Drug Safety最新文献

Background: Piperacillin-tazobactam combined with vancomycin is widely employed for broad-spectrum empiric coverage but has been increasingly associated with acute kidney injury (AKI). The comparative renal safety of substituting vancomycin with teicoplanin remains uncertain.

Objective: This meta-analysis aimed to evaluate renal outcomes between piperacillin-tazobactam plus teicoplanin (TZP-TEI) versus piperacillin-tazobactam plus vancomycin (TZP-VAN).

Methods: PubMed, Scopus, and Cochrane Central were searched for studies comparing TZP-TEI versus TZP-VAN in hospitalized patients. The primary outcome was AKI incidence, defined by Kidney disease: Improving global outcomes (KDIGO) or RIFLE (Risk of renal dysfunction, Injury to kidney, Failure or Loss of kidney function, and End-stage kidney disease) criteria. Data were analyzed using Review Manager, with heterogeneity assessed via the I² statistic.

Results: A total of 908 patients were included from five cohort studies, four of which applied propensity-score matching (PSM), with reported ages ranging from 56.8 to 79 years. The TZP-TEI regimen was associated with a significantly reduced rate of AKI compared with TZP-VAN (odds ratio [OR] 0.52; 95% confidence interval [CI] 0.30-0.89; p = 0.02; I² = 51%). No statistically significant differences were observed between groups for AKI recovery (OR 0.68; 95% CI 0.41-1.12; p = 0.13; I² = 0%) or for 30-day all-cause mortality (OR 1.34; 95% CI 0.77-2.32; p = 0.30; I² = 0%). Subgroup analyses stratified by AKI severity (KDIGO stages 1-3 or RIFLE criteria) demonstrated consistent directionality across stages, with no significant differences observed within PSM or non-PSM cohorts.

Conclusion: The TZP-TEI combination was associated with a significantly lower incidence of AKI than was TZP-VAN. Further studies are warranted to validate these findings, optimize teicoplanin dosing within the TZP-TEI combination, and inform therapeutic drug monitoring implementation in high-risk hospitalized patients.

Background and aims: Herpes zoster (HZ) infection and long-term non-steroidal anti-inflammatory drugs (NSAIDs) use are established risk factors for stroke and other cardiovascular diseases. Given the paucity of evidence regarding an association between NSAIDs use and HZ on stroke risk, this case-crossover study, utilizing a nationwide, population-based cohort, aimed to investigate the effect of HZ infection and concurrent NSAIDs use on the incidence of stroke.

Methods: Using Taiwan's National Health Insurance database (2014-2020), we identified 336,075 patients with incident stroke. A case-crossover design comparing exposure to HZ and NSAIDs between the focal period (1-30 days before stroke) and referent period (366-395 days before stroke) was employed. Conditional logistic regression estimated adjusted odds ratios (aORs) for stroke risk associated with NSAIDs use during HZ episodes. Pre-planned subgroup analyses further examined such effects on stroke subtypes (ischemic stroke, hemorrhagic stroke, and transient ischemic attack [TIA]), across age groups (< 50, 50-64, ≥ 65 years) and in patients with various comorbidities, including immunocompromised and autoimmune diseases, cardiometabolic risk factors, and renal and liver diseases.

Results: Combined HZ infection and NSAIDs use was associated with doubled stroke risk (aOR 2.05, 95% confidence interval [CI] 1.80-2.33) compared with periods without either exposure. For specific stroke types, the aORs were 1.94 (95% CI 1.65-2.29) for ischemic stroke, 1.81 (95% CI 1.34-2.43) for hemorrhagic stroke, and 2.81 (95% CI 2.06-3.85) for TIA. HZ episodes without NSAIDs (aOR 1.70, 95% CI 1.45-2.00) and NSAIDs use alone (aOR 1.42, 95% CI 1.40-1.44) showed lower but significant risk increment. In age-stratified analyses, individuals aged 65 years and older exhibited a significantly elevated stroke risk while concurrently utilizing NSAIDs during HZ episodes (aOR 2.19, 95% CI 1.92-2.62). Subgroup analyses demonstrated consistent elevated risks in patients with pre-existing comorbidities, particularly immunocompromised conditions (aOR 3.07, 95% CI 1.95-4.81) and renal disease (aOR 4.30, 95% CI 2.20-8.41).

