Computer-assisted prediction of clinical progression in the earliest stages of AD

The criteria used to rule out mild cognitive impairment impact dementia incidence rates in subjective cognitive decline

BACKGROUND

The research criteria for subjective cognitive decline (SCD) exclude mild cognitive impairment (MCI), but do not stipulate the use of specific MCI criteria. This study compared different approaches to defining (i.e., excluding) MCI during the ascertainment of SCD, focusing on the impact on dementia incidence rates in SCD.

METHODS

This cohort study utilized routine healthcare data collected in the Essex Memory Clinic from 1999 to 2023. Two different operationalizations of the SCD criteria were used to categorize the cohort into two SCD patient samples. One sample was based on local clinical practice - MCI was excluded according to the Winblad criteria (this sample was termed SCDWinblad). The other sample was created via the retrospective application of the Jak/Bondi criteria for the exclusion of MCI (termed SCDJak/Bondi). Only patients aged ≥ 55 years at baseline with ≥ 12 months follow-up were considered for inclusion. The initial clinical/demographic characteristics of the samples were compared. Rates of incident dementia were calculated for each sample, and unadjusted and Mantel-Haenszel-adjusted incidence rate ratios were calculated to compare dementia incidence between the SCD samples.

RESULTS

The Essex Memory Clinic database included 2,233 patients in total. The SCD and study eligibility criteria were used to select SCDWinblad (n = 86) and SCDJak/Bondi (n = 185) samples from the database. Median follow-up (3 years) did not differ between the two samples. The SCDJak/Bondi sample was significantly older than the SCDWinblad at first assessment (median age: 74 versus 70 years) and had poorer scores on tests of global cognition, immediate and delayed verbal recall, and category fluency. Following adjustment for age, the dementia incidence rate ratio [95% confidence interval] was 3.7 [1.5 to 9.3], indicating a significantly greater rate of progression to dementia in SCDJak/Bondi.

CONCLUSIONS

This study highlights that the approach used to ascertain SCD has important implications for both SCD phenotypes and prognosis. This underscores the importance of how MCI is operationalized within SCD studies. More broadly, the findings add to a growing body of work indicating that objective cognition should not be overlooked in SCD, and offer a potential explanation for the heterogeneity across the SCD prognostic literature.

Development and design of a diagnostic report to support communication in dementia: Co-creation with patients and care partners

Introduction

Clear communication of diagnostic test results and dementia diagnosis is challenging yet important to empower patients and care partners. A personalized diagnostic report could support the communication of dementia diagnostics and aid patients' understanding of diagnosis. In this study, we aimed to design a diagnostic report in co-creation with patients and care partners.

Methods

We used a mixed-methods approach, combining surveys with focus groups in iteration. Phase 1 consisted of an international survey assessing needs among patients (n = 50) and care partners (n = 46), and phase 2 consisted of focus group meetings (n = 3) to co-create the content and to hands-on co-design the layout of the diagnostic report with patients (n = 7) and care partners (n = 7). Phase 3 validated results from phase 2 in a survey among patients (n = 28) and care partners (n = 12), and phase 4 comprised final feedback by dementia (care) experts (n = 5). Descriptive statistics were used to report quantitative results and directed content analysis was used to analyze qualitative data.

Results

Most patients (39/50, 78%) and care partners (38/46, 83%) positively valued a diagnostic report to summarize test results. The report should be brief, straightforward, and comprise results of the diagnostic tests, including brain imaging and information on future expectations. Despite a clear preference for visual display of test results, several visualization options were deemed best and were equally comprehended.

Discussion

In this study, we developed a prototype of a personalized patient report through an iterative design process and learned that co-creation is highly valuable to meet the specific needs of end-users.

A Neuropsychological Perspective on Defining Cognitive Impairment in the Clinical Study of Alzheimer’s Disease: Towards a More Continuous Approach

The global fight against Alzheimer’s disease (AD) poses unique challenges for the field of neuropsychology. Along with the increased focus on early detection of AD pathophysiology, characterizing the earliest clinical stage of the disease has become a priority. We believe this is an important time for neuropsychology to consider how our approach to the characterization of cognitive impairment can be improved to detect subtle cognitive changes during early-stage AD. The present article aims to provide a critical examination of how we define and measure cognitive status in the context of aging and AD. First, we discuss pitfalls of current methods for defining cognitive impairment within the context of research shifting to earlier (pre)symptomatic disease stages. Next, we introduce a shift towards a more continuous approach for identifying early markers of cognitive decline and characterizing progression and discuss how this may be facilitated by novel assessment approaches. Finally, we summarize potential implications and challenges of characterizing cognitive status using a continuous approach.

Biomarkers of Alzheimer's Disease and Cerebrovascular Lesions and Clinical Progression in Patients with Subjective Cognitive Decline: A Systematic Review

BACKGROUND

Early identification of Alzheimer's disease (AD) may be extremely beneficial for delaying disease progression. Subjective cognitive decline (SCD) may be an early indicator of AD pathology. Not all individuals with SCD will eventually develop AD, making it critical to identify biomarkers during the SCD stage which indicate likely clinical progression.

OBJECTIVE

The present review aims to summarize available data on structural MRI and cerebrospinal fluid (CSF) biomarkers and their association with clinical progression to mild cognitive impairment (MCI) or AD in people with SCD.

METHODS

Database searches were conducted using Embase and PubMed until June 2020. Longitudinal studies assessing biomarkers in individuals with SCD and assessing clinical progression to MCI/AD were included. Two assessors performed data extraction and assessed the risk of bias in the included studies. Data were synthesized narratively.

