Methodology for Using Real-World Data From Electronic Health Records to Assess Chemotherapy Administration in Women With Breast Cancer.

PURPOSE: Identification of patients' intended chemotherapy regimens is critical to most research questions conducted in the real-world setting of cancer care. Yet, these data are not routinely available in electronic health records (EHRs) at the specificity required to address these questions. We developed a methodology to identify patients' intended regimens from EHR data in the Optimal Breast Cancer Chemotherapy Dosing (OBCD) study. METHODS: In women older than 18 years, diagnosed with primary stage I-IIIA breast cancer at Kaiser Permanente Northern California (2006-2019), we categorized participants into 24 drug combinations described in National Comprehensive Cancer Network guidelines for breast cancer treatment. Participants were categorized into 50 guideline chemotherapy administration schedules within these combinations using an iterative algorithm process, followed by chart abstraction where necessary. We also identified patients intended to receive nonguideline administration schedules within guideline drug combinations and nonguideline drug combinations. This process was adapted at Kaiser Permanente Washington using abstracted data (2004-2015). RESULTS: In the OBCD cohort, 13,231 women received adjuvant or neoadjuvant chemotherapy, of whom 10,213 (77%) had their intended regimen identified via the algorithm, 2,416 (18%) had their intended regimen identified via abstraction, and 602 (4.5%) could not be identified. Across guideline drug combinations, 111 nonguideline dosing schedules were used, alongside 61 nonguideline drug combinations. A number of factors were associated with requiring abstraction for regimen determination, including: decreasing neighborhood household income, earlier diagnosis year, later stage, nodal status, and human epidermal growth factor receptor 2 (HER2)+ status. CONCLUSION: We describe the challenges and approaches to operationalize complex, real-world data to identify intended chemotherapy regimens in large, observational studies. This methodology can improve efficiency of use of large-scale clinical data in real-world populations, helping answer critical questions to improve care delivery and patient outcomes.

A health-system-embedded deprescribing intervention targeting patients and providers to prevent falls in older adults (STOP-FALLS trial): study protocol for a pragmatic cluster-randomized controlled trial.

BACKGROUND: Central nervous system (CNS) active medications have been consistently linked to falls in older people. However, few randomized trials have evaluated whether CNS-active medication reduction reduces falls and fall-related injuries. The objective of the Reducing CNS-active Medications to Prevent Falls and Injuries in Older Adults (STOP-FALLS) trial is to test the effectiveness of a health-system-embedded deprescribing intervention focused on CNS-active medications on the incidence of medically treated falls among community-dwelling older adults. METHODS: We will conduct a pragmatic, cluster-randomized, parallel-group, controlled clinical trial within Kaiser Permanente Washington to test the effectiveness of a 12-month deprescribing intervention consisting of (1) an educational brochure and self-care handouts mailed to older adults prescribed one or more CNS-active medications (aged 60 + : opioids, benzodiazepines and Z-drugs; aged 65 + : skeletal muscle relaxants, tricyclic antidepressants, and antihistamines) and (2) decision support for their primary health care providers. Outcomes are examined over 18-26 months post-intervention. The primary outcome is first incident (post-baseline) medically treated fall as determined from health plan data. Our sample size calculations ensure at least 80% power to detect a 20% reduction in the rate of medically treated falls for participants receiving care within the intervention (n = 9) versus usual care clinics (n = 9) assuming 18 months of follow-up. Secondary outcomes include medication discontinuation or dose reduction of any target medications. Safety outcomes include serious adverse drug withdrawal events, unintentional overdose, and death. We will also examine medication signetur fields for attempts to decrease medications. We will report factors affecting implementation of the intervention. DISCUSSION: The STOP-FALLS trial will provide new information about whether a health-system-embedded deprescribing intervention that targets older participants and their primary care providers reduces medically treated falls and CNS-active medication use. Insights into factors affecting implementation will inform future research and healthcare organizations that may be interested in replicating the intervention. TRIAL REGISTRATION: ClinicalTrial.gov NCT05689554. Registered on 18 January 2023, retrospectively registered.

Viral strategies for targeting cortical circuits that control cocaine-taking and cocaine-seeking in rodents.

Addiction to cocaine is a chronic disease characterized by persistent drug-taking and drug-seeking behaviors, and a high likelihood of relapse. The prefrontal cortex (PFC) has long been implicated in the development of cocaine addiction, and relapse. However, the PFC is a heterogeneous structure, and understanding the role of PFC subdivisions, cell types and afferent/efferent connections is critical for gaining a comprehensive picture of the contribution of the PFC in addiction-related behaviors. Here we provide an update on the role of the PFC in cocaine addiction from recent work that used viral-mediated optogenetic and chemogenetic tools to study the role of the PFC in drug-taking and drug-seeking behavior in rodents. Following overviews of rodent PFC neuroanatomy and of viral-mediated optogenetic and chemogenetic techniques, we review studies of manipulations within the PFC, followed by a review of work that utilized targeted manipulations to PFC inputs and outputs.