Generalizability of findings from a clinical sample to a community-based sample: A comparison of ADNI and ARIC

Association of white matter hyperintensities with cognitive decline and neurodegeneration

Background

The relationship between white matter hyperintensities (WMH) and the core features of Alzheimer's disease (AD) remains controversial. Further, due to the prevalence of co-pathologies, the precise role of WMH in cognition and neurodegeneration also remains uncertain.

Methods

Herein, we analyzed 1803 participants with available WMH volume data, extracted from the ADNI database, including 756 cognitively normal controls, 783 patients with mild cognitive impairment (MCI), and 264 patients with dementia. Participants were grouped according to cerebrospinal fluid (CSF) pathology (A/T profile) severity. Linear regression analysis was applied to evaluate the factors associated with WMH volume. Modeled by linear mixed-effects, the increase rates (Δ) of the WMH volume, cognition, and typical neurodegenerative markers were assessed. The predictive effectiveness of WMH volume was subsequently tested using Cox regression analysis, and the relationship between WMH/ΔWMH and other indicators such as cognition was explored through linear regression analyses. Furthermore, we explored the interrelationship among amyloid-β deposition, cognition, and WMH using mediation analysis.

Results

Higher WMH volume was associated with older age, lower CSF amyloid-β levels, hypertension, and smoking history (all p ≤ 0.001), as well as cognitive status (MCI, p < 0.001; dementia, p = 0.008), but not with CSF tau levels. These results were further verified in any clinical stage, except hypertension and smoking history in the dementia stage. Although WMH could not predict dementia conversion, its increased levels at baseline were associated with a worse cognitive performance and a more rapid memory decline. Longitudinal analyses showed that baseline dementia and positive amyloid-β status were associated with a greater accrual of WMH volume, and a higher ΔWMH was also correlated with a faster cognitive decline. In contrast, except entorhinal cortex thickness, the WMH volume was not found to be associated with any other neurodegenerative markers. To a lesser extent, WMH mediates the relationship between amyloid-β and cognition.

Conclusion

WMH are non-specific lesions that are associated with amyloid-β deposition, cognitive status, and a variety of vascular risk factors. Despite evidence indicating only a weak relationship with neurodegeneration, early intervention to reduce WMH lesions remains a high priority for preserving cognitive function in the elderly.

Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants

We used indirect brain mapping with virtual lesion tractography to test the hypothesis that the extent of white matter tract disconnection due to white matter hyperintensities (WMH) is associated with corresponding tract-specific cognitive performance decrements. To estimate tract disconnection, WMH masks were extracted from FLAIR MRI data of 481 cognitively intact participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and used as regions of avoidance for fiber tracking in diffusion MRI data from 50 healthy young participants from the Human Connectome Project. Estimated tract disconnection in the right inferior fronto-occipital fasciculus, right frontal aslant tract, and right superior longitudinal fasciculus mediated the effects of WMH volume on executive function. Estimated tract disconnection in the left uncinate fasciculus mediated the effects of WMH volume on memory and in the right frontal aslant tract on language. In a subset of ADNI control participants with amyloid data, positive status increased the probability of periventricular WMH and moderated the relationship between WMH burden and tract disconnection in executive function performance.

A Comparison of an Australian Observational Longitudinal Alzheimer's Disease Cohort to Community-Based Australian Data

Background

Observational Alzheimer's disease (AD) cohorts including the Australian, Biomarkers, Imaging and Lifestyle (AIBL) Study have enhanced our understanding of AD. The generalizability of findings from AIBL to the general population has yet to be studied.

Objective

We aimed to compare characteristics of people with AD dementia in AIBL to 1) the general population of older Australians using pharmacological treatment for AD dementia, and to 2) the general population of older Australians who self-reported a diagnosis of dementia.

Methods

Descriptive study comparing people aged 65 years of over (1) in AIBL that had a diagnosis of AD dementia, (2) dispensed with pharmacological treatment for AD in Australia in 2021 linked to the Australian census in 2021 (refer to as PBS/census), (3) self-reported a diagnosis of dementia in the 2021 Australian census (refer to as dementia/census). Baseline characteristics included age, sex, highest education attainment, primary language, and medical co-morbidities.