Conclusions: Our findings demonstrate a significant association between HZ infection and NSAIDs use on stroke risk, particularly among individuals aged 65 years and older or those with pre-existing immunocompromised, cardiometabolic, and chronic conditions. The optimization of pain management strategies during HZ episodes is paramount to mitigate the risk of stroke while ensuring effective management of HZ-associated pain.

Background: It is unknown whether nR value [(ALT or AST/ULN) ÷ (AP/ULN)] ≥ 5 is better than alkaline phosphatase less than twice the upper limit of normal (AP < 2x ULN) in identifying hepatocellular drug-induced liver injury (HC DILI) consistent with Hy's law in clinical trials.

Objective: We aimed to compare nR value ≥ 5 and AP < 2x ULN in clinical trial DILI cases with ALT or AST ≥ 3x ULN and total bilirubin (TB) > 2x ULN.

Methods: We retrospectively categorized clinical trial, DILI cases from July 2020 to April 2024 with ALT or AST ≥ 3x ULN and jaundice as meeting nR value ≥ 5, AP < 2x ULN, both, or neither. We determined positive predictive values (PPVs) and sensitivities for HC DILI-related fatality (death or liver transplant) and acute liver failure (ALF).

Results: Of 1314 liver injuries across 73 drug applications, 294 (22%) were attributed to DILI; 55 had ALT or AST ≥ 3x ULN and TB > 2x ULN. We excluded three cases (Gilbert's, high baseline enzymes, hepatitis B reactivation). Of 52 remaining, 16 (31%) met nR ≥ 5, five (10%) AP < 2x ULN, 21 (40%) both, and 10 (19%) neither. There were four DILI fatalities. Excluding one cholestatic fatality, nR ≥ 5 and AP < 2x ULN had PPVs for HC DILI fatality of 8 and 4%, respectively; sensitivities were 100 and 33%, respectively. One patient survived HC DILI-related ALF. Including this ALF case with the fatalities, nR ≥ 5 and AP < 2x ULN had PPVs of 11 and 4%, respectively; sensitivities were 100 and 25%, respectively. All fatalities and ALF cases were due to different drugs.

Conclusion: While the number of cases with the most severe DILI outcomes was small, particularly those that resulted in fatalities or ALF, nR ≥ 5 better approximated Hy's Law and was more sensitive than AP < 2x ULN in detecting fatalities and ALF.

Introduction: In drug-safety monitoring systems, adverse events (AEs) associated with the use of medical products often consist of complex patterns of clinical events. Network analysis (NA) was used for pattern recognition and characterizing the Vaccine Adverse Event Reporting System (VAERS), but limited applications of NA to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) left its network description incomplete.

Methods: In this analysis, the network properties of FAERS were characterized and leveraged to facilitate pattern discovery. Reported AE information in FAERS is represented using preferred terms (PTs) in Medical Dictionary for Regulatory Activities terminology. The FAERS subsets were analyzed with drugs and PTs as nodes and interconnections as edges. Global characteristics, like the scale-free nature of the distribution, were examined to explore theoretical and structural considerations. Metrics that assess connectivity and edge weighting algorithms based on report co-occurrence or clustering were applied.

Results: Serious AE reports from 2016 to 2023 (2,062,099) were represented as a network of 20,965 nodes (16,847 PTs and 4116 drugs) with more than four million interconnections. Characteristics of FAERS subnetworks were determined with heavy-tailed degree distributions, high local clustering, and low diameters. Complexities related to structural and evolutionary characteristics were revealed as the log-normal model fits the degree distribution better than the power law.

Conclusions: Network-based techniques identified clinically relevant patterns and clustering patterns representative of known adverse drug reactions. Comparisons to VAERS reveal similarities in networks of AE reporting systems. This initial systematic application of NA to FAERS describes the overall network characteristics of the FAERS database and provides insight into the use of network applications in drug safety research.

