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IntroductionThe development of biomarkers for Alzheimer's disease (AD) has allowed researchers to increase sample homogeneity and test candidate treatments earlier in the disease. The integration of biomarker "screening" criteria should be met with a parallel implementation of standardized methods to disclose biomarker testing results to research participants; however, the extent to which protocolized disclosure occurs in trials is unknown.MethodsWe reviewed the literature to identify prodromal AD trials published in the past 10 years. From these, we quantified the frequency of biomarker disclosure reporting and the depth of descriptions provided.ResultsOf 30 published trials using positron emission tomography or cerebrospinal fluid-based amyloid positivity as an eligibility criterion, only one mentioned disclosure, with no details on methods.DiscussionPossible reasons for and implications of this information gap are discussed. Recommendations are provided for trialists considering biomarker screening as part of intervention trials focused on prodromal AD.HighlightsFew prodromal Alzheimer's disease (AD) trial papers discuss biomarker disclosure. Disclosure has implications for participants, family members, and trial success. Disclosure must be consistently integrated and reported in prodromal AD trials. Best practice guidelines and training resources for disclosure are needed.

Patient-family member dyads experience transitions through illness as an interdependent team. This study measures the association of depression, anxiety, and health-related quality of life (HRQOL) of older adult primary care patient-family member dyads. Baseline data from 1,808 patient-family member dyads enrolled in a trial testing early detection of Alzheimer's disease and related dementias in primary care. Actor-Partner Independence Model was used to analyze dyadic relationships between patients' and family members' depression (PHQ-9), anxiety (GAD-7), and HRQOL (SF-36 Physical Component Summary score and Mental Component Summary score). Family member mean (SD) age is 64.2 (13) years; 32.2% male; 84.6% White; and 64.8% being the patient's spouse/partner. Patient mean (SD) age is 73.7 (5.7) years; 47% male; and 85.1% White. For HRQOL, there were significant actor effects for patient and family member depression alone and depression and anxiety together on their own HRQOL (p < 0.001). There were significant partner effects where family member depression combined with anxiety was associated with the patient's physical component summary score of the SF-36 (p = 0.010), and where the family member's anxiety alone was associated with the patient's mental component summary score of the SF-36 (p = 0.031). Results from this study reveal that many dyads experience covarying health status (e.g. depression, anxiety) even prior to entering a caregiving situation. [ABSTRACT FROM AUTHOR]

This study examines the relationship of decisional conflict about driving habits between older adult drivers (≥70 years old) and their family members and close friends. This secondary analysis utilizes data originating from a multi-site randomized controlled trial assessing the effect of a driving decision aid (DDA) intervention. Decisional conflict about stopping or changing driving habits for drivers was measured with the Decisional Conflict Scale (DCS). Dyadic associations between drivers' and study partners' (SPs') DCS scores were analyzed using an actor-partner interdependence model. Among 228 driver-SP dyads, Dyadic DCS was correlated at baseline (r =.18, p <.01), and pre-intervention DCS was associated with post-intervention DCS (p <.001 for SPs [β =.73] and drivers [β =.73]). Drivers' baseline DCS and SPs' post-intervention DCS were slighly correlated (β =.10; p =.036). Higher decisional conflict about driving among older drivers is frequently shared by their SPs. Shared decisional conflict may persist beyond intervening to support decision-making about driving cessation. [ABSTRACT FROM AUTHOR]

As many countries experience population aging, patients with cancer are becoming older and have more preexisting comorbidities, which include prevalent, age‐related, chronic conditions such as dementia. People living with dementia (PLWD) are vulnerable to health disparities, and dementia has high potential to complicate and adversely affect care and outcomes across the cancer trajectory. This report offers an overview of dementia and its prevalence among patients with cancer and a summary of the research literature examining cancer care for PLWD. The reviewed research indicates that PLWD are more likely to have cancer diagnosed at an advanced stage, receive no or less extensive cancer treatment, and have poorer survival after a cancer diagnosis. These cancer disparities do not necessarily signify inappropriately later diagnosis or lower treatment of people with dementia as a group, and they are arguably less feasible and appropriate targets for care optimization. The reviewed research indicates that PLWD also have an increased risk of cancer‐related emergency presentations, lower quality processes of cancer‐related decision making, accessibility‐related barriers to cancer investigations and treatment, higher experienced treatment burden and higher caregiver burden for families, and undertreated cancer‐related pain. The authors propose that optimal cancer care for PLWD should focus on proactively minimizing these risk areas and thus must be highly person‐centered, with holistic decision making, individualized reasonable adjustments to practice, and strong inclusion and support of family carers. Comprehensive recommendations are made for clinical practice and future research to help clinicians and providers deliver best and equitable cancer care for PLWD and their families. [ABSTRACT FROM AUTHOR]

Background: Early detection of Alzheimer's disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial.Methods: We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient's physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient.Discussion: This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices.Trial Registration: ClinicalTrials.gov Identifier: NCT05231954 . Registered February 9, 2022. [ABSTRACT FROM AUTHOR]