Thursday, December 26, 2024
12/26/2024
ABSTRACT Delirium, or acute confusional state, affects 30-40% of hospitalized older adults, with the added cost of care es- timated to be up to $7 billion. Medications are one of the most common causes of delirium and potentially one of the most preventable and most treatable, with medications thought to be the sole precipitant for delirium in up to 39% of cases. Despite the critical role of medications as a source of delirium, surprisingly few studies have investigated this topic, often focused on only a few medications and with only modest quality. In this proposal, we will address these limitations by utilizing data collected through our Virtual Acute Care for Elders (ACE) quality improvement project, which instituted delirium screening once per shift by nursing staff for all individuals over age 65 admitted to University of Alabama at Birmingham (UAB) Hospital for the past decade. This unprecedented volume of data on more than 81,000 patients will allow us to achieve the necessary sample sizes for effective ap- plication of data mining algorithms. Data mining algorithms that discover patterns of associations in data, rather than testing predetermined hypotheses, are well suited to application in large-scale algorithms to identify med- ications associated with increased odds of delirium. Combining data mining with our Virtual ACE and hospital electronic health record (EHR) data, we will be able to evaluate more than 1,000 individual medications, as well as medication combinations and medication classes, for their association with delirium. Such a comprehensive, data-driven examination of delirium due to medications will be a significant contribution to our understanding of the risk factors and management of delirium in the older adult population.
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