Identifying frequent drug combinations associated with delirium in older adults: Application of association rules method to a case‐time‐control design

• Published in: Pharmacoepidemiology and drug safety Vol. 30; no. 10; pp. 1402 - 1410
• Main Authors: Chyou, Te‐Yuan, Nishtala, Prasad S.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.10.2021; Wiley Subscription Services, Inc
• Subjects: Aged; Aged, 80 and over; Antihistamines; Antimicrobial agents; Antipsychotics; association rules; Benzodiazepines; case‐time‐control design; Datasets; Delirium; Delirium - chemically induced; Delirium - diagnosis; Delirium - epidemiology; Diuretics; Drug Combinations; Drug development; Fentanyl; Furosemide; Haloperidol; Humans; Inflammation; Lorazepam; Narcotics; New Zealand; Older people; Omeprazole; Opioids; pharmacoepidemiology; pharmacovigilance; Quetiapine; New Zealand; association rules; older people; pharmacovigilance; case-time-control design; delirium; pharmacoepidemiology
• ISSN: 1053-8569; 1099-1557
• DOI: 10.1002/pds.5292

Background Older adults are at an increased risk of delirium because of age, polypharmacy, multiple comorbidities, frailty, and acute illness. Although medication‐induced delirium in older adults is well understood, limited population‐level evidence is available, particularly on combinations of medications associated with delirium in older adults. Objectives We aimed to apply association rule analysis to identify drug combinations contributing to delirium risk in adults aged 65 and older using a case‐time‐control design. Method We sourced a nationwide representative sample of New Zealander's aged ≥65 years from the pharmaceutical collections and hospital discharge information. Prescription records (2005–2015) were obtained from New Zealand pharmaceutical collections (Pharms). Medication exposures were coded as binary variables (exposed vs. not exposed) at the individual drug level. All medications, including antimicrobials, antihistamines, diuretics, opioids, and nonsteroidal anti‐inflammatory medications, were considered drugs of interest. The first‐time coded diagnosis of delirium was extracted from the National Minimal Dataset (NMDS). A unique patient identifier linked the prescription dataset to the event dataset to set up a case‐time‐control cohort, indexed at the first delirium event. Association rules were then applied to identify frequent drug combinations in the case and the control periods (l‐day with a 35‐day washout period) that are statistically associated with delirium, and the association was tested by computing a time‐trend adjusted matched odds‐ratio (MOR) and its 95% confidence interval (CI). Results We identified 28 503 individuals (mean age 84.1 years) from 2005 to 2015 with delirium. Our combined association rule and case‐time‐control analysis identified several drug classes, including antipsychotics, benzodiazepines, opioids, and diuretics associated with delirium. Our analysis also identified frequently used drug combinations that are associated with delirium. Examples include combined exposures to quetiapine and furosemide (MOR = 6.17; 95%CI = [2.05–18.54]), haloperidol (MOR = 4.81; 95%CI = [3.16–6.69]), combined exposures to furosemide, omeprazole, and lorazepam (MOR = 3.94; 95%CI = [3.03–5.10]), and fentanyl exposure (MOR = 3.46; 95%CI [2.05–9.21]). Conclusion The association rule method applied to a case‐time‐control design is a novel approach to identifying drug combinations contributing to delirium with adjustment for any temporal trends in exposures. The study provides new insight into the combination of medicines linked to delirium.