Results

Participants in AIBL were younger, had more years of education, and had a lower culturally and linguistically diverse (CALD) population compared to the PBS/census cohort and dementia/census cohort (mean age±standard deviation - AIBL 79±7 years, PBS/census 81±7, p < 0.001, dementia/census 83±8, p < 0.001; greater than 12 years of education AIBL 40%, PBS/census 35%, p = 0.020, dementia/census 29%, p < 0.001; CALD - AIBL 3%, PBS/census 20%, p < 0.001, dementia/census 22%, p < 0.001).

Conclusions

Our findings suggest that care should be taken regarding the generalizability of AIBL in CALD populations and the interpretation of results on the natural history of AD.

The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022

The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.

Different cohort, disparate results: Selection bias is a key factor in autopsy cohorts

INTRODUCTION

Research-oriented autopsy cohorts provide critical insights into dementia pathobiology. However, different studies sometimes report disparate findings, partially because each study has its own recruitment biases. We hypothesized that a straightforward metric, related to the percentage of research volunteers cognitively normal at recruitment, would predict other inter-cohort differences.

METHODS

The National Alzheimer's Coordinating Center (NACC) provided data on N = 7178 autopsied participants from 28 individual research centers. Research cohorts were grouped based on the proportion of participants with normal cognition at initial clinical visit.

RESULTS

Cohorts with more participants who were cognitively normal at recruitment contained more individuals who were older, female, had lower frequencies of apolipoprotein E ε4, Lewy body disease, and frontotemporal dementia, but higher rates of cerebrovascular disease. Alzheimer's disease (AD) pathology was little different between groups.

DISCUSSION

The percentage of participants recruited while cognitively normal predicted differences in findings in autopsy research cohorts. Most differences were in non-AD pathologies.

HIGHLIGHTS

Systematic differences exist between autopsy cohorts that serve dementia research. We propose a metric to use for gauging a research-oriented autopsy cohort. It is essential to consider the characteristics of autopsy cohorts.

How Generalizable Are Findings from a Community-Based Prospective Cohort Study? Extending Estimates from the Adult Changes in Thought Study to Its Source Population

Background

The Adult Changes in Thought (ACT) study is a cohort of Kaiser Permanente Washington members ages 65+ that began in 1994.

Objective

We wanted to know how well ACT participants represented all older adults in the region, and how well ACT findings on eye disease and its relationship with Alzheimer's disease generalized to all older adults in the Seattle Metropolitan Region.

Methods

We used participation weights derived from pooling ACT and Behavioral Risk Factor Surveillance System (BRFSS) data to estimate prevalences of common eye diseases and their associations with Alzheimer's disease incidence. Cox proportional hazards models accounted for age, education, smoking, sex, and APOE genotype. Confidence intervals for weighted analyses were bootstrapped to account for error in estimating the weights.

Results

ACT participants were fairly similar to older adults in the region. The largest differences were more self-reported current cholesterol medication use in BRFSS and higher proportions with low education in ACT. Incorporating the weights had little impact on prevalence estimates for age-related macular degeneration or glaucoma. Weighted estimates were slightly higher for diabetic retinopathy (weighted 5.7% (95% Confidence Interval 4.3, 7.1); unweighted 4.1% (3.6, 4.6)) and cataract history (weighted 51.8% (49.6, 54.3); unweighted 48.6% (47.3, 49.9)). The weighted hazard ratio for recent diabetic retinopathy diagnosis and Alzheimer's disease was 1.84 (0.34, 4.29), versus 1.32 (0.87, 2.00) in unweighted ACT.

Conclusions

Most, but not all, associations were similar after participation weighting. Even in community-based cohorts, extending inferences to broader populations may benefit from evaluation with participation weights.

Artificial intelligence for dementia research methods optimization

Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

Predicting amyloid-beta pathology in the general population

INTRODUCTION

Reliable models to predict amyloid beta (Aβ) positivity in the general aging population are lacking but could become cost-efficient tools to identify individuals at risk of developing Alzheimer's disease.

