How golden is the gold standard of neuropathology in dementia?

Brain metabolic imaging-based model identifies cognitive stability in prodromal Alzheimer's disease

The recent approval of anti-amyloid pharmaceuticals for the treatment of Alzheimer's disease (AD) has created a pressing need for the ability to accurately identify optimal candidates for anti-amyloid therapy, specifically those with evidence for incipient cognitive decline, since patients with mild cognitive impairment (MCI) may remain stable for several years even with positive AD biomarkers. Using fluorodeoxyglucose PET and biomarker data from 594 ADNI patients, a neural network ensemble was trained to forecast cognition from MCI diagnostic baseline. Training data comprised PET studies of patients with biological AD. The ensemble discriminated between progressive and stable prodromal subjects (MCI with positive amyloid and tau) at baseline with 88.6% area-under-curve, 88.6% (39/44) accuracy, 73.7% (14/19) sensitivity and 100% (25/25) specificity in the test set. It also correctly classified all other test subjects (healthy or AD continuum subjects across the cognitive spectrum) with 86.4% accuracy (206/239), 77.4% sensitivity (33/42) and 88.23% (165/197) specificity. By identifying patients with prodromal AD who will not progress to dementia, our model could significantly reduce overall societal burden and cost if implemented as a screening tool. The model's high positive predictive value in the prodromal test set makes it a practical means for selecting candidates for anti-amyloid therapy and trials.

Parkinsonism in Alzheimer's disease without Lewy bodies in association with nigral neuron loss: A data-driven clinicopathologic study

INTRODUCTION

Parkinsonism in patients with Alzheimer's disease (AD) is often attributed to Lewy-related pathology, given its high comorbidity. In the era of anti-amyloid therapy, recognizing parkinsonism caused by AD pathology is needed to optimize the treatment.

METHODS

This study aimed to quantitatively characterize parkinsonism and nigral neuropathology in AD without Lewy bodies (LB). Nigral neurons were counted automatically. Fine-tuned ChatGPT collected structured clinical data.

RESULTS

Among 635 AD patients without LB, 62 (9.7%) presented parkinsonism, which correlated with reduced nigral neuron density (p < 0.01). Tau burden did not explain the nigral neuronal loss. TAR DNA-binding protein 43 (TDP-43) pathology correlated with reduced nigral pigmented neuron density (p = 0.03).

DISCUSSION

Our findings suggest that parkinsonism in AD without LB is related to nigral neuronal loss in association with TDP-43 pathology. Recognition of parkinsonism in AD without LB is crucial for appropriate therapy.

HIGHLIGHTS

One in 10 Alzheimer's disease (AD) patients without Lewy bodies had parkinsonism. Parkinsonism in AD was correlated with reduced nigral neuron density. TAR DNA-binding protein 43 pathology was associated with nigral degeneration in AD. AD should be included in the differential diagnosis of dementia with parkinsonism.

Current Advancements in Digital Neuropathology and Machine Learning for the Study of Neurodegenerative Diseases

Computational neurodegenerative neuropathology represents a transformative approach in the analysis and understanding of neurodegenerative diseases through utilization of whole slide images (WSIs) and advanced machine learning/artificial intelligence (ML/AI) techniques. This review explores the emerging field of computational neurodegenerative neuropathology, emphasizing its potential to enhance neuropathologic assessment, diagnosis, and research. Recent advancements in ML/AI technologies have significantly affected image-based medical fields, including anatomic pathology, by automating disease staging, identifying novel morphologic biomarkers, and uncovering new clinical insights via multi-modal AI approaches. Despite its promise, the field faces several challenges, including limited expert annotations, slide scanning inaccessibility, inter-institutional variability, and the complexities of sharing large WSI data sets. This review discusses the importance of improving deep learning model accuracy and efficiency for better interpretation of neuropathologic data. It highlights the potential of unsupervised learning to identify patterns in unannotated data. Furthermore, the development of explainable AI models is crucial for experimental neuropathology. By addressing these challenges and leveraging cutting-edge AI techniques, computational neurodegenerative neuropathology has the potential to revolutionize the field and significantly advance our understanding of disease.

Atypical Presentation of Probable Sporadic Creutzfeldt-Jakob Disease: A Patient Without Mental Deterioration

Creutzfeldt-Jakob Disease (CJD) is a prion disease that leads to rapid mental deterioration and is always fatal. Prions are glycoproteins found in the brain. While their function is not completely understood, irregular folding of these proteins leads to prion disorders and neurodegenerative disease. CJD is extremely rare (1-2 cases per million people). A 68-year-old woman presented to the family medicine clinic with symptoms of weakness, paresthesia, and foot drop. Some weeks later she presented at the emergency department with left ankle and foot pain. All symptoms were on the left side of the body. An initial workup with labs was performed which all returned normal. Subsequently, a cerebrospinal fluid (CSF) panel was run and findings included elevated neuron-specific enolase and 14-3-3 gamma indicating a neurodegenerative disease. Further, an indeterminate real-time quaking-induced conversion (RT-QuIC) led to our diagnosis of a probable sporadic CJD. The patient was treated for symptoms and died four months following the initial presentation. Typically CJD presents with similar physical symptoms such as myoclonus. CJD is typically accompanied by severe mental deterioration including depression, memory loss, and dementia. CJD presentation without mental deterioration has only been reported in two other cases. Presenting here is a unique presentation of probable CJD that involved all the physical symptoms, including death, but the mental deterioration was absent. Clinicians should be aware of CJD and that presentation is not always standard.