RESULTS

An initial search identified 1,065 papers; after screening and review 14 studies were included. Sample size of the included studies ranged from 28-674, mean age was 60.0-68.6 years, and 10.2%-52%of participants converted to MCI/AD. Lower levels of CSF Aβ 42 were consistently associated with clinical progression. Combination measures identifying an AD-like profile of Aβ 42 and tau levels were strongly associated with clinical progression. Biomarkers identified with structural MRI were less conclusive, as some studies found significant associations while others did not.

CONCLUSION

Biomarkers may be able to predict clinical progression in those with cognitive complaints. Aβ 42, or combinations of Aβ 42 and tau may be useful biomarkers in identifying individuals with SCD who will progress to MCI/AD.

Cognitively supernormal older adults maintain a unique structural connectome that is resistant to Alzheimer's pathology

Studying older adults with excellent cognitive capacities (Supernormals) provides a unique opportunity for identifying factors related to cognitive success - a critical topic across lifespan. There is a limited understanding of Supernormals' neural substrates, especially whether any of them attends shaping and supporting superior cognitive function or confer resistance to age-related neurodegeneration such as Alzheimer's disease (AD). Here, applying a state-of-the-art diffusion imaging processing pipeline and finite mixture modelling, we longitudinally examine the structural connectome of Supernormals. We find a unique structural connectome, containing the connections between frontal, cingulate, parietal, temporal, and subcortical regions in the same hemisphere that remains stable over time in Supernormals, relatively to typical agers. The connectome significantly classifies positive vs. negative AD pathology at 72% accuracy in a new sample mixing Supernormals, typical agers, and AD risk [amnestic mild cognitive impairment (aMCI)] subjects. Among this connectome, the mean diffusivity of the connection between right isthmus cingulate cortex and right precuneus most robustly contributes to predicting AD pathology across samples. The mean diffusivity of this connection links negatively to global cognition in those Supernormals with positive AD pathology. But this relationship does not exist in typical agers or aMCI. Our data suggest the presence of a structural connectome supporting cognitive success. Cingulate to precuneus white matter integrity may be useful as a structural marker for monitoring neurodegeneration and may provide critical information for understanding how some older adults maintain or excel cognitively in light of significant AD pathology.

Clinicians' communication with patients receiving a MCI diagnosis: The ABIDE project

BACKGROUND

We aimed to explore clinicians' communication, including the discussion of diagnosis, cause, prognosis and care planning, in routine post-diagnostic testing consultations with patients with Mild Cognitive Impairment (MCI).

METHODS

Thematic content analysis was used to analyze audiotaped consultations in which 10 clinicians (eight neurologists and two geriatricians) from 7 memory clinics, disclosed diagnostic information to 13 MCI patients and their care partners. We assessed clinician-patient communication regarding diagnostic label, cause, prognosis and care planning to identify core findings.

RESULTS

Core findings were: clinicians 1) differed in how they informed about the MCI label; 2) tentatively addressed cause of symptoms; 3) (implicitly) steered against further biomarker testing; 4) rarely informed about the patient's risk of developing dementia; 5) often informed about the expected course of symptoms emphasizing potential symptom stabilization and/or improvement, and; 6) did not engage in a conversation on long-term (care) planning.

DISCUSSION

Clinicians' information provision about the underlying cause, prognosis and implications for long-term (care) planning in MCI could be more specific. Since most patients and care partners have a strong need to understand the patient's symptoms, and for information on the prognosis and implications for the future, clinicians' current approach may not match with those needs.

Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy

INTRODUCTION

An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination.

METHODS

We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients.

RESULTS

The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed).

CONCLUSIONS

We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.

The quantitative neuroradiology initiative framework: application to dementia

There are numerous challenges to identifying, developing and implementing quantitative techniques for use in clinical radiology, suggesting the need for a common translational pathway. We developed the quantitative neuroradiology initiative (QNI), as a model framework for the technical and clinical validation necessary to embed automated segmentation and other image quantification software into the clinical neuroradiology workflow. We hypothesize that quantification will support reporters with clinically relevant measures contextualized with normative data, increase the precision of longitudinal comparisons, and generate more consistent reporting across levels of radiologists' experience. The QNI framework comprises the following steps: (1) establishing an area of clinical need and identifying the appropriate proven imaging biomarker(s) for the disease in question; (2) developing a method for automated analysis of these biomarkers, by designing an algorithm and compiling reference data; (3) communicating the results via an intuitive and accessible quantitative report; (4) technically and clinically validating the proposed tool pre-use; (5) integrating the developed analysis pipeline into the clinical reporting workflow; and (6) performing in-use evaluation. We will use current radiology practice in dementia as an example, where radiologists have established visual rating scales to describe the degree and pattern of atrophy they detect. These can be helpful, but are somewhat subjective and coarse classifiers, suffering from floor and ceiling limitations. Meanwhile, several imaging biomarkers relevant to dementia diagnosis and management have been proposed in the literature; some clinically approved radiology software tools exist but in general, these have not undergone rigorous clinical validation in high volume or in tertiary dementia centres. The QNI framework aims to address this need. Quantitative image analysis is developing apace within the research domain. Translating quantitative techniques into the clinical setting presents significant challenges, which must be addressed to meet the increasing demand for accurate, timely and impactful clinical imaging services.

Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study

Background

In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.

Methods

In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy.

Results

After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p < .0001).

Conclusions

Adding the PredictND tool to the clinical evaluation increased clinicians’ confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0482-3) contains supplementary material, which is available to authorized users.