METHODS

We developed Aβ prediction models in the clinical Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) Study (n = 4,119) including a broad range of easily ascertainable predictors (demographics, cognition and daily functioning, health and lifestyle factors). Importantly, we determined the generalizability of our models in the population-based Rotterdam Study (n = 500).

RESULTS

The best performing model in the A4 Study (area under the curve [AUC] = 0.73 [0.69-0.76]), including age, apolipoprotein E (APOE) ε4 genotype, family history of dementia, and subjective and objective measures of cognition, walking duration and sleep behavior, was validated in the independent Rotterdam Study with higher accuracy (AUC = 0.85 [0.81-0.89]). Yet, the improvement relative to a model including only age and APOE ε4 was marginal.

DISCUSSION

Aβ prediction models including inexpensive and non-invasive measures were successfully applied to a general population-derived sample more representative of typical older non-demented adults.

Biomarkers of Alzheimer's disease in Black and/or African American Alzheimer's Disease Neuroimaging Initiative (ADNI) participants

Majority of dementia research is conducted in non-Hispanic White participants despite a greater prevalence of dementia in other racial groups. To obtain a better understanding of biomarker presentation of Alzheimer's disease (AD) in the non-Hispanic White population, this study exclusively examined AD biomarker abnormalities in 85 Black and/or African American participants within the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants were classified by the ADNI into 3 clinical groups: cognitively normal, mild cognitive impairment, or dementia. Data examined included demographics, apolipoprotein E (APOE) ε4, cerebrospinal fluid (CSF) Aβ1-42, CSF total tau (t-tau), CSF phosphorylated tau (p-tau), 3T magnetic resonance imaging (MRI), and measures of cognition and function. Analyses of variance and covariance showed lower cortical thickness in 5 of 7 selected MRI regions, lower hippocampal volume, greater volume of white matter hyperintensities, lower measures of cognition and function, lower measures of CSF Aβ1-42, and greater measures of CSF t-tau and p-tau between clinical groups. Our findings confirmed greater AD biomarker abnormalities between clinical groups in this sample.

Emerging diagnostics and therapeutics for Alzheimer disease

Alzheimer disease (AD) is the most common contributor to dementia in the world, but strategies that slow or prevent its clinical progression have largely remained elusive, until recently. This Review highlights the latest advances in biomarker technologies and therapeutic development to improve AD diagnosis and treatment. We review recent results that enable pathological staging of AD with neuroimaging and fluid-based biomarkers, with a particular emphasis on the role of amyloid, tau and neuroinflammation in disease pathogenesis. We discuss the lessons learned from randomized controlled trials, including some supporting the proposal that certain anti-amyloid antibodies slow cognitive decline during the mildly symptomatic phase of AD. In addition, we highlight evidence for newly identified therapeutic targets that may be able to modify AD pathogenesis and progression. Collectively, these recent discoveries-and the research directions that they open-have the potential to move AD clinical care toward disease-modifying treatment strategies with maximal benefits for patients.

Improving generalizability and study design of Alzheimer's disease cohort studies in the United States by including under-represented populations

The poor generalizability of clinical research data due to the enrollment of highly educated, non-Latinx White participants hampers the development of therapies for Alzheimer's disease (AD). Black and Latinx older adults have a greater risk for dementia, yet it is unclear how health-care disparities and sociocultural factors influence potential AD therapies and prognosis. Low enrollment of under-represented populations may be attributable to several factors including greater exclusion due to higher rates of comorbidities, lower access to AD clinics, and the legacy of unethical treatment in medical research. This perspective outlines solutions tested in the Brain Health Registry (BHR) and the Alzheimer's Disease Neuroimaging Initiative (ADNI), including culturally-informed digital research methods, community-engaged research strategies, leadership from under-represented communities, and the reduction of exclusion criteria based on comorbidities. Our successes demonstrate that it is possible to increase the inclusion and engagement of under-represented populations into US-based clinical studies, thereby increasing the generalizability of their results.

John J, Kono N, Henderson VW, Mack WJ. Generalizability of cognitive results from clinical trial participants to an older adult population: Addressing external validity

Introduction

Study inclusion criteria and recruitment practices limit the generalizability of randomized-controlled trial (RCT) results. Statistical modeling could enhance generalizability of outcomes. To illustrate this, the cognition-depression relationship was assessed with and without adjustment relative to the target population of older women.

