Alzheimer's & Dementia: Translational Research & Clinical Interventions

BackgroundAnticholinergic medications are widely used, however their use in older people has been linked to cognitive decline, dementia and increased mortality. This systematic review examines the literature investigating relationships between anticholinergic burden and risk of dementia, cognitive impairment, and outcomes in dementia. MethodsCochrane database and PubMed searches using the terms "anticholinergic" and "dementia" or "cognition" were performed up to May 2023. Outcomes included: (i) dementia diagnosis, (ii) cognitive outcomes in people without dementia (iii) cognitive outcomes, hospitalisation and death in people with dementia. Inclusion and exclusion criteria were defined, and papers were evaluated for inclusion by two researchers independently. Papers examining these relationships specifically for urinary drugs and antidepressants were also analysed separately. ResultsSixty observational studies met our criteria across the three outcomes of interest. Anticholinergic burden was found to be consistently associated with increased risk of dementia however the relationships with cognitive outcomes were less clear. In people with dementia, there were consistent associations between the anticholinergic burden and mortality (hazard ratio (HR) range: 1.04-1.23) or hospitalisation (HR range: 1.13-4.54) but not for cognitive outcomes. Urological drugs with high anticholinergic burden were associated with a [≥]50% increased mortality risk in people with dementia. ConclusionAnticholinergic burden has been consistently associated with increased dementia incidence. Furthermore, in people with existing dementia, anticholinergic burden is associated with increased mortality and hospitalisation. Associations with cognitive outcomes in people without/with dementia remains uncertain. Clinicians should be advised to exercise caution with anticholinergic medication use in older people.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSINTRODUCTIONC_ST_ABSTreatment response in Alzheimers disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heterogeneous treatment effects (HTE) from observational data while controlling for confounding bias. METHODSWe developed a causal machine learning framework integrating expert-guided causal graphs, complementary HTE estimators, sensitivity analyses, and policy learning. We applied it to cholinesterase inhibitors (ChEIs) in MCI due to AD to patients from the NACC and ADNI cohorts. RESULTSAnalysing 4,049 patients with 12-month and 2,223 with 36-month follow-up, all estimators indicated null or negative long-term ChEI effects on cognitive and functional outcomes, notably on functional measures. ChEIs showed slightly more deleterious effects among men than women. DISCUSSIONThis framework provides a methodology for estimating HTE from observational data. It revealed no beneficial responder subgroups, highlighting the challenge of detecting treatment heterogeneity in moderately sized cohorts. This approach can inform treatment selection for other AD therapies including memantine, anti-amyloid agents, and emerging treatments.

INTRODUCTIONThis retrospective study investigates whether exposition to levodopa/carbidopa (LA/CA) medication is associated with modified Alzheimers disease (AD) trajectories. METHODSMultivariate analysis used cerebrospinal fluid (CSF) biomarker information included in the National Alzheimers Coordinating Center Uniform Data Set for subjects with normal cognition (NC), mild cognitive impairment (MCI) and dementia (DE). Survival analyses examined the progression to MCI/DE and death events. RESULTSLA/CA use is associated with lower levels of CSF amyloid beta, phosphorylated tau (P-tau) and total Tau. After adjusting for age, sex and APOE {varepsilon}4 allele presence, that effect was quantified by negative coefficients of the fitted linear mixed models - P values <0.01 in all cases except for P-tau in the MCI subgroup (P=0.02). No similar effects were identified for other antiparkinsonian drugs. Exposition to LA/CA decreased the progression from MCI to DE (P=0.03). DISCUSSIONThe identified effects of LA/CA exposition on AD biomarkers and progression deserve further investigation in controlled clinical trials.