Impact of motor dysfunction on neuropsychiatric symptom profile in patients with autopsy-confirmed Alzheimer's disease

Motor dysfunction, which includes changes in gait, balance, and/or functional mobility, is a lesser-known feature of Alzheimer's Disease (AD), especially as it relates to the development of neuropsychiatric symptoms (NPS). This study (1) compared rates of NPS between autopsy-confirmed AD patients with and without early-onset motor dysfunction and (2) compared rates of non-AD dementia autopsy pathology (Lewy Body disease, Frontotemporal Lobar degeneration) between these groups. This retrospective longitudinal cohort study utilized National Alzheimer's Coordinating Center (NACC) data. Participants (N = 856) were required to have moderate-to-severe autopsy-confirmed AD, Clinical Dementia Rating-Global scores of ≤1 at their index visit, and NPS and clinician-rated motor data. Early motor dysfunction was associated with significantly higher NPI-Q total scores (T = 4.48, p < .001) and higher odds of delusions (OR [95%CI]: 1.73 [1.02-2.96]), hallucinations (2.45 [1.35-4.56]), depression (1.51 [1.11-2.06]), irritability (1.50 [1.09-2.08]), apathy (1.70 [1.24-2.36]), anxiety (1.38 [1.01-1.90]), nighttime behaviors (1.98 [1.40-2.81]), and appetite/eating problems (1.56 [1.09-2.25]). Early motor dysfunction was also associated with higher Lewy Body disease pathology (1.41 [1.03-1.93]), but not Frontotemporal Lobar degeneration (1.10 [0.71-1.69]), on autopsy. Our results suggest that motor symptoms in early AD are associated with a higher number and severity of NPS, which may be partially explained by comorbid non-AD neuropathology.

Considerations for Use of Blood-Based Biomarkers in Epidemiologic Dementia Research

Dementia represents a growing public health burden with large social, racial, and ethnic disparities. The etiology of dementia is poorly understood, and the lack of robust biomarkers in diverse, population-representative samples is a barrier to moving dementia research forward. Existing biomarkers and other measures of pathology-derived from neuropathology, neuroimaging, and cerebrospinal fluid samples-are commonly collected from predominantly White and highly educated samples drawn from academic medical centers in urban settings. Blood-based biomarkers are noninvasive and less expensive, offering promise to expand our understanding of the pathophysiology of dementia, including in participants from historically excluded groups. Although largely not yet approved by the Food and Drug Administration or used in clinical settings, blood-based biomarkers are increasingly included in epidemiologic studies on dementia. Blood-based biomarkers in epidemiologic research may allow the field to more accurately understand the multifactorial etiology and sequence of events that characterize dementia-related pathophysiological changes. As blood-based dementia biomarkers continue to be developed and incorporated into research and practice, we outline considerations for using them in dementia epidemiology, and illustrate key concepts with Alzheimer's Disease Neuroimaging Initiative (2003-present) data. We focus on measurement, including both validity and reliability, and on the use of dementia blood-based biomarkers to promote equity in dementia research and cognitive aging. This article is part of a Special Collection on Mental Health.

Toward a generalizable machine learning workflow for neurodegenerative disease staging with focus on neurofibrillary tangles

Machine learning (ML) has increasingly been used to assist and expand current practices in neuropathology. However, generating large imaging datasets with quality labels is challenging in fields which demand high levels of expertise. Further complicating matters is the often seen disagreement between experts in neuropathology-related tasks, both at the case level and at a more granular level. Neurofibrillary tangles (NFTs) are a hallmark pathological feature of Alzheimer disease, and are associated with disease progression which warrants further investigation and granular quantification at a scale not currently accessible in routine human assessment. In this work, we first provide a baseline of annotator/rater agreement for the tasks of Braak NFT staging between experts and NFT detection using both experts and novices in neuropathology. We use a whole-slide-image (WSI) cohort of neuropathology cases from Emory University Hospital immunohistochemically stained for Tau. We develop a workflow for gathering annotations of the early stage formation of NFTs (Pre-NFTs) and mature intracellular (iNFTs) and show ML models can be trained to learn annotator nuances for the task of NFT detection in WSIs. We utilize a model-assisted-labeling approach and demonstrate ML models can be used to aid in labeling large datasets efficiently. We also show these models can be used to extract case-level features, which predict Braak NFT stages comparable to expert human raters, and do so at scale. This study provides a generalizable workflow for various pathology and related fields, and also provides a technique for accomplishing a high-level neuropathology task with limited human annotations.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study

BACKGROUND

As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD).

METHODS

The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure.

RESULTS

We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD.

CONCLUSIONS

As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Accurate digital quantification of tau pathology in progressive supranuclear palsy

The development of novel treatments for Progressive Supranuclear Palsy (PSP) is hindered by a knowledge gap of the impact of neurodegenerative neuropathology on brain structure and function. The current standard practice for measuring postmortem tau histology is semi-quantitative assessment, which is prone to inter-rater variability, time-consuming and difficult to scale. We developed and optimized a tau aggregate type-specific quantification pipeline for cortical and subcortical regions, in human brain donors with PSP. We quantified 4 tau objects ('neurofibrillary tangles', 'coiled bodies', 'tufted astrocytes', and 'tau fragments') using a probabilistic random forest machine learning classifier. The tau pipeline achieved high classification performance (F1-score > 0.90), comparable to neuropathologist inter-rater reliability in the held-out test set. Using 240 AT8 slides from 32 postmortem brains, the tau burden was correlated against the PSP pathology staging scheme using Spearman's rank correlation. We assessed whether clinical severity (PSP rating scale, PSPRS) score reflects neuropathological severity inferred from PSP stage and tau burden using Bayesian linear mixed regression. Tufted astrocyte density in cortical regions and coiled body density in subcortical regions showed the highest correlation to PSP stage (r = 0.62 and r = 0.38, respectively). Using traditional manual staging, only PSP patients in stage 6, not earlier stages, had significantly higher clinical severity than stage 2. Cortical tau density and neurofibrillary tangle density in subcortical regions correlated with clinical severity. Overall, our data indicate the potential for highly accurate digital tau aggregate type-specific quantification for neurodegenerative tauopathies; and the importance of studying tau aggregate type-specific burden in different brain regions as opposed to overall tau, to gain insights into the pathogenesis and progression of tauopathies.

