Analyses of non-benzodiazepine-induced adverse events and prognosis in elderly patients based on the Japanese adverse drug event report database

Abstract Background The contents of the guidelines for the use of non-benzodiazepines (Z-drugs) differ slightly between THE JAPANESE SOCIETY OF SLEEP RESEARCH and THE JAPAN GERIATRIC SOCIETY, and the recommended directions are conflicting. Therefore, we analyzed the use of the Japanese Adverse Drug Event Report database (JADER) for identifying adverse events (AEs) caused by Z-drugs and clarifying their occurrence trend and prognosis. Methods The signal value for comparison was calculated by using the proportional reporting ratio (PRR) and chi-squared test (χ2) results of data of elderly and non-elderly patients. Among AEs for which signals were detected in the elderly, we determined that those with lower signal values for non-elderly patients that were half the signal value of the elderly should be used with particular caution in the elderly. We also compared the prognoses. Results The AEs with > 1 risk ratio (RR) in elderly and non-elderly patients were regarded as those that should be noted in the prognosis of AEs in elderly patients. Furthermore, 28 AEs were detected in elderly patients’ signals. In this study, in addition to movement disorders such as “falls” and “bone fractures,” identified by two academic societies, signal characteristics of the elderly were obtained for psychiatric disorders and eye disorders. Conclusions There was no difference in prognosis, but these disorders could reduce the quality of life of patients. Therefore, we consider that in prescribing appropriate drug therapy for insomnia, attention should be paid to the occurrence of the AEs caused by the Z-drugs revealed by this study and the guidelines

A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system

Abstract Background Patient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have to be computed increases, and it becomes very difficult to explore the whole spontaneous reporting system (SRS). Since the signals need to be detected quickly in pharmacovigilance, a simple exploration method is required. Although association rule mining (AR) is commonly used for the analysis of large data, its application to pharmacovigilance is rare and there are almost no studies comparing AR with conventional signal detection methods. Methods In this study, in order to establish a simple method to explore ADEs in patients with kidney or liver injury as a background disease, the AR and proportional reporting ratio (PRR) signal detection methods were compared. We used oral medicine SRS data from the Japanese Adverse Drug Event Report database (JADER), and used AR as the proposed search method and PRR as the conventional method for comparison. “Rule count ≥ 3”, “min lift value > 1”, and “min conviction value > 1” were used as the AR detection criteria, and the PRR detection criteria were “Rule count ≥3”, “PRR ≥ 2”, and “χ2 ≥ 4”. Results In patients with kidney injury, the AR method had a sensitivity of 99.58%, specificity of 94.99%, and Youden’s index of 0.946, while in patients with liver injury, the sensitivity, specificity, and Youden’s index were 99.57%, 94.87%, and 0.944, respectively. Additionally, the lift value and the strength of the signal were positively correlated. Conclusions It was suggested that computation using AR might be simple with the detection power equivalent to that of the conventional signal detection method as PRR. In addition, AR can theoretically be applicable to SRS other than JADER. Therefore, complicated conditions (patient’s background etc.) that must take factors other than the ADE into consideration can be easily explored by selecting the AR as the first screening for ADE exploration in pharmacovigilance using SRS

A New Search Method Using Association Rule Mining for Drug-Drug Interaction Based on Spontaneous Report System

Background: Adverse events (AEs) can be caused not only by one drug but also by the interaction between two or more drugs. Therefore, clarifying whether an AE is due to a specific suspect drug or drug-drug interaction (DDI) is useful information for proper use of drugs. Whereas previous reports on the search for drug-induced AEs with signal detection using spontaneous reporting systems (SRSs) are numerous, reports on drug interactions are limited. This is because in methods that use “a safety signal indicator” (signal), which is frequently used in pharmacovigilance, a huge number of combinations must be prepared when signal detection is performed, and each risk index must be calculated, which makes interaction search appear unrealistic.Objective: In this paper, we propose association rule mining (AR) using large dataset analysis as an alternative to the conventional methods (additive interaction model (AI) and multiplicative interaction model (MI)).Methods: The data source used was the Japanese Adverse Drug Event Report database. The combination of drugs for which the risk index is detected by the “combination risk ratio (CR)” as the target was assumed to be true data, and the accuracy of signal detection using the AR methods was evaluated in terms of sensitivity, specificity, Youden's index, F-score.Results: Our experimental results targeting Stevens-Johnson syndrome indicate that AR has a sensitivity of 99.05%, specificity of 92.60%, Youden's index of 0.917, F-score of 0.876, AI has a sensitivity of 95.62%, specificity of 96.92%, Youden's index of 0.925, and F-score of 0.924, and MI has a sensitivity of 65.46%, specificity of 98.78%, Youden's index of 0.642, and F-score of 0.771. This result was about the same level as or higher than the conventional method.Conclusions: If you use similar calculation methods to create combinations from the database, not only for SJS, but for all AEs, the number of combinations would be so enormous that it would be difficult to perform the calculations. However, in the AR method, the “Apriori algorithm” is used to reduce the number of calculations. Thus, the proposed method has the same detection power as the conventional methods, with the significant advantage that its calculation process is simple