Background: Three San Francisco health system emergency departments have developed Geriatric Emergency Department (GED) models of care programs supporting and providing care for emergency department (ED) patients at risk for or living with dementia. Each system recognized: 1) the high proportion of older adult ED patients and those at risk for dementia, 2) the need to identify cognitive impairment in older adult ED patients, 3) the importance of developing approaches to connect older adult ED patients and their care partners with resources and diagnostic specialty services. Methods: We describe how each hospital adopted and implemented pragmatic GED models of care to support and improve care for ED patients at risk or living with dementia. We also report the proportion of ED encounters made by patients with dementia histories and the number of these reached by GED programs. Results: Three San Francisco hospitals (a tertiary care, critical access, and large integrated health system-community ED) independently implemented GED programs to support and enhance emergency care for patients living with dementia. Each uses screening and assessment tools to identify patients at risk for cognitive impairment. Each captures screening and assessment data to facilitate care and resources for post-discharge care, ensuring coordinated transitions and support for older adults. Programs varied by target patient population age and staff and resource allocation to support program goals. Site-specific pathways differed by location, patient populations, and support from geriatrics, emergency medicine, palliative medicine, neurology, psychiatry, pharmacy, referral processes, and/or pastoral care. Conclusions: Developing GED care interventions that facilitate care for patients at risk of or living with dementia is possible and sustainable when the pathway aligns with health system leadership goals through persistent value demonstration, communication, and promotion. Ultimately, developing and disseminating models of GED care is designed to address geriatric syndromes inclusive of dementia care through continuous quality improvement.

BackgroundGabapentin prescriptions have increased 123% since 2010, reaching 15.5 million Americans annually. Recent studies suggest gabapentin-dementia associations, but whether concomitant medications modify this risk is unknown. Both gabapentin and calcium channel blockers (CCBs) affect neuronal calcium signaling through distinct mechanisms, raising the possibility of pharmacodynamic interaction. MethodsActive comparator new-user cohort study using Rutgers Clinical Research Data Warehouse (2015-2024). Adults [&ge;]40 years with hypertension initiating gabapentin (n=28,058) or pregabalin (n=5,733) were followed for incident dementia. Inverse probability of treatment weighted (IPTW) Cox models estimated hazard ratios stratified by baseline CCB exposure. Validation analyses tested CCB subtype specificity (dihydropyridine [DHP] vs verapamil), dementia subtypes (F03/G30/F01), frailty stratification (CKD, stroke), lag periods, falsification outcomes, and non-2{delta} anticonvulsant comparisons. ResultsAmong 33,791 patients (502 dementia events; median follow-up 1.22 years), we identified a novel drug-drug interaction: gabapentin was associated with substantially elevated dementia risk among CCB users (HR=2.22, 95% CI 1.42-3.47, p=0.0005) compared to non-users (HR=1.15, 95% CI 0.99-1.33; interaction p=0.004). A time-varying analysis confirmed this finding: among gabapentin users who initiated CCB during follow-up, CCB-exposed person-time showed 65% higher dementia incidence (Rate Ratio=1.65, 95% CI 1.19-2.29). This interaction showed striking CCB subtype specificity: DHP CCBs drove the signal (HR=3.20) while verapamil showed no interaction (insufficient events for analysis). The signal concentrated in F03 unspecified dementia (HR=1.68, p=0.004) with short latency (median 240 days), consistent with drug-induced cognitive impairment rather than neurodegeneration. Pre-index symptom balance analysis showed 6/6 symptom families balanced between groups, arguing against protopathic bias. The interaction was paradoxically weaker in frail patients (CKD ratio=0.25, stroke ratio=0.14), arguing against confounding by illness severity. Lag analyses showed strengthening over time (HR 2.22[-&gt;]3.72), falsification outcomes were largely null (4/7), and non-2{delta} anticonvulsants showed no CCB interaction. ConclusionsWe identified a novel drug-drug interaction whereby DHP CCB co-medication amplifies gabapentin-associated dementia risk, confirmed by time-varying analysis (Rate Ratio=1.65). The DHP-specific signal is biologically plausible given independent evidence that DHP CCBs may adversely affect cognition (DREAM consortium), while the absence of interaction with verapamil aligns with its potential neuroprotective properties identified in drug repurposing studies. The F03-specific pattern suggests drug-induced cognitive impairment that may be reversible. These hypothesis-generating findings identify gabapentin-DHP CCB combinations as a target for cognitive safety monitoring and warrant confirmation with concurrent exposure measurement.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSMild Behavioral Impairment (MBI) is a syndrome of late-life-onset persistent neuropsychiatric symptoms. Anticholinergic medication is commonly prescribed in older adults. Both MBI and anticholinergic exposure are associated with increased dementia risk. We sought to understand the association of anticholinergic burden (ACB) with MBI. Design, Setting, participantsWe mapped ratings on the Neuropsychiatric Inventory Questionnaire to the MBI checklist (MBI-C) using an established algorithm to define MBI status in cognitively unimpaired individuals in the National Alzheimers Coordinating Center database. We then assessed the association between time-varying ACB ratings and risk of incident MBI. Results4865 participants met inclusion criteria and were followed for a mean (SD) of 5.64 (3.92) years. ACB scores ranged from 0-11. 63.3% of participants had a score of 0, 27.7% had a score of 1-2, and 9% had a score of [&ge;]3. Higher maximum total ACB score was associated with a higher likelihood of developing MBI (p=<0.001). When assessed as a time varying covariate, ACB score was associated with incident MBI (HR 1.12, 95% CI 1.05-1.19, p=<0.001). This association remained significant when adjusted for 10-year mortality risk. ConclusionsMBI risk should be considered when prescribing anticholinergic medication in older adults.

