住院心脏病患者中潜在有害药物相互作用及其相关因素:一项横断面研究。

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2023-09-01 Epub Date: 2023-05-13 DOI:10.1007/s40801-023-00373-3
Abdulrahman Kalash, Aly Abdelrahman, Ibrahim Al-Zakwani, Yousuf Al Suleimani
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引用次数: 1

摘要

背景:心血管疾病在世界范围内造成了相当大比例的死亡。老年患者受心血管疾病的影响最大,由于多种药物、多发病以及与年龄相关的药物可用性和代谢变化等因素,他们极易发生药物-药物相互作用。药物-药物相互作用是导致住院患者和门诊患者出现负面结果的众多药物相关问题之一。因此,重要的是调查患病率、涉及的药物以及与潜在药物相互作用(pDDI)相关的因素,以适当优化这些患者的药物治疗方案。目的:我们旨在确定在阿曼马斯喀特苏丹卡布斯大学医院心脏科住院患者中pDDI的患病率、最常涉及的药物以及与这些相互作用相关的重要预测因素。方法:这项回顾性横断面研究包括215名患者。Micromedex Drug Reax®用于鉴定pDDI。收集并分析从患者病历中提取的数据。应用单变量和多变量线性回归来确定与观察到的pDDI相关的预测因素。结果:共确定2057个pDDI,每个患者的中位数为9(5-12)个pDDI。至少有一个pDDI的患者占所有纳入患者的97.2%。大多数pDDI具有严重程度(52.6%)、一般文献水平(45.5%)和药效学基础(55.9%)。阿托伐他汀和氯吡格雷之间的潜在药物相互作用最为常见(9%)。在所有检测到的pDDI中,约79.6%至少包含一种抗血小板药物。糖尿病合并症(B=2.564,p<0.001)和住院期间服用的药物数量(B=0.562,p<0.001)是与pDDI频率呈正相关的因素。结论:在阿曼马斯喀特苏丹卡布斯大学医院住院的心脏病患者中,潜在的药物相互作用非常普遍。糖尿病作为一种共病并且服用大量药物的患者,pDDI数量增加的风险更高。
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Potentially Harmful Drug-Drug Interactions and Their Associated Factors Among Hospitalized Cardiac Patients: A Cross-Sectional Study.

Background: Cardiovascular diseases are responsible for a significant proportion of mortalities worldwide. Elderly patients are the most affected by cardiovascular diseases, and because of factors such as polypharmacy, multimorbidity, and age-related changes in drug availability and metabolism, they are highly susceptible to the occurrence of drug-drug interactions. Drug-drug interactions are among the many drug-related problems leading to negative outcomes among inpatients and outpatients. Thus, it is important to investigate the prevalence, involved drugs, and factors related to potential drug-drug interactions (pDDIs) to properly optimize pharmacotherapy regimens for these patients.

Objective: We aimed to determine the prevalence of pDDIs, drugs most frequently implicated, and significant predictors associated with these interactions among hospitalized patients in the Cardiology Unit at Sultan Qaboos University Hospital in Muscat, Oman.

Methods: This retrospective cross-sectional study included 215 patients. Micromedex Drug-Reax® was used to identify pDDIs. Data extracted from patients' medical records were collected and analyzed. Univariable and multivariable linear regression was applied to determine the predictors associated with the observed pDDIs.

Results: A total of 2057 pDDIs were identified, with a median of nine (5-12) pDDIs per patient. Patients with at least one pDDI accounted for 97.2% of all the included patients. The majority of pDDIs were of major severity (52.6%), fair level of documentation (45.5%), and pharmacodynamic basis (55.9%). Potential drug-drug interactions between atorvastatin and clopidogrel were the most frequently observed (9%). Of all the detected pDDIs, around 79.6% of them included at least one antiplatelet drug. Having diabetes mellitus as a comorbidity (B = 2.564, p < 0.001) and the number of drugs taken during the hospitalization period (B = 0.562, p < 0.001) were factors positively associated with the frequency of pDDIs.

Conclusions: Potential drug-drug interactions were highly prevalent among hospitalized cardiac patients at Sultan Qaboos University Hospital, Muscat, Oman. Patients having diabetes as a comorbidity and with a high number of administered drugs were at a higher risk of an increased number of pDDIs.

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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
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.
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