Cognitivism ageing: The Alzheimer conundrum as switched ontology & the potential for a new materialist dementia

Following recent regulatory approvals for anti-Alzheimer's monoclonal antibodies, this paper considers the contemporary role of cognitivism in defining the ontological commitments of dementia research, as well as movements away from cognitivism under the umbrella of 4E cognitive science. 4E cognitive theories, extending cognition into bodies, their environs, and active relations between the two, share potentially fruitful affinities with new materialisms which focus on the co-constitution of matter in intra-action. These semi-overlapping conceptual positions furnish some opportunity for an ontological alternative to longstanding cognitivist commitments, particularly to the isolated brain as a material catalyst for commercial interventions. After outlining mainstream cognitivism and its shortcomings, I explore 4E and new materialism as possibly transformative conceptual schemas for dementia research, a field for which cognitivist imaginings of cognitive decline in later life have profound and often regrettable ramifications. To realise this new materialist dementia, I sketch out a cognitive ontology based on Barad's agential realism. This facilitates a reassessment of the biggest conundrum in dementia research - the lack of neat correlation between (apparently material) neuropathology and (apparently immaterial) cognitive impairment - alongside the continued failure of efforts to develop effective interventions. It also gives social researchers working on cognitive decline in later life an opportunity to reappraise the nature of social science as a response to such phenomena. If cognition and cognitive ageing are reimagined as an emergent characteristic of intra-acting matter, then new materialist social science might be at least as conducive to salutogenic interventions as the neuropsychiatric technoscience that dominates the contemporary dementia research economy despite continual failures. I argue that a new materialist cognitive ontology could help us think beyond an ageing cognitivism and, by extension, beyond the Alzheimer conundrum.

Diagnosis of Alzheimer Disease and Tauopathies on Whole-Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm

Neuropathologic assessment during autopsy is the gold standard for diagnosing neurodegenerative disorders. Neurodegenerative conditions, such as Alzheimer disease (AD) neuropathological change, are a continuous process from normal aging rather than categorical; therefore, diagnosing neurodegenerative disorders is a complicated task. We aimed to develop a pipeline for diagnosing AD and other tauopathies, including corticobasal degeneration (CBD), globular glial tauopathy, Pick disease, and progressive supranuclear palsy. We used a weakly supervised deep learning-based approach called clustering-constrained-attention multiple-instance learning (CLAM) on the whole-slide images (WSIs) of patients with AD (n = 30), CBD (n = 20), globular glial tauopathy (n = 10), Pick disease (n = 20), and progressive supranuclear palsy (n = 20), as well as nontauopathy controls (n = 21). Three sections (A: motor cortex; B: cingulate gyrus and superior frontal gyrus; and C: corpus striatum) that had been immunostained for phosphorylated tau were scanned and converted to WSIs. We evaluated 3 models (classic multiple-instance learning, single-attention-branch CLAM, and multiattention-branch CLAM) using 5-fold cross-validation. Attention-based interpretation analysis was performed to identify the morphologic features contributing to the classification. Within highly attended regions, we also augmented gradient-weighted class activation mapping to the model to visualize cellular-level evidence of the model's decisions. The multiattention-branch CLAM model using section B achieved the highest area under the curve (0.970 ± 0.037) and diagnostic accuracy (0.873 ± 0.087). A heatmap showed the highest attention in the gray matter of the superior frontal gyrus in patients with AD and the white matter of the cingulate gyrus in patients with CBD. Gradient-weighted class activation mapping showed the highest attention in characteristic tau lesions for each disease (eg, numerous tau-positive threads in the white matter inclusions for CBD). Our findings support the feasibility of deep learning-based approaches for the classification of neurodegenerative disorders on WSIs. Further investigation of this method, focusing on clinicopathologic correlations, is warranted.

Analysis of Costs for Imaging-Assisted Pharmaceutical Intervention in Alzheimer's Disease with Lecanemab: Snapshot of the First 3 Years

BACKGROUND

The approval of lecanemab for the treatment of Alzheimer's disease (AD) by the Food and Drug Administration in the United States has sparked controversy over issues of safety, cost, and efficacy. Furthermore, the prognostication of cognitive decline is prohibitively difficult with current methods. The inability to forecast incipient dementia in patients with biological AD suggests a prophylactic scenario wherein all patients with cognitive decline are prescribed anti-AD drugs at the earliest manifestations of dementia; however, most patients with mild cognitive impairment (approximately 77.7%) do not develop dementia over a 3-year period. Prophylactic response therefore constitutes unethical, costly, and unnecessary treatment for these patients.

OBJECTIVE

We present a snapshot of the costs associated with the first 3 years of mass availability of anti-AD drugs in a variety of scenarios.

METHODS

We consider multiple prognostication scenarios with varying sensitivities and specificities based on neuroimaging studies in patients with mild cognitive impairment to determine approximate costs for the large-scale use of lecanemab.

RESULTS

The combination of fluorodeoxyglucose and magnetic resonance was determined to be the most cost-efficient at $177,000 for every positive outcome every 3 years under an assumed adjustment in the price of lecanemab to $9,275 per year.

CONCLUSIONS

Imaging-assisted identification of cognitive status in patients with prodromal AD is demonstrated to reduce costs and prevent instances of unnecessary treatment in all cases considered. This highlights the potential of this technology for the ethical prescription of anti-AD medications under a paradigm of imaging-assisted early detection for pharmaceutical intervention in the treatment of AD.

Detection of Alzheimer's disease amyloid beta 1-42, p-tau, and t-tau assays

INTRODUCTION

We aimed to provide cut points for the automated Elecsys Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers.

METHODS

Cut points for Elecsys amyloid beta 42 (Aβ42), total tau (t-tau), hyperphosphorylated tau (p-tau), and t-tau/Aβ42 and p-tau/Aβ42 ratios were evaluated in Mayo Clinic Study of Aging (n = 804) and Mayo Clinic Alzheimer's Disease Research Center (n = 70) participants.

RESULTS

The t-tau/Aβ42 and p-tau/Aβ42 ratios had a higher percent agreement with normal/abnormal amyloid positron emission tomography (PET) than the individual CSF markers. Reciever Operating Characteristic (ROC)-based cut points were 0.26 (0.24-0.27) for t-tau/Aβ42 and 0.023 (0.020-0.025) for p-tau/Aβ42. Ratio cut points derived from other cohorts performed as well in our cohort as our own did. Individual biomarkers had worse diagnostic properties and more variable results in terms of positive and negative percent agreement (PPA and NPA).