INTRODUCTIONPolygenic Risk Scores for Alzheimer dementia (AD-PRS), a measure of aggregate AD genetic risk and polypharmacy have been associated with dementia. Here, we test their interactions association with future dementia among older adults without baseline neurodegenerative diagnoses. METHODSUsing Cox proportional hazards and mortality-adjusted competing risk regression models we analysed up to 17.5 years all-cause incident dementia in the Lothian Birth Cohort 1936 (n=759, 105 dementia patients). We used polypharmacy (total or nervous-system-acting medications count), AD-PRS, and their interaction as main predictors. RESULTSA non-significant interaction was found between AD-PRS and total polypharmacy (HR=1.06; p=0.15) or nervous-system-acting polypharmacy (HR=0.98; p=0.86) in shaping dementia risk. Omitting interaction, mortality-adjusted models showed significant AD-PRS prediction of dementia (HR [~]1.40; p<0.001), non-significant total (HR=1.03; p=0.49), and nervous-system-acting polypharmacy effects (HR=1.27; p=0.069). DISCUSSIONElucidating the complex interplay between polypharmacy and genetics could improve management of inappropriate medication in older adults genetically prone to dementia/AD.

With promising disease-modifying therapies (DMTs) emerging and good evidence to support risk reduction in the delay of dementia onset and progression, it is important to understand the profile of patients attending memory assessment services to estimate what proportion of patients might benefit from different types of interventions. The Oxford Brain Health Clinic (OBHC) is a psychiatry-led, clinical-research service that offers memory clinic patients detailed clinical assessments and equal access to research opportunities as part of their secondary care pathway. In this work, we describe the characteristics of OBHC patients in terms of demographics, diagnoses and prevalence of potentially modifiable risk factors compared with a cohort of healthy volunteers and the average memory clinic population. Our results suggest that high research consent rates (91.5%) in the OBHC resulted in a highly representative cohort of the clinical population. Based on Lecanemab trial inclusion criteria, 24.6% of the OBHC population may be suitable for further investigation into DMTs. Furthermore, 67.4% of OBHC patients have at least one potentially modifiable risk factor that may benefit from lifestyle interventions, particularly those focused on depression, sleep and physical activity.

ObjectivesTo assess the feasibility of linking and comparing markers of dementia-related health recorded in primary care electronic health records (EHR) to assessments of cognitive function undertaken in a specialist dementia service. MethodsOne thousand patients in a UK secondary care specialist dementia service were invited to take part. Primary care EHR were requested from 72 general practices of consenting patients. Sixty-three previously established individual markers within 13 broader domains of dementia-related health were then extracted from primary care EHR and compared to cognitive assessments scores recorded in the dementia service EHR. Results258 (26%) patients consented to take part. At least one cognitive assessment score was recorded for 242 (94%) patients, but primary and secondary care EHR records could only be linked in 93 patients. 56 of these 93 patients had two cognitive assessments scores at least 12 months apart. In the patients with data available for analysis individuals with a higher number of markers and domains recorded in their primary care records had lower mean cognitive assessment scores (range 1.6-2.1 points), and after adjustment for earlier cognitive scores (range 2.0-2.5 points), indicating poorer cognitive function, although differences were not statistically significant. ConclusionThis feasibility study highlights the challenges in obtaining consent and linking primary and secondary care EHR in dementia, and in extracting cognitive function scores from dementia service EHR.

Structured abstractO_ST_ABSBackgroundC_ST_ABSLeveraging machine learning on electronic health records offers a promising method for early identification of individuals at risk for dementia and neurodegenerative diseases. Current risk algorithms heavily rely on age, highlighting the need for alternative models with strong predictive power, especially at age 65, a crucial time for early screening and prevention. MethodsThis prospective study analyzed electronic health records (EHR) from 76,427 adults (age 65, 52.1% women) using the THIN database. A general risk algorithm for Alzheimers disease, Parkinsons disease, and dementia was developed using machine learning to select predictors from diagnoses, and medications. ResultsMedications (e.g., laxatives, urological drugs, antidepressants), along with sex, BMI, and comorbidities, were key predictors. The algorithm achieved a 38.4% detection rate at a 5% false-positive rate for 2-year dementia prediction. ConclusionThe validated prediction algorithms, easy to implement in primary care, identify high-risk 65-year-olds using medication records. Further refinement and broader validation are needed.

BACKGROUNDEffective treatments for neurodegenerative diseases remain elusive, underscoring the importance of preventive strategies. Probucol, a cholesterol lowering and antioxidant drug with established cardiovascular use, has shown neuroprotective effects in preclinical models of dementia by modulating peripheral lipoprotein amyloid metabolism and preserving capillary integrity. However, no large-scale human studies have examined its association with dementia risk. OBJECTIVETo examine the association between probucol use and incident dementia in older adults. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study used the Japan Medical Data Centre claims database from 2014 to 2023. Adults aged 50 years or older prescribed probucol or statins were included, excluding those with prior dementia or recent drug exposure. Participants were categorized as probucol monotherapy users, statin monotherapy users, or combination users ([&ge;]2 prescriptions). Propensity score matching was used to balance baseline comorbidities. EXPOSURESProbucol or statin therapy. MAIN OUTCOMES AND MEASURESThe primary outcome was incident all cause dementia. Secondary outcomes included Alzheimers disease and mixed Alzheimers and vascular dementia. Odds ratios (ORs) with 95% CIs were calculated using logistic regression adjusted for age and sex. RESULTSAmong 57 231 individuals (52.6% female) followed for up to 10 years (median, 3 years), 7 387 (12.9%) developed dementia. The cohort included 2 896 probucol users (5.1%) and 54 335 statin users (94.9%). Dementia incidence was higher among females (14.7%) than males (10.9%). Dementia incidence was lower in probucol users (5.6%, 162/2 896) than in statin users overall (13.2%, 8 248/62 519), with individual statins ranging from 11.4% (fluvastatin) to 16.7% (pravastatin). Probucol use was associated with a 62% lower adjusted risk of dementia compared with all statin users combined (adjusted OR, 0.38; 95% CI, 0.37-0.38). Protective associations were consistent across individual statin comparisons, with adjusted ORs ranging from 0.30 (pitavastatin) to 0.57 (fluvastatin). CONCLUSIONS AND RELEVANCEIn this large national cohort of Japanese adults, probucol use was associated with a substantially lower risk of incident dementia compared with statins. These findings provide the first large-scale human evidence linking probucol exposure with reduced dementia risk, supporting its further evaluation as a preventive therapy in prospective clinical trials. Key PointsO_ST_ABSQuestionC_ST_ABSIs use of the lipid-lowering and antioxidant agent probucol associated with a reduced risk of incident dementia compared with statin therapy in older adults? FindingsIn this nationwide cohort study of 57 231 Japanese adults aged 50 years and older, dementia incidence was nearly halved in probucol users (5.6%) compared with statin users overall (13.2%), corresponding to 62% lower adjusted odds of dementia. Protective associations were consistent across dementia subtypes and individual statins. MeaningThese findings suggest that probucol, a long-standing and well-tolerated cardiovascular drug, may offer a mechanistically distinct and potentially scalable strategy for dementia prevention, warranting confirmation in prospective intervention trials.

