Signal detection of oral drug-induced dementia in chronic kidney disease patients using association rule mining and Bayesian confidence propagation neural network

Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source

Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

Assessment of the Potential Adverse Events Related to Ribavirin-Interferon Combination for Novel Coronavirus Therapy

Purpose

We aimed to analyze and evaluate the safety signals of ribavirin-interferon combination through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS), so as to provide reference for the rationale use of these agents in the management of relevant toxicities emerging in patients with novel coronavirus pneumonia (COVID-19).

Methods

Reports to the FAERS from 1 January 2004 to 8 March 2020 were analyzed. The proportion of report ratio (PRR), reporting odds ratio (ROR), and Bayesian confidence interval progressive neural network (BCPNN) method were used to detect the safety signals.

Results

A total of 55 safety signals were detected from the top 250 adverse event reactions in 2200 reports, but 19 signals were not included in the drug labels. All the detected adverse event reactions were associated with 13 System Organ Classes (SOC), such as gastrointestinal, blood and lymph, hepatobiliary, endocrine, and various nervous systems. The most frequent adverse events were analyzed, and the results showed that females were more likely to suffer from anemia, vomiting, neutropenia, diarrhea, and insomnia.

Conclusion

The ADE (adverse drug event) signal detection based on FAERS is helpful to clarify the potential adverse events related to ribavirin-interferon combination for novel coronavirus therapy; clinicians should pay attention to the adverse reactions of gastrointestinal and blood systems, closely monitor the fluctuations of the platelet count, and carry out necessary mental health interventions to avoid serious adverse events.

Mechanisms of cognitive dysfunction in CKD

Cognitive impairment is an increasingly recognized major cause of chronic disability and is commonly found in patients with chronic kidney disease (CKD). Knowledge of the relationship between kidney dysfunction and impaired cognition may improve our understanding of other forms of cognitive dysfunction. Patients with CKD are at an increased risk (compared with the general population) of both dementia and its prodrome, mild cognitive impairment (MCI), which are characterized by deficits in executive functions, memory and attention. Brain imaging in patients with CKD has revealed damage to white matter in the prefrontal cortex and, in animal models, in the subcortical monoaminergic and cholinergic systems, accompanied by widespread macrovascular and microvascular damage. Unfortunately, current interventions that target cardiovascular risk factors (such as anti-hypertensive drugs, anti-platelet agents and statins) seem to have little or no effect on CKD-associated MCI, suggesting that the accumulation of uraemic neurotoxins may be more important than disturbed haemodynamic factors or lipid metabolism in MCI pathogenesis. Experimental models show that the brain monoaminergic system is susceptible to uraemic neurotoxins and that this system is responsible for the altered sleep pattern commonly observed in patients with CKD. Neural progenitor cells and the glymphatic system, which are important in Alzheimer disease pathogenesis, may also be involved in CKD-associated MCI. More detailed study of CKD-associated MCI is needed to fully understand its clinical relevance, underlying pathophysiology, possible means of early diagnosis and prevention, and whether there may be novel approaches and potential therapies with wider application to this and other forms of cognitive decline.