Publications

BACKGROUND: Hospitalized older adults are commonly discharged with changes to antihypertensive and glucose-lowering (cardiometabolic) medications. Though adverse drug events remain a leading cause of readmissions, there is little contemporary data on how medication discharge planning is communicated and how often medication errors occur post-discharge.

OBJECTIVE: To assess older adults' post-hospital medication use and ambulatory follow-up after receiving cardiometabolic medication changes during hospitalization.

DESIGN: Prospective cohort study from 11/2022 to 01/2024.

PARTICIPANTS: Adults aged 65 years or older from discharged home from an academic medical center with changes to pre-admission cardiometabolic medications.

MAIN MEASURES: Participants completed 7- and 90-day telephonic surveys on health status, medication use, and discharge planning. Self-report of medication use was compared to discharge summaries to identify medication errors (not initiating, not stopping, or taking incorrect dose). Multivariable regression models were used to identify characteristics associated with errors.

KEY RESULTS: The cohort included 151 participants (median [IQR] age 74 [70-78] years; 54% male; 17% Black, 82% White, 41% frail). Participants were admitted with a median (IQR) of 3 (2-4) cardiometabolic medications and discharged with a median (IQR) of 2 (1-4) medication changes. Of the 319 individual medications changed at discharge, 33% were further modified by 90 days. Participants reported comprehensive medication discharge planning for only 13% of medication changes. Though 93% of participants reported they understood the purpose of each of their medications at discharge, 39% had ≥ 1 medication errors at 7 days and 50% at 90 days. Use of ≥ 5 cardiometabolic medications was associated with higher rates of medication errors at 7 days (IRR 1.63; 95% CI 1.07-2.48) and 90 days (IRR 1.66; 95% CI 1.13-2.45).

CONCLUSIONS: Most hospitalized older adults discharged with cardiometabolic medication changes experienced medication errors or gaps in discharge planning. Steps to ensure all patients receive high-quality medication discharge planning are needed.

BACKGROUND AND OBJECTIVES: Electronic health record (EHR) tools are widely used to influence prescribing behaviors. However, the application of EHR tools to deprescribing in older adults, particularly persons living with dementia, is understudied, despite the potential to ameliorate cognitive decline by targeting medications like benzodiazepines and anticholinergics. We explored the design and implementation of EHR-based tools for deprescribing using a multi-phase mixed-methods approach.

RESEARCH DESIGN AND METHODS: Within a large healthcare system, we first conducted semi-structured qualitative interviews to understand perspectives with primary care providers (PCPs) and care partners. Transcripts were analyzed using immersion/crystallization approaches to generate themes. Second, we designed potential EHR deprescribing tools, including pre-visit provider and patient messages and provider decision support, using an interdisciplinary team based on the interviews. Third, we conducted additional qualitative interviews of drafted tools. Lastly, we conducted pilot testing of the EHR tools with PCPs.

RESULTS: We conducted interviews with 16 care partners of persons living with dementia and 7 PCPs and pilot testing of tools with 12 PCPs. From qualitative interviews, we observed several key themes around the complexity of managing deprescribing, effective framing for deprescribing interventions, health-system limitations influencing deprescribing, and leveraging technology and EHR deprescribing tools. From pilot testing, we observed the acceptability and feasibility of the tools, with salient concerns including the potential for information overload and ensuring adequate personalization.

DISCUSSION AND IMPLICATIONS: This multi-phase implementation study uncovered ways to use EHR tools to overcome deprescribing barriers, with pilot-tested tools that show promise of acceptability and feasibility, warranting further testing.

BACKGROUND: Traditional, complementary, and integrative (TCI) medicine is an essential component of health systems worldwide, especially in low- and middle-income countries. Despite its widespread use, existing research on the safety, efficacy, and integration of TCI medicine within conventional healthcare systems is fragmented. This fragmentation highlights the urgent need for a clearly defined global research agenda to guide future studies, secure funding, and inform governance in this field.

METHODS: The Traditional, Complementary, and Integrative Medicine Unit at the World Health Organization Headquarters in Geneva, Switzerland coordinated an international research priority-setting exercise using the Child Health and Nutrition Research Initiative (CHNRI) method between June and December 2023. We invited a purposive sample of 120 experts from established academic networks to participate; 53 experts (44.16% response rate) contributed, and 34 of them scored 157 unique research ideas according to five CHNRI criteria: feasibility, effectiveness, deliverability, equity, and potential for disease burden reduction. Additionally, we performed a comparative analysis by generating research priorities using large language models (LLMs), including ChatGPT-4o, Claude 3.5, and Grok 3, and these outputs were compared with the expert-derived priorities.

RESULTS: Top-ranked research priorities focused on chronic disease management (e.g. diabetes, dyslipidemia), geriatric safety (e.g. herb-drug interactions), mental health (e.g. resilience and mood disorders), and integration of TCI into health systems. Priorities varied by income setting. Comparison with LLM-generated lists showed thematic overlap in efficacy and safety but divergence in focus, with LLMs emphasising research capacity, policy, and systems-level priorities.

CONCLUSIONS: We established a global, expert-informed research agenda to guide the future direction of TCI medicine and ensure alignment with public health needs. The comparison with LLMs highlights the complementary potential of artificial intelligence in research governance and agenda-setting.