CONCLUSION

CSF t-tau/Aβ42 and p-tau/Aβ42 ratios are very robust indicators of AD. For individual biomarkers, the intended use should determine which cut point is chosen.

Evaluation of the Accuracy of Cognitive Screening Tests in Detecting Dementia Associated with Alzheimer's Disease: A Hierarchical Bayesian Latent Class Meta-Analysis

BACKGROUND

Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) are neuropsychological tests commonly used by physicians for screening cognitive dysfunction of Alzheimer's disease (AD). Due to different imperfect reference standards, the performance of MoCA and MMSE do not reach consensus. It is necessary to evaluate the consistence and differentiation of MoCA and MMSE in the absence of a gold standard for AD.

OBJECTIVE

We aimed to assess the accuracy of MoCA and MMSE in screening AD without a gold standard reference test.

METHODS

Studies were identified from PubMed, Web of Science, CNKI, Chinese Wanfang Database, China Science and Technology Journal Database, and Cochrane Library. Our search was limited to studies published in English and Chinese before August 2021. A hierarchical Bayesian latent class model was performed in meta-analysis when the gold standard was absent.

RESULTS

A total of 67 studies comprising 5,554 individuals evaluated for MoCA and 76,862 for MMSE were included in this meta-analysis. The pooled sensitivity was 0.934 (95% CI 0.906 to 0.954) for MoCA and 0.883 (95% CI 0.859 to 0.903) for MMSE, while the pooled specificity was 0.899 (95% CI 0.859 to 0.928) for MoCA and 0.903 (95% CI 0.879 to 0.923) for MMSE. MoCA was useful to rule out dementia associated with AD with lower negative likelihood ratio (LR-) (0.074, 95% CI 0.051 to 0.108). MoCA showed better performance with higher diagnostic odds ratio (DOR) (124.903, 95% CI 67.459 to 231.260).

CONCLUSION

MoCA had better performance than MMSE in screening dementia associated with AD from patients with mild cognitive impairment or healthy controls.

Deep learning-based model for diagnosing Alzheimer's disease and tauopathies

AIMS

This study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau-immunostained digital slide images.

METHODS

We trained the YOLOv3 object detection algorithm to detect five tau lesion types: neuronal inclusions, neuritic plaques, tufted astrocytes, astrocytic plaques and coiled bodies. We used 2522 digital slide images of CP13-immunostained slides of the motor cortex from 10 cases each of AD, PSP and CBD for training. Data augmentation was performed to increase the size of the training dataset. We next constructed random forest classifiers using the quantitative burdens of each tau lesion from motor cortex, caudate nucleus and superior frontal gyrus, ascertained from the object detection model. We split 120 cases (32 AD, 36 PSP, 31 CBD and 21 PiD) into training (90 cases) and test (30 cases) sets to train random forest classifiers.

RESULTS

The resultant random forest classifier achieved an average test score of 0.97, indicating that 29 out of 30 cases were correctly diagnosed. A validation study using hold-out datasets of CP13- and AT8-stained slides from 50 cases (10 AD, 17 PSP, 13 CBD and 10 PiD) showed >92% (without data augmentation) and >95% (with data augmentation) diagnostic accuracy in both CP13- and AT8-stained slides.

CONCLUSION

Our diagnostic model trained with CP13 also works for AT8; therefore, our diagnostic tool can be potentially used by other investigators and may assist medical decision-making in neuropathological diagnoses of tauopathies.

Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)

BACKGROUND

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings.

OBJECTIVES

To determine the accuracy of the Mini Mental State Examination for the early detection of dementia in people with mild cognitive impairment SEARCH METHODS: We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data.

SELECTION CRITERIA

We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve.

MAIN RESULTS

In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis.

AUTHORS' CONCLUSIONS

Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

Deep Learning-Based Image Classification in Differentiating Tufted Astrocytes, Astrocytic Plaques, and Neuritic Plaques

This study aimed to develop a deep learning-based image classification model that can differentiate tufted astrocytes (TA), astrocytic plaques (AP), and neuritic plaques (NP) based on images of tissue sections stained with phospho-tau immunohistochemistry. Phospho-tau-immunostained slides from the motor cortex were scanned at 20× magnification. An automated deep learning platform, Google AutoML, was used to create a model for distinguishing TA in progressive supranuclear palsy (PSP) from AP in corticobasal degeneration (CBD) and NP in Alzheimer disease (AD). A total of 1500 images of representative tau lesions were captured from 35 PSP, 27 CBD, and 33 AD patients. Of those, 1332 images were used for training, and 168 images for cross-validation. We tested the model using 100 additional test images taken from 20 patients of each disease. In cross-validation, precision and recall for each individual lesion type were 100% and 98.0% for TA, 98.5% and 98.5% for AP, and 98.0% and 100% for NP, respectively. In a test set, all images of TA and NP were correctly predicted. Only eleven images of AP were predicted to be TA or NP. Our data indicate the potential usefulness of deep learning-based image classification methods to assist in differential diagnosis of tauopathies.

Impact of the Prevalence of Cognitive Impairment on the Accuracy of the Montreal Cognitive Assessment: The Advantage of Using two MoCA Thresholds to Identify Error-prone Test Scores

OBJECTIVES

The focus of this study is the classification accuracy of the Montreal Cognitive Assessment (MoCA) for the detection of cognitive impairment (CI). Classification accuracy can be low when the prevalence of CI is either high or low in a clinical sample. A more robust result can be expected when avoiding the range of test scores within which most classification errors are expected, with adequate predictive values for more clinical settings.

METHODS

The classification methods have been applied to the MoCA data of 5019 patients in the Uniform Data Set of the University of Washington's National Alzheimer's Coordinating Center, to which 30 Alzheimer Disease Centers (ADCs) contributed.

