Hecker Michael, Frahm Niklas, Bachmann Paula, Debus Jane Louisa, Haker Marie-Celine, Mashhadiakbar Pegah, Langhorst Silvan Elias, Baldt Julia, Streckenbach Barbara, Heidler Felicita, Zettl Uwe Klaus
Division of Neuroimmunology, Department of Neurology, Rostock University Medical Center, Rostock, Germany.
Ecumenic Hainich Hospital gGmbH, Mühlhausen, Germany.
Front Pharmacol. 2022 Aug 5;13:946351. doi: 10.3389/fphar.2022.946351. eCollection 2022.
Patients with multiple sclerosis (MS) often undergo complex treatment regimens, resulting in an increased risk of polypharmacy and potential drug-drug interactions (pDDIs). Drug interaction databases are useful for identifying pDDIs to support safer medication use. To compare three different screening tools regarding the detection and classification of pDDIs in a cohort of MS patients. Furthermore, we aimed at ascertaining sociodemographic and clinical factors that are associated with the occurrence of severe pDDIs. The databases Stockley's, Drugs.com and MediQ were used to identify pDDIs by screening the medication schedules of 627 patients. We determined the overlap of the identified pDDIs and the level of agreement in pDDI severity ratings between the three databases. Logistic regression analyses were conducted to determine patient risk factors of having a severe pDDI. The most different pDDIs were identified using MediQ ( = 1,161), followed by Drugs.com ( = 923) and Stockley's ( = 706). The proportion of pDDIs classified as severe was much higher for Stockley's (37.4%) than for Drugs.com (14.4%) and MediQ (0.9%). Overall, 1,684 different pDDIs were identified by at least one database, of which 318 pDDIs (18.9%) were detected with all three databases. Only 55 pDDIs (3.3%) have been reported with the same severity level across all databases. A total of 336 pDDIs were classified as severe (271 pDDIs by one database, 59 by two databases and 6 by three databases). Stockley's and Drugs.com revealed 47 and 23 severe pDDIs, respectively, that were not included in the other databases. At least one severe pDDI was found for 35.2% of the patients. The most common severe pDDI was the combination of acetylsalicylic acid with enoxaparin, and citalopram was the drug most frequently involved in different severe pDDIs. The strongest predictors of having a severe pDDI were a greater number of drugs taken, an older age, living alone, a higher number of comorbidities and a lower educational level. The information on pDDIs are heterogeneous between the databases examined. More than one resource should be used in clinical practice to evaluate pDDIs. Regular medication reviews and exchange of information between treating physicians can help avoid severe pDDIs.
多发性硬化症(MS)患者通常接受复杂的治疗方案,这导致多药并用风险增加以及潜在的药物相互作用(pDDI)。药物相互作用数据库有助于识别pDDI,以支持更安全的用药。为了比较三种不同的筛查工具在一组MS患者中检测和分类pDDI的情况。此外,我们旨在确定与严重pDDI发生相关的社会人口统计学和临床因素。使用Stockley's、Drugs.com和MediQ数据库,通过筛查627例患者的用药计划来识别pDDI。我们确定了已识别的pDDI的重叠情况以及三个数据库之间pDDI严重程度评级的一致性水平。进行逻辑回归分析以确定发生严重pDDI的患者风险因素。使用MediQ识别出的pDDI最多(= 116),其次是Drugs.com(= 923)和Stockley's(= 706)。Stockley's将pDDI分类为严重的比例(37.4%)远高于Drugs.com(14.4%)和MediQ(0.9%)。总体而言,至少一个数据库识别出1684种不同的pDDI,其中318种pDDI(18.9%)在所有三个数据库中均被检测到。所有数据库中报告的严重程度相同的pDDI仅55种(3.3%)。共有336种pDDI被分类为严重(一个数据库识别出271种,两个数据库识别出59种,三个数据库识别出6种)。Stockley's和Drugs.com分别发现了47种和23种其他数据库未涵盖的严重pDDI。35.2%的患者至少发现一种严重pDDI。最常见的严重pDDI是乙酰水杨酸与依诺肝素的组合,而西酞普兰是不同严重pDDI中最常涉及的药物。发生严重pDDI的最强预测因素是服用药物数量较多、年龄较大、独居、合并症数量较多以及教育水平较低。所检查的数据库之间关于pDDI的信息存在异质性。临床实践中应使用多种资源来评估pDDI。定期进行用药审查以及治疗医生之间的信息交流有助于避免严重pDDI。