BACKGROUND: Postoperative delirium is the most common complication following surgery amongst older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, loss of independence and increased health-care costs. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications.

METHODS: Preoperative resting-state electroencephalograms (EEGs) and the Montreal Cognitive Assessment were collected from a prospective observational cohort of 85 older adults (12 cases of delirium) undergoing elective surgery. Four machine learning models were tested and the model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (6 cases of delirium) undergoing elective surgery.

RESULTS: Occipital alpha powers have higher f1-score (0.57 ± 0.07) than frontal alpha powers (0.47 ± 0.07), EEG spectral slowing (0.48 ± 0.08), or modelling of EEG power spectral density into periodic and aperiodic components (0.44 ± 0.09) in the training cohort. Occipital alpha powers plus cognitive scores were able to predict postoperative delirium with area under the receiver operating characteristic curve (AUC) (0.94, 95% CI: [0.86-0.99]), sensitivity (0.83, 95% CI: [0.50-1.00]) and specificity (0.91, 95% CI: [0.82-0.98]) in the validation cohort, and outperformed models incorporating occipital alpha powers alone or cognitive scores alone.

CONCLUSIONS: Whilst the sample size is small and findings require confirmation in larger studies, our results suggest that the thalamocortical circuit exhibits different EEG patterns under stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.

IMPORTANCE: Millions of Americans provide health and function-related help to family (broadly defined). These "care partners" provide critical support; however, they are rarely identified or supported in care delivery.

OBJECTIVES: Conduct a multi-site evaluation of a portal-based intervention designed to identify, engage, and support care partners in primary care settings.

DESIGN: Three days before a visit, patients were sent a portal notification inviting them to complete a questionnaire. Participants reporting caregiving responsibilities were provided a link to the Caregiver Intensity Index™ (CII), an assessment of caregiving intensity with local resources. In-clinic materials were available with information about accessing the CII.

SETTING: Five primary care practices at two health care organizations.

PARTICIPANTS: All patients age 18 + years of age at organization 1 and 21 + at organization 2.

MAIN OUTCOMES: Intervention reach: proportion and characteristics of patients completing the pre-visit survey. Intervention effectiveness: proportion of patients completing the CII.

RESULTS: The demonstration was implemented at 5 primary care practices between September 30, 2022, and May 31, 2024. At Organization 1, 19,407 patients received the pre-visit portal based questionnaire and 8,905 completed it at least once (response rate = 45.9%). At Organization 2, 12,047 patients received the questionnaire; 7,819 completed it at least once (response rate = 64.9%). The majority of pre-visit questionnaires were completed by patients. Patients assisted by care partners in completing the survey were older and less likely to speak English as a first language. About 1 in 5 respondents (16.9% at Organization 1 and 22.8% at Organization 2) reported caregiving responsibilities. Of these respondents, 36.9% and 27.3% completed the CII. Across both organizations, 61% of patients accessing and completing the CI through any of the available means (QR codes on in office materials, link in the pre-visit survey, link in the after-visit summary document) moderate caregiving intensity. Among all participants completing the CII 16.2% (Organization 1) and 19.5% (Organization 2) clicked on at least one resource.

CONCLUSION: To our knowledge, this pilot represents the first-of-its-kind effort to identify and support patients with caregiving responsibilities through a portal-based intervention. Our demonstration suggests a simple portal questionnaire, with the CII linked to resources, and supported with in-clinic materials, may successfully identify, engage, and support care partners, with minimal changes to clinician workflow. This approach may also offer an opportunity to digitally engage more vulnerable patients. As efforts to improve support for care partners gain traction, incorporation of this portal-based intervention offers an opportunity for widespread engagement and support.

OBJECTIVES: Delirium, an acute disorder of attention and cognition, is a preventable contributor to poor outcomes in older adults including future cognitive decline. The goal of this study was to examine the cognitive impact of postoperative delirium.

DESIGN, SETTING, PARTICIPANTS: A secondary analysis of the randomized clinical trial STRIDE (A Strategy to Reduce the Incidence of Postoperative Delirium in Elderly Patients) which included two hundred hip fracture repair patients was conducted.

MEASUREMENTS: Cognitive changes one year after surgery were examined. Modified Clinical Dementia Rating (CDR) and postoperative delirium status were adjudicated by a consensus panel. Delirium Rating Scale-Revised-98(DRS-R-98) was used to measure delirium severity. Data were analyzed using a random-intercept linear spline model, with Mini Mental State Examination (MMSE) and dementia severity (using CDR Sum of Boxes [CDR-SB]), as outcomes.

RESULTS: 36.5% of the overall cohort experienced delirium. When stratified by baseline cognitive status, delirium was associated with a faster rate of decline in MMSE and worsening in CDR-SB in cognitively unimpaired participants, but not in participants with baseline cognitive impairment. We found an estimated delirium associated change in MMSE of 1.52 points and CDR-SB of 1.22 points within one year after surgery associated with postoperative delirium after hip fracture repair in cognitively normal participants.

CONCLUSIONS: This study provides evidence that postoperative delirium is associated with a faster rate of cognitive decline, particularly in cognitively unimpaired individuals. This work highlights the importance of delirium prevention and management strategies in improving long-term cognitive outcomes in older adults.