RESULTS

The ADCs show sample prevalence of CI varying from 0.22 to 0.87. Applying an optimal cutoff score of 23, the MoCA showed for only 3 of 30 ADCs both a positive predictive value (PPV) and a negative predictive value (NPV) ≥0.8, and in 18 cases, a PPV ≥0.8 and for 13 an NPV ≥0.8. Overall, the test scores between 22 and 25 have low odds of true against false decisions of 1.14 and contains 55.3% of all errors when applying the optimal dichotomous cut-point. Excluding the range 22 to 25 offers higher classification accuracies for the samples of the individual ADCs. Sixteen of 30 ADCs showed both NPV and PPV ≥0.8, 25 show a PPV ≥0.8, and 21 show an NPV ≥0.8.

CONCLUSION

In comparison to a dichotomous threshold, considering the most error-prone test scores as uncertain enables a classification that offers adequate classification accuracies in a larger number of clinical settings.

Investigation of risk factors for the conversion of mild cognitive impairment to dementia

BACKGROUND

Mild cognitive impairment (MCI) is an intermediate state between normal aging and dementia and is characterized by pathological cognitive decline. The study aimed at revealing the risk of MCI progressing to dementia through a follow-up investigation.

METHODS

In 2011, 441 MCI subjects were recruited, and the disease status was tracked by the follow-up survey in 2017. Subjects with MCI stable (MCIs; N = 356) and MCI progressed into dementia (MCIp; N = 77) were analysed in our study. Community-dwelling old people of age ≥ 55 were recruited from 30 streets and 24 committees (or communities) of the Pudong New District (Shanghai, China). Neuropsychological tests of MMSE, MoCA, 17-item HAMD-17, ADL and HIS were performed. Additionally, the correlations of neuropsychological items and MCIp were explored by univariate and multivariate regression analyses.

RESULTS

MCIp patients had the lower MMSE and MoCA total scores, whereas the ADL, and HIS total score in MCIp group were higher than in MCIs group. The univariate analysis revealed age, attention (MoCA), visuospatial/executive, number of births, marital status and attention and calculation were significant predictors of MCI progression. In multivariate analysis, age was an independent risk factor of MCI aggravating, while attention (MoCA) was independent protective factor for MCI progression.

CONCLUSIONS

Age and worsening attention but not depression in MCI patients were independently associated with the progression of dementia in a 6-year follow-up period.

Positioning ethnicity in dementia awareness research: does the use of senility risk ascribing racialised knowledge deficits to minority groups?

Over recent decades, the importance of increasing dementia awareness has been promoted by charities, researchers and governments. In response, a large body of research has emerged that evaluates the awareness of different populations. One such population are minority ethnic communities. Associated studies typically conclude that minority ethnic groups have a poor awareness of dementia and that interventions should be developed to better educate them. Operationalisations of awareness almost always reference senility - the traditional notion that dementia is a natural outcome of ageing - a widely held belief among many populations. Senility is considered incorrect knowledge in the research literature, and those participants who identify with it are deemed to have poor awareness. Despite the researchers' claims that senility is false, the scientific evidence is inconclusive, and the concept is contested. As such, a large body of research repeatedly positions minority ethnic communities as inferior and in need of re-education based on researchers' questionable assumptions. This issue is bound up with a racialised deficit-model of science communication and wider critiques of psychiatric colonialism. In response, researchers of dementia and ethnicity should reflect on their own awareness and the ways in which they position others in relation to it.

Validation of machine learning models to detect amyloid pathologies across institutions

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) are the most commonly used method in Alzheimer’s disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer’s disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer’s Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice.

Critical Steps to be Taken into Consideration Before Quantification of β-Amyloid and Tau Isoforms in Blood can be Implemented in a Clinical Environment

This review aims to document difficulties, limitations, and pitfalls when considering protein analysis in blood samples. It proposes an improved workflow for design, development, and validation of (immuno)assays for blood proteins, without providing reflections on a potential hypothesis of the origin of protein mismetabolism and deposition. There is a special focus on assay development for quantification of β-amyloid (Aβ) and tau in blood for diagnostic use or for integration in clinical trials in the field of Alzheimer's disease (AD).

Data-driven approaches for tau-PET imaging biomarkers in Alzheimer's disease

Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions-of-interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post-mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data-driven method to identify spatial patterns of tau-PET distribution, and to compare these patterns to previously published "pathology-driven" ROIs. Tau-PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [¹⁸ F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [¹⁸ F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [¹⁸ F]AV1451 uptake in the data-driven clusters, and in 35 previously published pathology-driven ROIs, was extracted from ADNI [¹⁸ F]AV1451 scans. We performed linear models comparing [¹⁸ F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data-driven ROIs consistently demonstrated the strongest or near-strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data-driven, one pathology-driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [¹⁸ F]AV1451-PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.

Clinical utility of MRI and SPECT in the diagnosis of cognitive impairment referred to memory clinic

ABSTRACTBackground:Despite of their limited availability and potential for significant variation between and within each modality, this is the first study to prospectively measure the clinical utility of MRI and/or SPECT brain scanning in addition to the routine diagnostic workup of patients presenting to memory clinic.

METHODS

A single center study was conducted over a convenience of 12-month sampling period. For each patient referred for MRI and/or SPECT scanning, the primary geriatrician or psychogeriatrician was asked to assign an initial diagnosis. The initial diagnosis was then compared with the final consensus diagnosis after any scans or neuropsychology testing had been completed.

RESULTS

During the 12-month study period, 66 patients (26%) were referred for scans out of a total of 253 patients included in the study. There were 16/44 (36%) positive MRI outcomes and 13/35 (37%) positive SPECT outcomes. The diagnosis changed consistent with the MRI scan findings in 11/44 (25%) and changed consistent with the SPECT scan findings in 9/35 (26%). Potentially reversible pathology was identified in a single patient, 1/50 (2%), via an MRI scan that suggested normal pressure hydrocephalus. The number needed to test for one positive outcome was 3.8 (95% CI 2.0-23.3), 6.0 (95% CI NA), and 1.7 (95% CI 1.3-2.5) for MRI only, SPECT only, and MRI and SPECT together, respectively.

