Approaches to Optimize Medication Data Analysis in Clinical Cohort Studies.
Approaches to Optimize Medication Data Analysis in Clinical Cohort Studies.
复制标题
在临床队列研究中优化药物数据分析的方法。
DOI：
10.1111/jgs.16844
复制
发表时间：
2020-12
影响因子：
6.3
通讯作者：
Inouye SK
中科院分区：
文献类型：
--
作者：
Duprey MS;Devlin JW;Briesacher BA;Travison TG;Griffith JL;Inouye SK
建立了大规模数据库药物流行病学研究的方法。虽然老年人的临床队列通常包含关键信息，以促进我们对药物风险和益处的理解，但最适合管理这些样本中的药物数据的方法有时并不清楚，它们的验证程度也不清楚。我们试图在老年人精神错乱的临床队列研究的背景下，为研究人员提供指导，指导他们使用来自临床队列的数据来更好地了解药物风险因素和结果的方法工具。前瞻性队列研究。择期手术后成功老龄化(SAGES)前瞻性队列研究。560名非痴呆症老年人(≥70岁)接受择期大手术。使用SAGS临床队列，对用于表征药物的方法进行识别、回顾、分析，并根据在较小的临床数据集中表征药物流行病学数据的适当性和有效性程度来区分。药物编码是必不可少的；在美国最常用的美国医院处方系统(AHFS)并不比其他系统更受欢迎。对单一药物类别(如阿片类药物)使用等量剂量表(例如，吗啡当量)比使用多类止痛药当量表更可取。来自同一类药物(如苯二氮卓类)的药物聚集是公认的；聚集的最佳流行率突破尚不清楚。联合使用结构不同的药物(如抗胆碱类药物)的有效量表(S)应谨慎使用；对于最佳规模缺乏共识。在确定潜在混杂因素时，有向无环图(S)是公认的概念化病因框架的方法。基于模型的策略应该与基于证据的先验变量选择策略一起使用。正如SAGES队列中强调的那样，用于对临床上丰富的队列研究中的药物数据进行分类和分析的方法在它们被开发和验证的严格性上有所不同。
Methods for pharmacoepidemiologic studies of largescale data repositories are established. While clinical cohorts of older adults often contain critical information to advance our understanding of medication risk and benefit, the methods best-suited to manage medication data in these samples are sometimes unclear and their degree of validation unknown. We sought to provide researchers, in the context of a clinical cohort study of delirium in older adults, with guidance on the methodological tools to use data from clinical cohorts to better understand medication risk factors and outcomes. Prospective cohort study. The Successful Aging after Elective Surgery (SAGES) prospective cohort. 560 older adults (≥70 years) without dementia undergoing elective major surgery. Using the SAGES clinical cohort, methods used to characterize medications were identified, reviewed, analyzed and distinguished by appropriateness and degree of validation for characterizing pharmacoepidemiologic data in smaller clinical datasets. Medication coding is essential; the American Hospital Formulary System (AHFS), most often used in the U.S., is not preferred over others. Use of equivalent dosing scales (e.g., morphine equivalents) for a single medication class (e.g. opioids) is preferred over multi-class analgesic equivalency scales. Medication aggregation from the same class (e.g. benzodiazepines) is well-established; the optimal prevalence breakout for aggregation remains unclear. Validated scale(s) to combine structurally dissimilar medications (e.g., anticholinergics) should be used with caution; a lack consensus exists regarding the optimal scale. Directed acyclic graph(s) are an accepted method to conceptualize etiologic frameworks when identifying potential confounders. Modelling-based strategies should be used with evidence-based, a priori variable-selection strategies. As highlighted in the SAGES cohort, the methods used to classify and analyze medication data in clinically-rich cohort studies vary in the rigor which they have been developed and validated.
登录
查看更多内容
影响因子：
7.6
作者：
BARON, RM;KENNY, DA
通讯作者：
KENNY, DA
影响因子：
7.6
作者：
Jensen, Niels H.;Rodriguiz, Ramona M.;Roth, Bryan L.
通讯作者：
Roth, Bryan L.
影响因子：
2.8
作者：
Green, Ariel R.;Reifler, Liza M.;Boyd, Cynthia M.
通讯作者：
Boyd, Cynthia M.
影响因子：
3.3
作者：
Garpestad, Erik;Devlin, John W.
通讯作者：
Devlin, John W.
影响因子：
10.6
作者：
Andreasen, Nancy C.;Pressler, Marcus;Nopoulos, Peg;Miller, Del;Ho, Beng-Choon
通讯作者：
Ho, Beng-Choon