BackgroundSecond-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimers disease (AD), despite ongoing concerns about their safety. Comparative evidence on mortality risk across specific SGAs remains limited. ObjectiveTo compare all-cause mortality among AD patients treated with commonly prescribed SGAs and to explore treatment effect heterogeneity using causal machine learning. MethodsWe conducted a retrospective cohort study using de-identified electronic health records from the Truveta platform (2018-2024). Patients with incident AD initiating treatment with aripiprazole, risperidone, quetiapine, or olanzapine were identified using an active-comparator, new-user design. Drug exposure was modeled as a time-varying covariate in Cox proportional hazards models, with propensity score matching applied to control for confounding. Causal tree and targeted maximum likelihood estimation (TMLE) were used to identify subgroups with heterogeneous treatment effects. ResultsAmong 17,004 AD patients, aripiprazole was associated with significantly lower mortality than olanzapine (HR = 0.667, 95% CI: 0.472-0.941) and quetiapine (HR = 0.677, 95% CI: 0.462-0.990). Quetiapine was also associated with lower mortality than olanzapine (HR = 0.833, 95% CI: 0.702-0.990) and risperidone (HR = 0.830, 95% CI: 0.705-0.978). Causal tree analysis revealed treatment effect heterogeneity by clinical characteristics, particularly among patients using type 2 diabetes (T2DM) medications. In subgroup analyses, aripiprazole remained protective in T2DM users (HR = 0.604 vs. quetiapine and risperidone, p = 0.002). ConclusionsMortality risks vary substantially across SGAs in AD patients. Aripiprazole and quetiapine were associated with lower mortality compared to olanzapine and risperidone. Treatment effect heterogeneity suggests the need for individualized prescribing based on patient characteristics such as comorbid T2DM.

Several of the hypothesized or studied exposures that may affect dementia risk are known to increase the risk of death. This may explain counterintuitive results, where exposures that are known to be harmful for mortality risk sometimes seem protective for the risk of dementia. Authors have attempted to explain these counterintuitive results as biased, but the bias associated with a particular analytic method cannot be defined or assessed if the causal question is not explicitly specified. Indeed, we can consider several causal questions when competing events like death, which cannot be prevented by design, are present. Current dementia research guidelines have not explicitly considered what constitutes a meaningful causal question in this setting or, more generally, how this choice justifies and should drive particular analytic decisions. To contextualize current practices, we first perform a systematic review of the conduct and interpretation of longitudinal studies focused on dementia outcomes where death is a competing event. We then describe and demonstrate how to address different causal questions (referred here as "the total effect" and "the controlled direct effect") with traditional analytic approaches under explicit assumptions. Our application focuses on smoking cessation in late-midlife. To illustrate core concepts, we discuss this example both in terms of a hypothetical randomized trial and with an emulation of such a trial using observational data from the Rotterdam Study.