CONCLUSIONS

The clinical utility of MRI and/or SPECT scanning in this study may be broadly superior to the available international evidence, and further research is needed to identify predictors of positive scan outcomes.

Data-Driven Differential Diagnosis of Dementia Using Multiclass Disease State Index Classifier

Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.

Identifying dementia cases with routinely collected health data: A systematic review

Introduction

Prospective, population-based studies can be rich resources for dementia research. Follow-up in many such studies is through linkage to routinely collected, coded health-care data sets. We evaluated the accuracy of these data sets for dementia case identification.

Methods

We systematically reviewed the literature for studies comparing dementia coding in routinely collected data sets to any expert-led reference standard. We recorded study characteristics and two accuracy measures—positive predictive value (PPV) and sensitivity.

Results

We identified 27 eligible studies with 25 estimating PPV and eight estimating sensitivity. Study settings and methods varied widely. For all-cause dementia, PPVs ranged from 33%–100%, but 16/27 were >75%. Sensitivities ranged from 21% to 86%. PPVs for Alzheimer's disease (range 57%–100%) were generally higher than those for vascular dementia (range 19%–91%).

Discussion

Linkage to routine health-care data can achieve a high PPV and reasonable sensitivity in certain settings. Given the heterogeneity in accuracy estimates, cohorts should ideally conduct their own setting-specific validation.

Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference-test

Estimating biomarker-index accuracy when only imperfect reference-test information is available is usually performed under the assumption of conditional independence between the biomarker and imperfect reference-test values. We propose to define a latent normally-distributed tolerance-variable underlying the observed dichotomous imperfect reference-test results. Subsequently, we construct a Bayesian latent-class model based on the joint multivariate normal distribution of the latent tolerance and biomarker values, conditional on latent true disease status, which allows accounting for conditional dependence. The accuracy of the continuous biomarker-index is quantified by the AUC of the optimal linear biomarker-combination. Model performance is evaluated by using a simulation study and two sets of data of Alzheimer's disease patients (one from the memory-clinic-based Amsterdam Dementia Cohort and one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database). Simulation results indicate adequate model performance and bias in estimates of the diagnostic-accuracy measures when the assumption of conditional independence is used when, in fact, it is incorrect. In the considered case studies, conditional dependence between some of the biomarkers and the imperfect reference-test is detected. However, making the conditional independence assumption does not lead to any marked differences in the estimates of diagnostic accuracy.

Neuroimaging biomarkers in Alzheimer's disease and other dementias

In vivo imaging of β-amyloid (Aβ) has transformed the assessment of Aβ pathology and its changes over time, extending our insight into Aβ deposition in the brain by providing highly accurate, reliable, and reproducible quantitative statements of regional or global Aβ burden in the brain. This knowledge is essential for therapeutic trial recruitment and for the evaluation of anti-Aβ treatments. Although cross sectional evaluation of Aβ burden does not strongly correlate with cognitive impairment, it does correlate with cognitive (especially memory) decline and with a higher risk for conversion to AD in the aging population and MCI subjects. This suggests that Aβ deposition is a protracted pathological process starting well before the onset of symptoms. Longitudinal observations, coupled with different disease-specific biomarkers to assess potential downstream effects of Aβ are required to confirm this hypothesis and further elucidate the role of Aβ deposition in the course of Alzheimer's disease.

Alzheimer's Disease Normative Cerebrospinal Fluid Biomarkers Validated in PET Amyloid-β Characterized Subjects from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study

BACKGROUND

The cerebrospinal fluid (CSF) amyloid-β (Aβ)(1-42), total-tau (T-tau), and phosphorylated-tau (P-tau181P) profile has been established as a valuable biomarker for Alzheimer's disease (AD).

OBJECTIVE

The current study aimed to determine CSF biomarker cut-points using positron emission tomography (PET) Aβ imaging screened subjects from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, as well as correlate CSF analyte cut-points across a range of PET Aβ amyloid ligands.

METHODS

Aβ pathology was determined by PET imaging, utilizing ¹¹C-Pittsburgh Compound B, ¹⁸F-flutemetamol, or ¹⁸F-florbetapir, in 157 AIBL participants who also underwent CSF collection. Using an INNOTEST assay, cut-points were established (Aβ(1-42) >544 ng/L, T-tau <407 ng/L, and P-tau181P <78 ng/L) employing a rank based method to define a "positive" CSF in the sub-cohort of amyloid-PET negative healthy participants (n = 97), and compared with the presence of PET demonstrated AD pathology.

RESULTS

CSF Aβ(1-42) was the strongest individual biomarker, detecting cognitively impaired PET positive mild cognitive impairment (MCI)/AD with 85% sensitivity and 91% specificity. The ratio of P-tau181P or T-tau to Aβ(1-42) provided greater accuracy, predicting MCI/AD with Aβ pathology with ≥92% sensitivity and specificity. Cross-validated accuracy, using all three biomarkers or the ratio of P-tau or T-tau to Aβ(1-42) to predict MCI/AD, reached ≥92% sensitivity and specificity.

CONCLUSIONS

CSF Aβ(1-42) levels and analyte combination ratios demonstrated very high correlation with PET Aβ imaging. Our study offers additional support for CSF biomarkers in the early and accurate detection of AD pathology, including enrichment of patient cohorts for treatment trials even at the pre-symptomatic stage.

Development of a diagnostic test based on multiple continuous biomarkers with an imperfect reference test

Ignoring the fact that the reference test used to establish the discriminative properties of a combination of diagnostic biomarkers is imperfect can lead to a biased estimate of the diagnostic accuracy of the combination. In this paper, we propose a Bayesian latent-class mixture model to select a combination of biomarkers that maximizes the area under the ROC curve (AUC), while taking into account the imperfect nature of the reference test. In particular, a method for specification of the prior for the mixture component parameters is developed that allows controlling the amount of prior information provided for the AUC. The properties of the model are evaluated by using a simulation study and an application to real data from Alzheimer's disease research. In the simulation study, 100 data sets are simulated for sample sizes ranging from 100 to 600 observations, with a varying correlation between biomarkers. The inclusion of an informative as well as a flat prior for the diagnostic accuracy of the reference test is investigated. In the real-data application, the proposed model was compared with the generally used logistic-regression model that ignores the imperfectness of the reference test. Conditional on the selected sample size and prior distributions, the simulation study results indicate satisfactory performance of the model-based estimates. In particular, the obtained average estimates for all parameters are close to the true values. For the real-data application, AUC estimates for the proposed model are substantially higher than those from the 'traditional' logistic-regression model.