Methods

Randomized participants from four RCTs and non-randomized participants from two cohorts were included in this study. Prediction models estimated probability of being randomized into trials from target populations. These probabilities were used for inverse odds weighting relative to target populations. Weighted linear regression was used to assess the depression-cognition relationship.

Results

There was no depression-cognition relationship in the combined randomized sample. After applying weights relative to a representative cohort, negative relationships were observed. After applying weights relative to a non-representative cohort, bias of estimates increased.

Discussion

Quantitative approaches to transportability using representative samples may explain the absence of a-priori established relationships in RCTs.

Artificial Intelligence for Dementia Research Methods Optimization

INTRODUCTION

Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.

METHODS

We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research.

RESULTS

We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future.

DISCUSSION

ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.

Apolipoprotein E (APOE) genotype, dementia, and memory performance among Caribbean Hispanic versus US populations

INTRODUCTION

Apolipoprotein E (APOE) is considered the major susceptibility gene for developing Alzheimer's disease. However, the strength of this risk factor is not well established across diverse Hispanic populations.

METHODS

We investigated the associations among APOE genotype, dementia prevalence, and memory performance (immediate and delayed recall scores) in Caribbean Hispanics (CH), African Americans (AA), Hispanic Americans (HA) and non-Hispanic White Americans (NHW). Multivariable logistic regressions and negative binomial regressions were used to examine these associations by subsample.

RESULTS

Our final dataset included 13,516 participants (5198 men, 8318 women) across all subsamples, with a mean age of 74.8 years. Prevalence of APOE ε4 allele was similar in CHs, HAs, and NHWs (21.8%-25.4%), but was substantially higher in AAs (33.6%; P < 0.001). APOE ε4 carriers had higher dementia prevalence across all groups.

DISCUSSION

APOE ε4 was similarly associated with increased relative risk of dementia and lower memory performance in all subsamples.

Correlates of Coronavirus Disease 2019 Inpatient Mortality at a Southern California Community Hospital With a Predominantly Hispanic/Latino Adult Population

Background

Studies of inpatient coronavirus disease 2019 (COVID-19) mortality risk factors have mainly used data from academic medical centers or large multihospital databases and have not examined populations with large proportions of Hispanic/Latino patients. In a retrospective cohort study of 4881 consecutive adult COVID-19 hospitalizations at a single community hospital in Los Angeles County with a majority Hispanic/Latino population, we evaluated factors associated with mortality.

Methods

Data on demographic characteristics, comorbidities, laboratory and clinical results, and COVID-19 therapeutics were abstracted from the electronic medical record. Cox proportional hazards regression modeled statistically significant, independently associated predictors of hospital mortality.

Results

Age ≥65 years (hazard ratio [HR] = 2.66; 95% confidence interval [CI] = 1.90-3.72), male sex (HR = 1.31; 95% CI = 1.07-1.60), renal disease (HR = 1.52; 95% CI = 1.18-1.95), cardiovascular disease (HR = 1.45; 95% CI = 1.18-1.78), neurological disease (HR = 1.84; 95% CI = 1.41-2.39), D-dimer ≥500 ng/mL (HR = 2.07; 95% CI = 1.43-3.0), and pulse oxygen level <88% (HR = 1.39; 95% CI = 1.13-1.71) were independently associated with increased mortality. Patient household with (1) multiple COVID-19 cases and (2) Asian, Black, or Hispanic compared with White non-Hispanic race/ethnicity were associated with reduced mortality. In hypoxic COVID-19 inpatients, remdesivir, tocilizumab, and convalescent plasma were associated with reduced mortality, and corticosteroid use was associated with increased mortality.

Conclusions

We corroborate several previously identified mortality risk factors and find evidence that the combination of factors associated with mortality differ between populations.

The Mediating Roles of Neurobiomarkers in the Relationship Between Education and Late-Life Cognition

BACKGROUND

The mediating roles of neuropathologies and neurovascular damage in the relationship between early-life education and later-life cognitive function are unknown.

