Subeesh Viswam K, Abraham Rishma, Satya Sai Minnikanti Venkata, Koonisetty Kranthi Swaroop
Department of Pharmacy Practice, Faculty of Pharmacy, M. S. Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India.
Perspect Clin Res. 2020 Apr-Jun;11(2):70-74. doi: 10.4103/picr.PICR_110_18. Epub 2020 May 6.
The primary intent of the study is to analyze the prescribing pattern and to identify the various drug-related problems (DRPs) associated with the therapy in chronic kidney disease (CKD) patients.
A prospective observational study was conducted in 160 patients diagnosed with any stages of CKD. The prescribing pattern was studied and DRPs were identified, reported, and categorized as per the Pharmaceutical Care Network Europe classification V 5.01. The association between categorical variables was analyzed using the Chi-square test. The predictors of DRPs were identified using binary logistic regression analysis.
The mean age of the study population was 50.08 ± 15.32 years with male predominance (71%). The average number of drugs per prescription was found to be 9.16 ± 3.01. The most prescribed drug category was antihypertensives and the most commonly prescribed drugs were diuretics. A total of 337 DRPs were identified, out of which the most common DRP was drug interactions (60%), followed by frequency errors (11.6%). Logistic regression analysis identified comorbidities more than three (odds ratio 2.09), antihypertensives more than two (odds ratio 1.9), alcoholism (odds ratio 1.5), and polypharmacy (odds ratio 1.2) as the predictors of DRPs even though they were not statistically significant at = 0.01.
DRPs increase the risk of deterioration of the disease state and increase the length of hospital stay. Identification and resolving of the DRPs will lead to better patient care and proper treatment. Early identification and modification of the above-mentioned predictors could possibly prevent/reduce DRPs.
本研究的主要目的是分析慢性肾脏病(CKD)患者的用药模式,并识别与治疗相关的各种药物相关问题(DRP)。
对160例诊断为任何阶段CKD的患者进行前瞻性观察研究。研究用药模式,识别DRP,并根据欧洲药学保健网络分类V 5.01进行报告和分类。使用卡方检验分析分类变量之间的关联。使用二元逻辑回归分析确定DRP的预测因素。
研究人群的平均年龄为50.08±15.32岁,男性占主导(71%)。每张处方的平均药物数量为9.16±3.01。最常开具的药物类别是抗高血压药,最常用的药物是利尿剂。共识别出337个DRP,其中最常见的DRP是药物相互作用(60%),其次是频次错误(11.6%)。逻辑回归分析确定合并症超过三种(比值比2.09)、抗高血压药超过两种(比值比1.9)、酗酒(比值比1.5)和多重用药(比值比1.2)为DRP的预测因素,尽管在α=0.01时它们无统计学意义。
DRP增加疾病状态恶化的风险并延长住院时间。识别和解决DRP将带来更好的患者护理和恰当治疗。尽早识别并修正上述预测因素可能预防/减少DRP。