Mini-Mental State Examination (MMSE) for the detection of dementia in clinically unevaluated people aged 65 and over in community and primary care populations

BACKGROUND

The Mini Mental State Examination (MMSE) is a cognitive test that is commonly used as part of the evaluation for possible dementia.

OBJECTIVES

To determine the diagnostic accuracy of the Mini-Mental State Examination (MMSE) at various cut points for dementia in people aged 65 years and over in community and primary care settings who had not undergone prior testing for dementia.

SEARCH METHODS

We searched the specialised register of the Cochrane Dementia and Cognitive Improvement Group, MEDLINE (OvidSP), EMBASE (OvidSP), PsycINFO (OvidSP), LILACS (BIREME), ALOIS, BIOSIS previews (Thomson Reuters Web of Science), and Web of Science Core Collection, including the Science Citation Index and the Conference Proceedings Citation Index (Thomson Reuters Web of Science). We also searched specialised sources of diagnostic test accuracy studies and reviews: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). We attempted to locate possibly relevant but unpublished data by contacting researchers in this field. We first performed the searches in November 2012 and then fully updated them in May 2014. We did not apply any language or date restrictions to the electronic searches, and we did not use any methodological filters as a method to restrict the search overall.

SELECTION CRITERIA

We included studies that compared the 11-item (maximum score 30) MMSE test (at any cut point) in people who had not undergone prior testing versus a commonly accepted clinical reference standard for all-cause dementia and subtypes (Alzheimer disease dementia, Lewy body dementia, vascular dementia, frontotemporal dementia). Clinical diagnosis included all-cause (unspecified) dementia, as defined by any version of the Diagnostic and Statistical Manual of Mental Disorders (DSM); International Classification of Diseases (ICD) and the Clinical Dementia Rating.

DATA COLLECTION AND ANALYSIS

At least three authors screened all citations.Two authors handled data extraction and quality assessment. We performed meta-analysis using the hierarchical summary receiver-operator curves (HSROC) method and the bivariate method.

MAIN RESULTS

We retrieved 24,310 citations after removal of duplicates. We reviewed the full text of 317 full-text articles and finally included 70 records, referring to 48 studies, in our synthesis. We were able to perform meta-analysis on 28 studies in the community setting (44 articles) and on 6 studies in primary care (8 articles), but we could not extract usable 2 x 2 data for the remaining 14 community studies, which we did not include in the meta-analysis. All of the studies in the community were in asymptomatic people, whereas two of the six studies in primary care were conducted in people who had symptoms of possible dementia. We judged two studies to be at high risk of bias in the patient selection domain, three studies to be at high risk of bias in the index test domain and nine studies to be at high risk of bias regarding flow and timing. We assessed most studies as being applicable to the review question though we had concerns about selection of participants in six studies and target condition in one study.The accuracy of the MMSE for diagnosing dementia was reported at 18 cut points in the community (MMSE score 10, 14-30 inclusive) and 10 cut points in primary care (MMSE score 17-26 inclusive). The total number of participants in studies included in the meta-analyses ranged from 37 to 2727, median 314 (interquartile range (IQR) 160 to 647). In the community, the pooled accuracy at a cut point of 24 (15 studies) was sensitivity 0.85 (95% confidence interval (CI) 0.74 to 0.92), specificity 0.90 (95% CI 0.82 to 0.95); at a cut point of 25 (10 studies), sensitivity 0.87 (95% CI 0.78 to 0.93), specificity 0.82 (95% CI 0.65 to 0.92); and in seven studies that adjusted accuracy estimates for level of education, sensitivity 0.97 (95% CI 0.83 to 1.00), specificity 0.70 (95% CI 0.50 to 0.85). There was insufficient data to evaluate the accuracy of the MMSE for diagnosing dementia subtypes.We could not estimate summary diagnostic accuracy in primary care due to insufficient data.

AUTHORS' CONCLUSIONS

The MMSE contributes to a diagnosis of dementia in low prevalence settings, but should not be used in isolation to confirm or exclude disease. We recommend that future work evaluates the diagnostic accuracy of tests in the context of the diagnostic pathway experienced by the patient and that investigators report how undergoing the MMSE changes patient-relevant outcomes.

Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings.

OBJECTIVES

To determine the diagnostic accuracy of the MMSE at various thresholds for detecting individuals with baseline MCI who would clinically convert to dementia in general, Alzheimer's disease dementia or other forms of dementia at follow-up.

SEARCH METHODS

We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data.

SELECTION CRITERIA

We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve.

MAIN RESULTS

In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis.

AUTHORS' CONCLUSIONS

Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

How does additional diagnostic testing influence the initial diagnosis in patients with cognitive complaints in a memory clinic setting?

BACKGROUND

patients suspected of dementia frequently undergo additional diagnostic testing (e.g. brain imaging or neuropsychological assessment) after standard clinical assessment at a memory clinic. This study investigates the use of additional testing in an academic outpatient memory clinic and how it influences the initial diagnosis.

METHODS

the initial diagnosis after standard clinical assessment (history, laboratory tests, cognitive screening and physical and neurological examination) and the final diagnosis after additional testing of 752 memory clinic patients were collected. We specifically registered if, and what type of, additional testing was requested.

RESULTS

additional testing was performed in 518 patients (69%), 67% of whom underwent magnetic resonance imaging, 45% had neuropsychological assessment, 14% had cerebrospinal fluid analysis and 49% had (combinations of) other tests. This led to a modification of the initial diagnosis in 17% of the patients. The frequency of change was highest in patients with an initial non-Alzheimer's disease (AD) dementia diagnosis (54%, compared with 11 and 14% in patients with AD and 'no dementia'; P < 0.01). Finally, after additional testing 44% was diagnosed with AD, 9% with non-AD dementia and 47% with 'no dementia'.

