A targeted analytical framework to optimize posthospitalization delirium pharmacotherapy in patients with Alzheimers disease and related dementias

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Delirium (acute disturbance in mental status) occurs in 46-56% of persons living with dementia (PLWDs) during hospitalization. Alzheimer’s disease and related dementias (ADRD) are among the strongest risk factors for developing delirium during hospitalization. Although an off-label use, antipsychotic medications (APMs) are the most commonly used pharmacotherapy to manage psychological symptoms of delirium. Because PLWDs often have a prolonged recovery course from delirium due to acute illness, ~30% of the patients who newly initiate an APM during hospitalization are discharged with them, and >60% of those discharged with an APM persist for >6 weeks. Since APMs may cause numerous life-threatening adverse reactions, it is critical to discontinue them after hospitalization in a timely fashion. However, several critical knowledge gaps limit the necessary evidence generation to guide such a deprescribing process: 1) There is currently no direct data from randomized control trials (RCT) on discontinuation of APMs used for delirium because it is extremely difficult to recruit and consent PLWDs or their healthcare proxies when the patient is in an acute delirious state to participate in an RCT, and any interventional study would severely underrepresent frail PLWDs seen in routine care. 2) In the non-randomized settings, adjusting for confounding is challenging when comparing different deprescribing strategies of a medication used for acute delirium, and the detailed clinical information required for such analyses is not typically available in routine care data. Our objective is to establish an analytical framework that enables valid causal effect estimation comparing continuation and multiple deprescribing strategies (e.g., abrupt discontinuation vs. gradual dose reduction) of APMs in PLWDs with delirium after hospitalization. We will integrate electronic health records (EHR), national claims data, and multiple clinical assessment data, covering >502,000 PLWDs from 2013 to 2026, and employ high-dimensional machine- learning aided confounding adjustment and phenotyping algorithms. Our specific aims include 1) To integrate EHR with Medicare claims data, Minimum Data Set (MDS), Outcomes and Assessment Information Set (OASIS), and Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI) and to develop novel algorithms to determine key clinical phenotypes; 2) To assess APM utilization/discontinuation patterns and risk factors of prolonged use of APMs for delirium in PLWDs after hospitalization; 3) To assess the health impact of different discontinuation strategies (considering the amount and rate of dose reduction) of APMs vs. continuing APMs in PLWDs with delirium after hospitalization. The subgroup effects by key clinical phenotypes, typical vs. atypical APMs, and type of admission will also be determined. This proposal will generate evidence reflecting routine care delivery to inform post-discharge APM management in PLWDs with delirium. It will also establish a generalizable analytical framework assessing the health effects of deprescribing pharmacotherapies for delirium with detailed treatment effect heterogeneity evaluation necessary for precision medicine.