gender. We identified 11,538 reports that were related to cardiotoxicity of PD-1/PD-L1 inhibitors, in which 178 different preferred terms (PTs) were distinguished, and nivolumab reported the most PTs with signal. All targeted medications showed signals in myocardial disorders and pericardial disorders, which tend to occur in the first 1-2 months. Non-small cell neoplasm was the top and common indication during anti-PD-1 or anti-PD-L1 therapy with cardiotoxicity.
UNASSIGNED: This study could help early diagnosis and surveillance of ICIs-related cardiotoxicity.

BACKGROUND: Previous reports on daptomycin\'s adverse drug reactions (ADRs) have been insufficient, often because of limited data. Pharmacovigilance risk signal detection is innovative and has been applied to the safety monitoring and reevaluation of drugs post-marketing.
OBJECTIVE: The study aimed to promote safe daptomycin prescribing by mining and evaluating the daptomycin ADR signals from the US Food and Drug Administration Adverse Event Reporting System (FAERS).
METHODS: A disproportionality analysis (reporting odds ratio ROR and proportional reporting ratio PRR) was utilized for FAERS data mining from the first quarter of 2004 to the second quarter of 2021 (the most recent quarterly data at the time of the study). Preferred Terms of ADR reports were categorized by System Organ Class (SOC) based on the Medical Dictionary for Regulatory Activities.
RESULTS: This study retrieved 12,221 cases within the reporting period. A total of 140 repetitive signals were obtained by ROR and PRR, of which 53 new ADR signals were not recorded in the drug labels/datasheets. The top three ADR reports were \"blood creatine phosphokinase elevation\" (ROR, 56.66, 95% confidence interval (CI) 51.07-62.87, PRR 51.94), \"eosinophilic pneumonia\" (ROR 696.71, 95%CI 603.21-804.70, PRR 657.57), and \"rhabdomyolysis\" (ROR 22.85, 95%CI 19.94-26.18, PRR 21.83). The highest ROR of \"antimicrobial susceptibility test resistant\" was found at 9808.14. Reports of rare adverse events, such as \"necrotizing fasciitis and compartment syndrome,\" have emerged. The significant SOCs were \"Infections and Infestations\" and \"Investigations.\"
CONCLUSIONS: New daptomycin ADR signals were detected. Clinicians should monitor these potential ADRs in patients receiving daptomycin.

Introduction: Antipsychotic drugs are the main therapy for schizophrenia and have been widely used in mental disorder fields. However, the research on the safety of antipsychotic drugs in the real-world is rare. The purpose of this research is to evaluate the safety of antipsychotic drugs based on real-world data. Methods: ADR reports collected by the Henan Adverse Drug Reaction Monitoring Center from 2016 to 2020 were analyzed. We described the safety of antipsychotic drugs by descriptive analysis and four signal mining methods. Meanwhile, the risk factors for serious adverse reactions of antipsychotics were identified. Results: A total of 3363 ADR reports related to antipsychotics were included. We found that the number of adverse drug reaction reports and the proportion of serious adverse reactions have increased year by year from 2016 to 2020. Most adverse drug reactions occurred within 3 months after taking the medicine. The symptoms caused by typical antipsychotics and atypical antipsychotics were different and dyskinesia was more common in typical antipsychotics. Most patients improved or recovered after treatment or intervention while only one patient had sequelae. Low-level hospitals, psychiatric hospitals, youth, and old age could increase the risk of serious adverse reactions. Four off-label signals were found through signal mining, including amisulpride-pollakiuria, ziprasidone-dyspnoea, quetiapine-urinary incontinence, olanzapine-hepatic function abnormal. Conclusion: We found that most ADRs occurred within 3 months after taking the medicine, so close observation was required for patients during the first 3 months of treatment. The ADRs of antipsychotics involved multiple organ-system damages but were not serious. It might be recommended to take alternative drugs after a serious ADR occurred. The symptoms caused by typical APDs and atypical APDs were different. For patients with typical APDs, dyskinesia was more common and should be given special attention. Statistics showed that low-level hospitals, psychiatric hospitals, youth, and old age were risk factors for serious ADRs. The four off-label signals obtained by signal mining should be paid special attention, including amisulpride-pollakiuria, ziprasidone-dyspnoea, quetiapine-urinary incontinence, and olanzapine-hepatic function abnormal.