OBJECTIVE

To examine whether Alzheimer's and neurovascular biomarkers mediate the relationships between education and cognitive functions.

METHODS

Data were from 537 adults aged 55-94 in the Alzheimer's Disease Neuroimaging Initiative 3. We tested whether the relationships between education (continuous, years) and cognitive function (memory, executive functioning, and language composites) were mediated by neuroimaging biomarkers (hippocampal volumes, cortical gray matter volumes, meta-temporal tau PET standard uptake value ratio, and white matter hyperintensity volumes). Models were adjusted for age, race, sex/gender, cardiovascular history, body mass index, depression, and Apolipoprotein E-ɛ4 status.

RESULTS

Hippocampal volumes and white matter hyperintensities partially mediated the relationships between education and cognitive function across all domains (6.43% to 15.72% mediated). The direct effects of education on each cognitive domain were strong and statistically significant.

CONCLUSIONS

Commonly measured neurobiomarkers only partially mediate the relationships between education and multi-domain cognitive function.

Association of Traumatic Brain Injury with Late Life Neuropathological Outcomes in a Community-Based Cohort

BACKGROUND

Prior studies into the association of head trauma with neuropathology have been limited by incomplete lifetime neurotrauma exposure characterization.

OBJECTIVE

To investigate the neuropathological sequelae of traumatic brain injury (TBI) in an autopsy sample using three sources of TBI ascertainment, weighting findings to reflect associations in the larger, community-based cohort.

METHODS

Self-reported head trauma with loss of consciousness (LOC) exposure was collected in biennial clinic visits from 780 older adults from the Adult Changes in Thought study who later died and donated their brain for research. Self-report data were supplemented with medical record abstraction, and, for 244 people, structured interviews on lifetime head trauma. Neuropathology outcomes included Braak stage, CERAD neuritic plaque density, Lewy body distribution, vascular pathology, hippocampal sclerosis, and cerebral/cortical atrophy. Exposures were TBI with or without LOC. Modified Poisson regressions adjusting for age, sex, education, and APOE ɛ4 genotype were weighted back to the full cohort of 5,546 participants.

RESULTS

TBI with LOC was associated with the presence of cerebral cortical atrophy (Relative Risk 1.22, 95% CI 1.02, 1.42). None of the other outcomes was associated with TBI with or without LOC.

CONCLUSION

TBI with LOC was associated with increased risk of cerebral cortical atrophy. Despite our enhanced TBI ascertainment, we found no association with the Alzheimer's disease-related neuropathologic outcomes among people who survived to at least age 65 without dementia. This suggests the pathophysiological processes underlying post-traumatic neurodegeneration are distinct from the hallmark pathologies of Alzheimer's disease.

Closing the Gap Between Observational Research and Randomized Controlled Trials for Prevention of Alzheimer Disease and Dementia

Although observational studies have identified modifiable risk factors for Alzheimer disease and related dementias (ADRD), randomized controlled trials (RCTs) of risk factor modification for ADRD prevention have been inconsistent or inconclusive. This finding suggests a need to improve translation between observational studies and RCTs. However, many common features of observational studies reduce their relevance to designing related RCTs. Observational studies routinely differ from RCTs with respect to eligibility criteria, study population, length of follow-up, treatment conditions, outcomes, and effect estimates. Using the motivating example of blood pressure reduction for ADRD prevention, we illustrate the need for a tighter connection between observational studies and RCTs, discuss barriers to using typically reported observational evidence in developing RCTs, and highlight methods that may be used to make observational research more relevant to clinical trial design. We conclude that the questions asked and answered by observational research can be made more relevant to clinical trial design and that better use of observational data may increase the likelihood of successful, or at least definitive, trials. Although we focus on improving translation of observational studies on risk factors for ADRD to RCTs in ADRD prevention, the overarching themes are broadly applicable to many areas of biomedical research.

