A Pharmacovigilance Study on Psychotropic Agent-Induced Urinary Retention Using the Japanese Adverse Drug Event Report Database.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2024-11-13 DOI:10.1007/s40801-024-00465-8

Shusuke Uekusa, Keika Mogi, Yuki Ota, Yuki Hanai, Kohei Kitagawa, Takashi Yoshio, Kazuhiro Matsuo

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Abstract

Introduction: Psychotropic drugs have been reported to cause urinary retention (UR) via anticholinergic and other mechanisms. However, UR has not received much attention because of its non-fatal symptoms. We investigated the occurrence of UR associated with psychotropic drugs using the Japanese Adverse Drug Event Report (JADER) database.

Methods: Using the JADER database, we calculated reporting odds ratios for UR for 74 psychotropic drugs. Multivariate logistic regression analysis was used to adjust for the effects of sex, underlying disease, and age on UR. Variable selection included forced entry for sex, age, benign prostatic hyperplasia (BPH), depression, and backward-forward stepwise selection for each drug.

Results: A total of 887,704 cases were reported, of which 4653 (0.52%) had UR. In terms of sex, 0.79% (3401/429,372 cases) and 0.43% (1797/415,358 cases) of male and female patients had UR. In terms of age, 0.31% (892/288,676 cases) and 0.68% (3463/506,907 cases) of patients aged < 60 years and 60 years or older had UR. Among the underlying diseases, 8.22% (930/11,316 cases) and 0.43% (3723/876,388 cases) of patients with BPH and without BPH had UR, respectively. Further, 1.99% (337/16,959 cases) and 0.50% (4316/870,745 cases) of patients with depression and without depression had UR, respectively. Overall, 38 psychotropic drugs met the criteria for signal detection. In logistic regression, a total of 783,083 patients of discernible age and sex were included. The selected variables were sex, age, BPH, depression, and 23 drugs, including quetiapine [adjusted reporting odds ratio (ROR) 95% confidence interval (CI): 1.46-2.81], chlorpromazine (adjusted ROR 95%CI: 1.29-3.13), etizolam (adjusted ROR 95%CI: 1.47-3.09), maprotiline (adjusted ROR 95%CI: 1.99-8.34), mirtazapine (adjusted ROR 95%CI: 1.37-2.88), and duloxetine (adjusted ROR 95%CI: 2.15-4.21).

Conclusions: Many psychotropic drugs induce UR, which may be owing to their pharmacological effects. Appropriate monitoring is needed, especially in patients with other risk factors for UR.

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利用日本药物不良事件报告数据库对精神药物引起的尿潴留进行药物警戒研究。

导言：据报道，精神药物可通过抗胆碱能和其他机制导致尿潴留（UR）。然而，由于尿潴留的症状并不致命，因此并未引起广泛关注。我们利用日本药物不良事件报告（JADER）数据库调查了与精神药物相关的尿潴留发生率：方法：利用 JADER 数据库，我们计算了 74 种精神药物的 UR 报告几率比。采用多变量逻辑回归分析调整性别、基础疾病和年龄对 UR 的影响。变量选择包括强制输入性别、年龄、良性前列腺增生症（BPH）、抑郁症，以及对每种药物进行后向逐步选择：共报告了 887 704 个病例，其中 4653 例（0.52%）患有尿潴留。在性别方面，分别有 0.79% （3401/429,372 例）和 0.43% （1797/415,358 例）的男性和女性患者患有 UR。在年龄方面，0.31%（892/288,676 例）和 0.68%（3463/506,907 例）年龄小于 60 岁和大于 60 岁的患者患有尿毒症。在基础疾病中，患有良性前列腺增生症和未患有良性前列腺增生症的患者中分别有 8.22% （930/11,316 例）和 0.43% （3723/876,388 例）患有尿失禁。此外，分别有 1.99%（337/16959 例）和 0.50%（4316/870745 例）的抑郁症患者和非抑郁症患者患有 UR。共有 38 种精神药物符合信号检测标准。在逻辑回归中，共纳入了 783,083 例可识别年龄和性别的患者。所选变量为性别、年龄、BPH、抑郁和 23 种药物，包括喹硫平[调整后的报告几率比（ROR）95% 置信区间（CI）：1.46-2.81]、氯丙嗪（调整后的 ROR 95%CI：1.29-3.13）、依替唑仑（调整后ROR 95%CI：1.47-3.09）、马普替林（调整后ROR 95%CI：1.99-8.34）、米氮平（调整后ROR 95%CI：1.37-2.88）和度洛西汀（调整后ROR 95%CI：2.15-4.21）.结论：结论：许多精神药物会诱发 UR，这可能是由于其药理作用所致。结论：许多精神药物都会诱发尿崩症，这可能是由于其药理作用所致，因此需要进行适当的监测，尤其是对有其他尿崩症风险因素的患者。

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来源期刊

Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-

CiteScore

3.60

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.