Approaches to Optimize Medication Data Analysis in Clinical Cohort Studies

Abstract

OBJECTIVES Methods for pharmacoepidemiologic studies of large‐scale data repositories are established. Although 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. DESIGN Prospective cohort study. SETTING The Successful Aging After Elective Surgery (SAGES) prospective cohort. PARTICIPANTS A total of 560 older adults (aged ≥70 years) without dementia undergoing elective major surgery. MEASUREMENTS 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 data sets. RESULTS Medication coding is essential; the American Hospital Formulary System, most often used in the United States, 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 multiclass 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 of consensus exists regarding the optimal scale. Directed acyclic graph(s) are an accepted method to conceptualize causative frameworks when identifying potential confounders. Modeling‐based strategies should be used with evidence‐based, a priori variable‐selection strategies. CONCLUSION As highlighted in the SAGES cohort, the methods used to classify and analyze medication data in clinically rich cohort studies vary in the rigor by which they have been developed and validated.

Clinical Pharmacist Intervention Reduces Potentially Inappropriate Prescriptions in a Geriatric Perioperative Care Unit Dedicated to Hip Fracture

“…Finally, we used a directed acyclic graph, a method validated in pharmaco-epidemiology, to model and identify potential confounders that require conditioning when estimating causal effect in quasi-experimental studies [43, 44]. To our knowledge, no other study has used this method to explore a CP intervention, but Duprey et al [45] used a directed acyclic graph to model and analyse the potential interactions between different factors, such as age and urinary retention, and anticholinergic drug use and delirium.…”

Overestimation of the Causal Effects of Medications on Delirium During Postoperative Hospital Days

“…2 Thus, in SAGES, the diagnosis of delirium was based mostly on CAM assessments and, for most patients, without corroborating clinical notes. 2,3 Duprey et al 1 state, “all medication exposure was confirmed to precede delirium development for individuals who were delirium positive before being entered into the [analytical] model.” Although confirming the temporal sequence increases the chance that a given medication caused delirium, 4 we posit that the authors’ results likely overestimated the causal effect of medications on delirium.…”

“…Later that day, when the second CAM assessment was made, the patient satisfied the CAM criteria for delirium. Thus, the 2:00 am administration of benzodiazepine preceded the second CAM assessment and, using the methods of Duprey et al, 1,4 a causative link between benzodiazepine and delirium would be inferred. However, that conclusion would have been incorrect.…”

Association Between Perioperative Medication Use and Postoperative Delirium and Cognition in Older Adults Undergoing Elective Noncardiac Surgery

“…Medications were coded by class using the American Hospital Formulary System (AHFS) classifications. 17 Additionally, total anticholinergic activity was quantified with each medication being assigned a value from 0 to 3, based on the strength of the agent's anticholinergic activity according to the Anticholinergic Cognitive Burden (ACB) scale and the Anticholinergic Drug Score (ADS). 17 If a class of medications had a prevalence <5% in either the prehospital or postsurgical period, then the medication class was excluded from this time period.…”