INTRODUCTIONDelaying cognitive decline in Alzheimers disease can significantly impact both function and quality of life. METHODSLongitudinal analysis of National Alzheimers Coordinating Center (NACC) dataset of 7,653 mild dementia CDR-SB AD participants at baseline with prescriptions for diabetes (DBMD), lipid-lowering (LIPL), anti-hypertensive (AHTN), and non-steroidal anti-inflammatory (NSD) medications over 10 years was evaluated for change in cognitive function relative to non-treated stratified by sex and APOE genotype. RESULTSCombination therapy of DBMD+LIPL+AHTN+NSD resulted in a 44% / 35% (MMSE/CDR-SB) delay in cognitive decline at 5 years and 47% / 35% (MMSE/CDR-SB) delay at 10 years. Females and APOE4 carriers exhibited greatest cognitive benefit of combination therapy. DISCUSSIONCombination therapies significantly delayed cognitive decline in NACC AD participants at a magnitude comparable to or greater than beta-amyloid immunomodulator interventions. These data support combination precision medicine targeting AD risk factors to alter the course of the disease that persists for a decade.

BackgroundThe novel South London and Maudsley Brain Health Clinic (SLaM BHC) leverages advances in remote consultations and biomarkers to provide a timely, cost-efficient and accurate diagnosis in mild cognitive impairment (MCI). AimsTo describe the organisation, patient cohort, and acceptability of the remote diagnostic and interventional procedures. MethodWe describe the recruitment, consultation setup, the clinical and biomarker program, and the two online group interventions for cognitive wellbeing and lifestyle change. We evaluate the acceptability of the remote consultations, lumbar puncture (LP), saliva genotyping and remote cognitive and functional assessments. ResultsWe present the results of the first 68 (mean age 73, 55% female, 43% ethnic minority) of 146 patients who enrolled for full remote clinical, cognitive, genetic, cerebrospinal fluid, and neuroimaging phenotyping. 86% were very satisfied/ satisfied with the remote service. 67% consented to LP and 95% of those were very satisfied, all having no significant complications. 93% found taking saliva genotyping very easy/easy and 93% found the cognitive assessments instructions clear. 98% were satisfied with the cognitive wellbeing groups and 90% of goals were achieved in the lifestyle intervention group. ConclusionsThe SLaM BHC provides a highly acceptable and safe clinical model for remote assessments and lumbar punctures in a representative, ethnically diverse population. This allows early and accurate diagnosis of Alzheimers, differentiation from other MCI causes and targets modifiable risk factors. This is crucial for future disease modification, ensuring equitable access to research, and provides precise, timely and cost-efficient diagnoses in UK mental health services.

INTRODUCTION: Dementia, particularly Alzheimer disease (AD), is a major global health challenge, with prevalence projected to reach 150 million cases by 2050. AD is characterized by progressive cognitive decline linked to neuroinflammation and neurodegeneration. Non-steroidal anti-inflammatory drugs (NSAIDs) have been explored as potential neuroprotective agents, particularly diclofenac, which has been proposed to modulate microglial inflammasome signaling. However, prior studies investigating NSAIDs in AD have yielded inconsistent findings. We therefore reexamined the relationship between selected NSAIDs and dementia outcomes in a large longitudinal cohort from the National Alzheimer Coordinating Center (NACC). METHODS: We analyzed cross-sectional and longitudinal data from the NACC database collected between 2005 and 2022. Associations between NSAID exposure and dementia, AD, and cognitive trajectories were examined. Propensity score matching was performed to compare NSAID users with matched non-users while adjusting for demographic and clinical confounders. Longitudinal mixed-effects models were used to assess cognitive decline based on Montreal Cognitive Assessment (MoCA) scores. RESULTS: Among 47,165 participants, diclofenac and naproxen use were associated with a lower prevalence of dementia and AD compared with matched non-users, whereas etodolac showed no significant associations. Diclofenac users demonstrated reduced odds of dementia and AD. Naproxen showed similar cross-sectional associations. In longitudinal modeling, diclofenac users had a significantly slower rate of cognitive decline than non-users. DISCUSSION: These findings suggest a compound-specific association between NSAID use and AD, with diclofenac potentially modulating disease progression through anti-inflammatory mechanisms. The observed modulation of longitudinal cognitive decline supports further investigation of inflammatory pathways, including microglial and inflammasome signaling, as therapeutic targets in biomarker-defined AD populations.