A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias

Due to needs surrounding rigor and reproducibility, subgroup specific disease knowledge, and questions of external validity, data harmonization is an essential tool in population neuroscience of Alzheimer's disease and related dementias (ADRD). Systematic harmonization of data elements is necessary to pool information from heterogeneous samples, and such pooling allows more expansive evaluations of health disparities, more precise effect estimates, and more opportunities to discover effective prevention or treatment strategies. The key goal of this Tutorial in Population Neuroimaging Curriculum, Instruction, and Pedagogy article is to guide researchers in creating a customized population neuroscience of ADRD harmonization training plan to fit their needs or those of their mentees. We provide brief guidance for retrospective data harmonization of multiple data types in this area, including: (1) clinical and demographic, (2) neuropsychological, and (3) neuroimaging data. Core competencies and skills are reviewed, and resources are provided to fill gaps in training as well as data needs. We close with an example study in which harmonization is a critical tool. While several aspects of this tutorial focus specifically on ADRD, the concepts and resources are likely to benefit population neuroscientists working in a range of research areas.

Self-reported word-finding complaints are associated with cerebrospinal fluid amyloid beta and atrophy in cognitively normal older adults

INTRODUCTION

Self-reported word-finding difficulties are among the most frequent complaints in cognitively normal (CN) older adults. However, the clinical significance is still debated.

METHODS

We selected 239 CN from the Alzheimer's Disease Neuroimaging Initiative database who had completed the Everyday Cognition (ECog) questionnaire, as well as a lumbar puncture for amyloid beta (Aβ) and magnetic resonance imaging.

RESULTS

Word-finding complaints, with a few other memory items, were significantly more severe compared to all other cognitive complaints. Ecog-Lang1 (Forgetting names of objects) severity significantly predicted Aβ levels in CN, even when controlling for general cognitive complaint, demographic, and psychological variables. Individuals with high Ecog-Lang1 complaints showed atrophy in the left fusiform gyrus and the left rolandic operculum compared to CN with low complaints.

DISCUSSION

Overall, our results support the fact that word-finding complaints should be taken seriously. They have the potential to identify CN at risk of AD and support the need to include other cognitive domains in the investigation of subjective cognitive decline.

Evaluation of the Cost-effectiveness of Drug Treatment for Alzheimer Disease in a Simulation Model That Includes Caregiver and Societal Factors

Importance

The possibility of widespread use of a novel effective therapy for Alzheimer disease (AD) will present important clinical, policy, and financial challenges.

Objective

To describe how including different patient, caregiver, and societal treatment-related factors affects estimates of the cost-effectiveness of a hypothetical disease-modifying AD treatment.

Design, Setting, and Participants

In this economic evaluation, the Alzheimer Disease Archimedes Condition Event Simulator was used to simulate the prognosis of a hypothetical cohort of patients selected from the Alzheimer Disease Neuroimaging Initiative database who received the diagnosis of mild cognitive impairment (MCI). Scenario analyses that varied costs and quality of life inputs relevant to patients and caregivers were conducted. The analysis was designed and conducted from June 15, 2019, to September 30, 2020.

Exposures

A hypothetical drug that would delay progression to dementia in individuals with MCI compared with usual care.

Main Outcomes and Measures

Incremental cost-effectiveness ratio (ICER), measured by cost per quality-adjusted life-year (QALY) gained.

Results

The model included a simulated cohort of patients who scored between 24 and 30 on the Mini-Mental State Examination and had a global Clinical Dementia Rating scale of 0.5, with a required memory box score of 0.5 or higher, at baseline. Using a health care sector perspective, which included only individual patient health care costs, the ICER for the hypothetical treatment was $192 000 per QALY gained. The result decreased to $183 000 per QALY gained in a traditional societal perspective analysis with the inclusion of patient non-health care costs. The inclusion of estimated caregiver health care costs produced almost no change in the ICER, but the inclusion of QALYs gained by caregivers led to a substantial reduction in the ICER for the hypothetical treatment, to $107 000 per QALY gained in the health sector perspective. In the societal perspective scenario, with the broadest inclusion of patient and caregiver factors, the ICER decreased to $74 000 per added QALY.

Conclusions and Relevance

The findings of this economic evaluation suggest that policy makers should be aware that efforts to estimate and include the effects of AD treatments outside those on patients themselves can affect the results of the cost-effectiveness analyses that often underpin assessments of the value of new treatments. Further research and debate on including these factors in assessments that will inform discussions on fair pricing for new treatments are needed.