Introduction: Dementia is the most prevalent neurodegenerative disorder. Several studies have demonstrated an association between anticholinergic drug use and an increased risk of cognitive and physical impairment. However, anticholinergic drugs are commonly prescribed for various clinical conditions, and their cumulative effects, referred to as anticholinergic burden, can contribute to cognitive decline and dementia. Although the causal relationship remains inconclusive, a higher anticholinergic burden is linked to a greater risk of cognitive deterioration.

Objective: This study aims to assess the compliance of patients aged ≥ 65 years with the STOPP-START and Beers criteria concerning the concurrent use of medications with high anticholinergic potency in situations where their use is not clinically justified.

Methods: This observational descriptive study was conducted using data from the Spanish Database for Pharmacoepidemiological Research (BIFAP). The study population comprised male and female patients aged ≥ 65 years.

Results: Of the 81,405 patients who developed dementia during the study period, 46.7% had been exposed to multiple anticholinergic drugs. Among them, 81.1% used these drugs sequentially, while 18.9% used two or more simultaneously. The absolute risk of developing dementia was 6.5% in patients who met the STOPP-START and BEERS criteria, compared to 13.4% in those who did not.

Conclusion: Although a high anticholinergic burden is a risk factor for cognitive decline, the unjustified concurrent use of multiple anticholinergic drugs remains uncommon among the elderly population in Spain.

Introduction: Adverse drug reactions (ADRs), including those resulting from drug interactions, remain a leading cause of morbidity and mortality. Structured product labels (SPLs) serve as a primary source for drug safety information. Having machine-readable product labels, including adverse reactions (ARs) and drug interactions, readily available would allow researchers to streamline medication safety studies. However, extracting this information is complex and requires the use of natural language processing (NLP) methods.

Objective: In this study, we explored the application of generative language models in the extraction of drug safety information from SPLs.

Methods: We compared multiple generative LLMs (GPT, Llama, and Mixtral) to two baseline methods in the task of extracting adverse reactions (ARs) from SPLs. We explored various factors, such as prompting strategies and term complexity, that impact the performance of these models in the extraction of ARs. Finally, we explored the generative models' capacity to extract drug interactions from a separate section of SPLs without additional fine-tuning or training, demonstrating their flexibility and adaptability for information retrieval.

Results: We found that generative language models, specifically GPT-4, are able to match or exceed the performance of previous state-of-the-art models without additional training or fine-tuning. Additionally, we found that the specific SPL section, surrounding context, and complexity of the AR term impacted the extraction performance. Finally, we demonstrated the generalizability of these models by applying them to a separate task of extracting drug names from the drug interaction section where curated training data are not available.

Conclusion: Generative language models demonstrate significant potential for automating drug safety information extraction from SPLs, offering a promising avenue for improving post-market surveillance and reducing ADRs. Future work should focus on refining prompting strategies and expanding the models' capabilities to handle increasingly complex and nuanced drug safety information.

Disproportionality analysis is used by many pharmacovigilance organizations for detecting and assessing signals of potential adverse drug reactions. However, its goal is often misunderstood and the approach misapplied, leading to erroneous conclusions due to neglected violated assumptions. In this paper we illustrate how simplistic use and interpretation of disproportionality analysis can lead to incorrect conclusions. Using VigiBase, the WHO global database of adverse event reports, and the Information Component disproportionality metric, we provide selected examples to highlight common sources of error that can introduce spurious disproportionalities or lead to missing important signals: confounding (by age, sex, indication, comedication), effect modification (by age), notoriety bias, masking, misclassification (by miscoding, incomplete or imprecise event retrieval), neglecting report utility, and violated independence assumption. Additionally, we present how sophisticated analyses may introduce new biases or amplify existing ones, such as collider bias or masking amplification. Due to its pitfalls, disproportionality analysis plays a supportive rather than decisive role in signal detection and assessment. Careful design and interpretation of disproportionality analysis, with appropriate subgrouping and clinical assessment, are essential. While subgrouping can mitigate some pitfalls, it reduces sample size and may introduce or amplify existing biases and needs to be used with care. Further development of tools to detect and mitigate biases in disproportionality analyses, and to assess their risk of bias, is needed.