INTRODUCTIONThree distinct pathophysiological subtypes of Alzheimers disease (AD) have been identified based on CSF proteomics. We aim to estimate the potential incremental cost-effectiveness ratio of individualized hypothetical AD pharmacological treatment through stratification by these subtypes. METHODSIn a model-based health-economic evaluation usual care was compared to A) hypothetical treatment to in all subtypes and B) hypothetical treatment to those categorized as subtype 1 (high levels of BACE1) based on CSF proteomics; in a population of persons with MCI and positive amyloid and assuming hypothetical treatment efficacy in this subtype 1 only. RESULTSThe potential incremental cost-effectiveness ratio (ICER) was k{euro}36 per quality-adjusted life year (QALY) for strategy A (no subtyping test, hypothetical treatment in all subtypes) as compared to usual care, and k{euro}22 for strategy B (subtyping and hypothetical treatment for subtype 1 only) as compared to usual care. Compared to strategy A, strategy B was dominant, in terms of no difference in QALY and mean cost savings of k{euro}6 per person. DISCUSSIONGiven the assumptions in this study, individualized hypothetical AD pharmacological treatment of specific subgroups of AD patients, here based on a proteomics biomarker profile, has the potential to gain health-economic benefits. Future research should focus on retrospective analysis of trial data to generate empirical evidence on treatment effect between CSF proteomics subgroups.

ObjectivesGeriatric Emergency Management (GEM) nurses aim to reduce adverse outcomes by addressing unique needs of older adults seen in emergency departments (EDs), but evidence to demonstrate their impact on ED care transitions is mixed. We evaluated the impact of implementing a GEM nurse model in a local ED on thirty-day revisits using propensity score matching to control for relevant patient characteristics. MethodsA case-control design was used to analyze older adult patients who were triaged to a stretcher at an ED in Levis, Quebec, from October 2018 to September 2019. We used propensity score matching to compare patients who received the GEM nurse intervention with control patients who did not receive the intervention. This intervention involved a targeted geriatric ED assessment including history, physical exam, chart review, communication with caregivers and home care services, and the creation of an intervention and care transition plan to support safe discharge from the ED. We followed both the EQUATOR networks brief guidelines for reporting a propensity score analysis and the STROBE guideline. ResultsOut of 21,024 patients visiting the ED over a one-year period, 7,952 were eligible for analysis, with pre-matching differences showing GEM patients were older and more frequent ED users. Propensity score matching resulted in 724 patients with no significant differences in baseline characteristics between groups. Using a Cox regression analysis, we found a non-significant 6% decrease in the risk of ED revisit within 30 days for the GEM group (HR = 0.94, p = .692). ConclusionsThe GEM nursing intervention targeting better care transition plans personalized to the needs of each patient did not significantly impact thirty-day revisits to the ED. Further work is needed to determine the most effective specific components of such interventions to maximize future positive impact on the care transitions of older patients. Clinicians capsuleO_ST_ABSWhat is known about the topic?C_ST_ABSGeriatric emergency management (GEM) nurses heterogeneously contribute to reducing emergency department revisits among older adults but may improve the quality of care. What did this study ask?How would the implementation of a Geriatric Emergency Management (GEM) nurse intervention in a local emergency department (ED) impact 30-day revisits among older adults? What did this study find?Despite not reaching statistical significance, we observed a 6% reduction in revisit rates. Why does this study matter to clinicians?We should refine and support GEM nurse practices, along with shifting outcome measures from service-level metrics to patient-centered metrics like quality of life and symptom burden.