CONCLUSION

additional testing should especially be considered in non-AD patients. In the large group of patients with an initial AD or 'no dementia' diagnosis, additional tests have little diagnostic impact and may perhaps be used with more restraint.

Optimizing the use of expert panel reference diagnoses in diagnostic studies of multidimensional syndromes

Background

In the absence of a gold standard, a panel of experts can be invited to assign a reference diagnosis for use in research. Available literature offers limited guidance on assembling and working with an expert panel for this purpose. We aimed to develop a protocol for an expert panel consensus diagnosis and evaluated its applicability in a pilot project.

Methods

An adjusted Delphi method was used, which started with the assessment of clinical vignettes by 3 experts individually, followed by a consensus discussion meeting to solve diagnostic discrepancies. A panel facilitator ensured that all experts were able to express their views, and encouraged the use of argumentation to arrive at a specific diagnosis, until consensus was reached by all experts. Eleven vignettes of patients suspected of having a primary neurodegenerative disease were presented to the experts. Clinical information was provided stepwise and included medical history, neurological, physical and cognitive function, brain MRI scan, and follow-up assessments over 2 years. After the consensus discussion meeting, the procedure was evaluated by the experts.

Results

The average degree of consensus for the reference diagnosis increased from 52% after individual assessment of the vignettes to 94% after the consensus discussion meeting. Average confidence in the diagnosis after individual assessment was 85%. This did not increase after the consensus discussion meeting. The process evaluation led to several recommendations for improvement of the protocol.

Conclusion

A protocol for attaining a reference diagnosis based on expert panel consensus was shown feasible in research practice.

Electronic supplementary material

The online version of this article (doi:10.1186/s12883-014-0190-3) contains supplementary material, which is available to authorized users.

Imago Mundi, Imago AD, Imago ADNI

Since the launch in 2003 of the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the USA, ever growing, similarly oriented consortia have been organized and assembled around the world. The various accomplishments of ADNI have contributed substantially to a better understanding of the underlying physiopathology of aging and Alzheimer's disease (AD). These accomplishments are basically predicated in the trinity of multimodality, standardization and sharing. This multimodality approach can now better identify those subjects with AD-specific traits that are more likely to present cognitive decline in the near future and that might represent the best candidates for smaller but more efficient therapeutic trials - trials that, through gained and shared knowledge, can be more focused on a specific target or a specific stage of the disease process. In summary, data generated from ADNI have helped elucidate some of the pathophysiological mechanisms underpinning aging and AD pathology, while contributing to the international effort in setting the groundwork for biomarker discovery and establishing standards for early diagnosis of AD.

Apolipoprotein E gene, environmental risk factors, and their interactions in dementia among seniors

OBJECTIVES

Little research has been conducted to explore the joint effect of apolipoprotein E (ApoE) genotypes and environmental risk factors on dementia. In this study, we examined the roles of ApoE alleles and genotypes in dementia and cognitively impaired not demented (CIND), assessed the risk of co-existing or prior health conditions (i.e. depression), family history of diseases, and lifestyle factors on dementia, and explored the interactions between genetic and environmental risk factors and their joint effects on dementia and cognitive impairment.

METHODS

This is a genetic association study. A total of 1185 seniors (391 dementia, 389 CIND, and 405 cognitively intact, matched for age and gender) were selected from the Canadian Study of Health and Aging clinical assessment datasets. Multivariate logistic regression was used to explore the association between ApoE, environment risk factors, and outcomes.

RESULTS

Participants with ApoE ε4 alleles or ε3/ε4 genotypes were at risk of dementia. More education reduced the risk of dementia or CIND. Previous health conditions (e.g. stroke) increased the risk of dementia or CIND. Regular exercise decreased the risk of CIND. ApoE ε3/ε4 genotype and baseline depression had a 7.97-fold greater risk of incident dementia after adjusting for other significant risk factors. No interactions were found in any dementia and CIND models.

CONCLUSIONS

More attention should be paid to assess and treat depressed older people, especially for those with ApoE ε3/ε4 genotypes. Further replication studies in different populations are warranted.

Mortality and morbidity hazards associated with cognitive status in seniors: a Canadian population prospective cohort study

INTRODUCTION

Although cognitive impairment is widely accepted as a leading indicator of dementia, influences of cognitive status on incident dementia and mortality remain unclear. The present study investigated the morbidity hazard associated with cognitive impairment and the mortality hazard associated with dementia in comparison to cognitively intact seniors.

METHODS

A population-based sample of 2914 seniors with clinically diagnosed cognitive status at Wave I (1991-1992) of the Canadian Study of Health and Aging (CSHA) were followed-up 5 years later (1996-1997). At Wave I, there were 921 cognitively intact, 861 cognitively impaired but not demented (CIND), and 1132 seniors with dementia, respectively. The primary outcome measures 5 years later were being cognitively intact, CIND, dementia and death. Kaplan-Meier estimates, log-rank tests, and Cox's proportional models were used in the analyses.

RESULTS

Respondents with CIND at Wave I were 2.191 times (95%CI 1.706-2.814) more likely to have dementia 5 years later than cognitively intact seniors. After adjusting for confounding socio-demographic and health status factors, the odds ratio was reduced to 2.147 times (95%CI 1.662-2.774), but remained significant. Respondents with CIND had a mortality rate 1.869 times (95%CI 1.602-2.179) and seniors with dementia 3.362 times greater (95%CI 2.929-3.860) than that of seniors who were cognitively intact. After controlling the confounders, the odds remained significant at 1.576 (95%CI 1.348-1.843) for CIND respondents and 2.415 (95%CI 2.083-2.800) for seniors with dementia.

DISCUSSION

CIND increases both the risk of dementia and mortality. Early intervention with CIND is warranted to reduce both dementia incidence and mortality.

Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment

Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters.