Abstract

BackgroundAlthough tramadol is an effective weak opioid analgesic, careful monitoring of potential central nervous system adverse reactions in older adults is needed, especially when used with concomitant medications which may trigger the adverse effects. We aimed to characterize tramadol users with potentially inappropriate co-medications in older adults using a latent class analysis (LCA).MethodPatients aged 65 years or older using tramadol and receiving potentially inappropriate co-medications were included from a nationwide healthcare claims database. We defined antidepressants, first-generation antihistamines, and anxiolytics as potentially inappropriate co-medications. We applied an LCA for grouping tramadol users based on the common characteristics of medication use and healthcare utilization, and each patient was probabilistically assigned to a class. Patients’ characteristics in different latent classes were compared. Potential adverse drug reactions (ADRs) was defined as the any visits for emergency department after the occurrence of potentially inappropriate co-medications. Logistic regression analysis was used to examine the association between latent classes and potential ADRs.ResultsWe identified four distinct latent classes of tramadol users representing different patterns of co-medications: multiple potential drug-drug interaction (pDDI) combination users, antihistamines-tramadol users, antidepressants-tramadol users, and anxiolytics-tramadol users. Multiple pDDI combination users showed high proportion of regular tramadol use, tended to visit more medical institutions, and had a high Charlson comorbidity score. The duration of use of potentially inappropriate co-medications with tramadol was the longest in multiple pDDI combination users and the shortest in antihistamines-tramadol users.When compared with antihistamines-tramadol users, increased potential ADR risk was observed in multiple pDDI combination users (adjusted odds ratio (OR), 1.81; 95% confidence interval (CI), 1.75–1.88), antidepressants-tramadol users (1.24; 1.19–1.29), and anxiolytics-tramadol users (1.04; 1.00–1.08).ConclusionsFour distinct classes were identified among older adults using tramadol and potentially inappropriate co-medications. Differences in potential ADR risk were observed between these classes. These findings may help to identify patients at a high risk for ADRs owing to potentially inappropriate co-medications with tramadol.

Highlights

  • Tramadol is one of the most commonly used opioid analgesics worldwide [1, 2]

  • We identified four distinct latent classes of tramadol users representing different patterns of co-medications: multiple potential drug-drug interaction combination users, antihistamines-tramadol users, antidepressants-tramadol users, and anxiolytics-tramadol users

  • Differences in potential adverse drug reaction (ADR) risk were observed between these classes

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Summary

Introduction

Tramadol is one of the most commonly used opioid analgesics worldwide [1, 2]. In older adults, while pain-related conditions become more prevalent [3], age-related physiological changes increase the vulnerability to the adverse effects of commonly used analgesics [4]. Besides a weak agonistic effect on the μ-opioid receptors, tramadol acts by simultaneously inhibiting norepinephrine and serotonin reuptake [8] This unique mechanism was associated with a dose-related increase in adverse drug reaction (ADR) risk of tramadol, including serotonin syndrome (SS), seizures, and sedation [7, 9,10,11]. Because older adults with chronic pain syndromes are prone to have multiple comorbidities, adverse reactions may be triggered by potential interactions with commonly used co-medications [12]. Tramadol is an effective weak opioid analgesic, careful monitoring of potential central nervous system adverse reactions in older adults is needed, especially when used with concomitant medications which may trigger the adverse effects. We aimed to characterize tramadol users with potentially inappropriate co-medications in older adults using a latent class analysis (LCA)

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