IntroductionDelirium is a cerebral dysfunction seen commonly in the acute care setting. Delirium is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions. ObjectiveOur objective was to identify clinically valuable predictive models for prevalent delirium within the first 24 hours of hospitalization based on the available data by assessing the performance of logistic regression and a variety of machine learning models. MethodsThis was a retrospective cohort study to develop and validate a predictive risk model to detect delirium using patient data obtained around an ED encounter. Data from electronic health records for patients hospitalized from the ED between January 1, 2014, and December 31, 2019, were extracted. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded while hospitalized. The outcome measure of this study was delirium within one day of hospitalization determined by a positive DOSS or CAM assessment. We developed the model with and without the Barthel index for activity of daily living, since this was measured after hospital admission. ResultsThe area under the ROC curves for delirium ranged from .69 to .77 without the Barthel index. Random forest and gradient-boosted machine showed the highest AUC of .77. At the 90% sensitivity threshold, gradient-boosted machine, random forest, and logistic regression achieved a specificity of 35%. After the Barthel index was included, random forest, gradient-boosted machine, and logistic regression models demonstrated the best predictive ability with respective AUCs of .85 to .86. ConclusionThis study demonstrated the use of machine learning algorithms to identify the combination of variables that are predictive of delirium within 24 hours of hospitalization from the ED.

ImportanceInterpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimers disease (AD) and promote early intervention at non-specialist settings. ObjectiveTo develop a risk score to predict the likelihood of AD with interpretable machine learning using variables that are obtainable at integrated primary care settings. DesignA secondary data analysis including cohort studies from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the National Alzheimers Coordinating Center (NACC) extracted in August 2023 and March 2024. SettingThe ADNI and NACC are multi-site cohort studies in North America. ParticipantsParticipants with normal cognition or mild cognitive impairment at baseline visit were identified. Participants with the same diagnosis overtime were assigned to the stable group, and those converted to AD were placed in the progressive group. Main Outcome(s) and Measure(s)Cognitive tests and daily functioning measured with Functional Assessment Questionnaire (FAQ) at baseline visit. ResultsA total of 676 participants from ADNI and 4592 participants from NACC were identified. After removing incomplete data, 665 ADNI (mean age [SD]: 73.44 [6.90]; 293 [44.1%] female; 374 stable and 291 progressive) and 3657 NACC participants (mean age [SD]: 70.96 [10.03]; 2405 [65.8%] female; 2445 stable and 1212 progressive) remained. Combinations of 4 measures were selected to generate 10 scorecards using FasterRisk algorithm, showing strong performance (area under the curve [AUC] = 0.868-0.892) in ADNI and remaining robust when validated in NACC (AUC = 0.795). The features were Category Animal [&le;] 20 (2 points), Trail Making Test B [&le;] 143 (-3 points), Logical Memory Delayed [&le;] 3 (4 points), Logical Memory Delayed [&le;] 8 (3 points), and FAQ [&le;] 2 (-5 points). The probable AD risk corresponded to total points: 7.4% (-8), 25.3% (-4), 50% (-1), 74.7% (2), and > 90% ([&ge;] 6). We refer to this model as the (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or FLAME scorecard. Conclusions and RelevanceOur findings highlight the potential to predict AD development using obtainable information, allowing for applicability at integrated primary care. While our scope centers on AD, this foundation paves the way for other dementia types Key PointsO_ST_ABSQuestionC_ST_ABSCan accessible information, such as demographics, cognitive tests, and functioning questionnaire, yield in reliable results for predicting Alzheimers disease development using interpretable machine learning? FindingsThe results of 665 participants from the Alzheimers Disease Neuroimaging Initiative demonstrated robust performance of determining Alzheimers disease development using four separate measures of (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or the FLAME scorecard. It remains reliable when externally validated with a separate dataset of 3657 participants from the National Alzheimers Coordinating Center. MeaningThe FLAME scorecard shows potential to be implemented in integrated primary care settings to promote early detection and intervention of cognitive decline due to Alzheimers disease.