Background It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs. Methods Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs. Results MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding. Conclusions An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs.
Aim. Polypharmacy may increase the prevalence of potential multidrug interactions (pMDIs), where one drug interacts with two or more other drugs, possibly amplifying the risk of a potential adverse drug event (pADE). The major goal of this study was to estimate the prevalence of amplifying pMDIs in an ambulatory cohort of older patients. Methods. Current medication lists of 22033 randomly chosen outpatients ≥50 years old were extracted from the New York Presbyterian Hospital (NYP) data warehouse. Network analysis identified patients prescribed three or more interacting drugs from their current medication lists. Potentially harmful interactions were identified from the NYP drug-drug interaction alerting system. pMDIs were considered amplifying if the interactions increased the probability of a pADE through pharmacokinetic, pharmacodynamic or conditional mechanisms. Results. pMDIs were identified in 5.1% of the medication lists; 3.4% were three-drug and 1.1% were four-drug pMDIs. The most common drugs involved were psychotropic, comprising 23.3% of the total drugs. The most common pADEs associated with the interactions were serotonin syndrome (17.2%), seizures (14.4%), prolonged QT interval (15.8%) and bleeding (14.4%). pADE amplification risk was identified in 71.8% of three-drug pMDIs when one drug interacted with two others, 97.8% when all three interacted with each other, and 93% for four-drug pMDIs. Conclusion. Our data suggest that approximately 5% of elderly ambulatory patients may be exposed to pMDIs which amplify the probability of associated adverse drug events. The recent and persistent rise in polypharmacy will likely increase the prevalence of pMDIs and potential exposure to serious adverse events.
One pervasive task found throughout the empirical sciences is to determine the effect of interventions from non-experimental data. It is wellunderstood that assumptions are necessary to perform causal inferences, which are commonly articulated through causal diagrams (Pearl, 2000). Despite the power of this approach, there are settings where the knowledge necessary to specify a causal diagram over all observed variables may not be available, particularly in complex, highdimensional domains. In this paper, we introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) that allows for the partial specification of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram. A C-DAG specifies relationships between clusters of variables, while the relationships between the variables within a cluster are left unspecified. We develop the foundations and machinery for valid causal inferences over C-DAGs. In particular, we first define a new version of the d-separation criterion and prove its soundness and completeness. Secondly, we extend these new separation rules and prove the validity of the corresponding docalculus. Lastly, we show that a standard identification algorithm is sound and complete to systematically compute causal effects from observational data given a C-DAG.
Reasoning about the effect of interventions and counterfactuals is a fundamental task found throughout the data sciences. A collection of principles, algorithms, and tools has been developed for performing such tasks in the last decades. One of the pervasive requirements found throughout this literature is the articulation of assumptions, which commonly appear in the form of causal diagrams. Despite the power of this approach, there are significant settings where the knowledge necessary to specify a causal diagram over all variables is not available, particularly in complex, high-dimensional domains. In this paper, we introduce a new graphical modeling tool called cluster DAGs (for short, C-DAGs) that allows for the partial specification of relationships among variables based on limited prior knowledge, alleviating the stringent requirement of specifying a full causal diagram. A C-DAG specifies relationships between clusters of variables, while the relationships between the variables within a cluster are left unspecified, and can be seen as a graphical representation of an equivalence class of causal diagrams that share the relationships among the clusters. We develop the foundations and machinery for valid inferences over C-DAGs about the clusters of variables at each layer of Pearl's Causal Hierarchy - L1 (probabilistic), L2 (interventional), and L3 (counterfactual). In particular, we prove the soundness and completeness of d-separation for probabilistic inference in C-DAGs. Further, we demonstrate the validity of Pearl's do-calculus rules over C-DAGs and show that the standard ID identification algorithm is sound and complete to systematically compute causal effects from observational data given a C-DAG. Finally, we show that C-DAGs are valid for performing counterfactual inferences about clusters of variables.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.