Polypharmacy Patterns: Unravelling Systematic Associations between Prescribed Medications

Abstract

ObjectivesThe aim of this study was to demonstrate the existence of systematic associations in drug prescription that lead to the establishment of patterns of polypharmacy, and the clinical interpretation of the associations found in each pattern. MethodsA cross-sectional study was conducted based on information obtained from electronic medical records and the primary care pharmacy database in 2008. An exploratory factor analysis of drug dispensing information regarding 79,089 adult patients was performed to identify the patterns of polypharmacy. The analysis was stratified by age and sex. ResultsSeven patterns of polypharmacy were identified, which may be classified depending on the type of disease they are intended to treat: cardiovascular, depression-anxiety, acute respiratory infection (ARI), chronic obstructive pulmonary disease (COPD), rhinitis-asthma, pain, and menopause. Some of these patterns revealed a clear clinical consistency and included drugs that are prescribed together for the same clinical indication (i.e., ARI and COPD patterns). Other patterns were more complex but also clinically consistent: in the cardiovascular pattern, drugs for the treatment of known risk factors—such as hypertension or dyslipidemia—were combined with other medications for the treatment of diabetes or established cardiovascular pathology (e.g., antiplatelet agents). Almost all of the patterns included drugs for preventing or treating potential side effects of other drugs in the same pattern.ConclusionsThe present study demonstrated the existence of non-random associations in drug prescription, resulting in patterns of polypharmacy that are sound from the pharmacological and clinical viewpoints and that exist in a significant proportion of the population. This finding necessitates future longitudinal studies to confirm some of the proposed causal associations. The information discovered would further the development and/or adaptation of clinical patient guidelines to patients with multimorbidity who are taking multiple drugs.

Stroke, multimorbidity and polypharmacy in a nationally representative sample of 1,424,378 patients in Scotland: implications for treatment burden

“…First, as this study and others have shown, the number of medications increases with number of conditions [20,21]. Second, treatments may interact, leading to side effects [5,7,46] and this has the potential to further increase the volume of work; for example, as new treatments are given to compensate for interactions [47]. Third, multimorbidity is likely to increase healthcare contacts and affect the capacity of the individual to follow therapeutic regimens [48]; for example, those with stroke and comorbid arthritis may find physiotherapy sessions more challenging [49,50].…”

“…This issue has been gaining prominence [58,59]. Guidelines should be redesigned to take account of comorbidity and treatment burden; for example, by providing guidance on potential interactions from drug combinations commonly prescribed for those with stroke and multimorbidity and how to deal with the possible side effects or interactions that may arise [47]. In the current study, 21.9% of people with stroke had a painful condition, 20.7% had depression and 13.0% had atrial fibrillation, increasing the risk of being prescribed non-steroidal anti-infammatory drugs (NSAIDs), The stroke group comprised 35,690 people, and the stroke-free group comprised 1,388,688 people.…”

The Drug Prescription Network: A System-Level View of Drug Co-Prescription in Community-Dwelling Elderly People

“…The existence of patterns of poly-pharmacy showing the non-random associations in drug prescriptions has also been demonstrated. 11 The present work was performed to evaluate whether the DPN is a good approach for studying the complexity of drug prescription in older persons. Specifically, we aimed at generating, analyzing, and comparing a series of DPNs to elucidate the structure of the DPNs and the possible differences due to changes in drug co-prescription across time and space.…”

Lichen sclerosus and data science: using an age, site and gender specific disease to define correlation and causality

“…New information on transporter mechanisms, which have substrate and end organ specificity, has improved our understanding of inter-organ variability in drug response. Menopause changes cholesterol and bile metabolism and drugs impact key regulators of cholesterol and bile metabolism [13,14]. Bile salts and the gut microbiome provide the steroid substrate precursors essential for steroidogenesis.…”