Diagnosis in vascular dementia, applying ‘Cochrane diagnosis rules’ to ‘dementia diagnostic tools’

In this issue of Clinical Science, Biesbroek and colleagues describe recent work on magnetic resonance imaging (MRI)-based cerebral lesion location and its association with cognitive decline. The authors conclude that diagnostic neuroimaging in dementia should shift from whole-brain evaluation to focused quantitative analysis of strategic brain areas. This commentary uses the review of lesion location mapping to discuss broader issues around studies of dementia test strategies. We draw upon work completed by the Cochrane Dementia and Cognitive Improvement Group designed to improve design, conduct and reporting of dementia biomarker studies

Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.

IntroductionWe characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsWe apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.ResultsWe find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.DiscussionThe latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms

Early diagnosis of Alzheimer's disease: update on combining genetic and brain-imaging measures.

Diagnosis of Alzheimer's disease is often missed or delayed in clinical practice; thus, methods to improve early detection would provide opportunities for early intervention, symptomatic treatment, and improved patient function. Emerging data suggest that the disease process begins years before clinical diagnostic confirmation. This paper reviews current research focusing on methods for more specific and sensitive early detection using measures of genetic risk for Alzheimer's disease and functional brain imaging. This approach aims to identify patients in a presymptomatic stage for early treatment to delay progressive cognitive decline and disease onset

Dementia assessment and management in primary care settings: a survey of current provider practices in the United States.

BACKGROUND:Primary care providers (PCPs) are typically the first to screen and evaluate patients for neurocognitive disorders (NCDs), including mild cognitive impairment and dementia. However, data on PCP attitudes and evaluation and management practices are sparse. Our objective was to quantify perspectives and behaviors of PCPs and neurologists with respect to NCD evaluation and management. METHODS:A cross-sectional survey with 150 PCPs and 50 neurologists in the United States who evaluated more than 10 patients over age 55 per month. The 51-item survey assessed clinical practice characteristics, and confidence, perceived barriers, and typical practices when diagnosing and managing patients with NCDs. RESULTS:PCPs and neurologists reported similar confidence and approaches to general medical care and laboratory testing. Though over half of PCPs performed cognitive screening or referred patients for cognitive testing in over 50% of their patients, only 20% reported high confidence in interpreting results of cognitive tests. PCPs were more likely to order CT scans than MRIs, and only 14% of PCPs reported high confidence interpreting brain imaging findings, compared to 70% of specialists. Only 21% of PCPs were highly confident that they correctly recognized when a patient had an NCD, and only 13% were highly confident in making a specific NCD diagnosis (compared to 72 and 44% for neurologists, both p < 0.001). A quarter of all providers identified lack of familiarity with diagnostic criteria for NCD syndromes as a barrier to clinical practice. CONCLUSIONS:This study demonstrates how PCPs approach diagnosis and management of patients with NCDs, and identified areas for improvement in regards to cognitive testing and neuroimaging. This study also identified all providers' lack of familiarity with published diagnostic criteria for NCD syndromes. These findings may inform the development of new policies and interventions to help providers improve the efficacy of their decision processes and deliver better quality care to patients with NCDs

An evidence-based algorithm for the utility of FDG-PET for diagnosing Alzheimer's disease according to presence of medial temporal lobe atrophy

BackgroundImaging biomarkers for Alzheimer's disease include medial temporal lobe atrophy (MTLA) depicted on computed tomography (CT) or magnetic resonance imaging (MRI) and patterns of reduced metabolism on fluorodeoxyglucose positron emission tomography (FDG-PET).AimsTo investigate whether MTLA on head CT predicts the diagnostic usefulness of an additional FDG-PET scan.MethodParticipants had a clinical diagnosis of Alzheimer's disease (n = 37) or dementia with Lewy bodies (DLB; n = 30) or were similarly aged controls (n = 30). We visually rated MTLA on coronally reconstructed CT scans and, separately and blind to CT ratings, abnormal appearances on FDG-PET scans.ResultsUsing a pre-defined cut-off of MTLA ≥5 on the Scheltens (0-8) scale, 0/30 controls, 6/30 DLB and 23/30 Alzheimer's disease had marked MTLA. FDG-PET performed well for diagnosing Alzheimer's disease v. DLB in the low-MTLA group (sensitivity/specificity of 71%/79%), but in the high-MTLA group diagnostic performance of FDG-PET was not better than chance.ConclusionsIn the presence of a high degree of MTLA, the most likely diagnosis is Alzheimer's disease, and an FDG-PET scan will probably not provide significant diagnostic information. However, in cases without MTLA, if the diagnosis is unclear, an FDG-PET scan may provide additional clinically useful diagnostic information

Non-verbal episodic memory deficits in primary progressive aphasias are highly predictive of underlying amyloid pathology

Diagnostic distinction of primary progressive aphasias (PPA) remains challenging, in particular for the logopenic (lvPPA) and nonfluent/agrammatic (naPPA) variants. Recent findings highlight that episodic memory deficits appear to discriminate these PPA variants from each other, as only lvPPA perform poorly on these tasks while having underlying amyloid pathology similar to that seen in amnestic dementias like Alzheimer’s disease (AD). Most memory tests are, however, language based and thus potentially confounded by the prevalent language deficits in PPA. The current study investigated this issue across PPA variants by contrasting verbal and non-verbal episodic memory measures while controlling for their performance on a language subtest of a general cognitive screen. A total of 203 participants were included (25 lvPPA; 29 naPPA; 59 AD; 90 controls) and underwent extensive verbal and non-verbal episodic memory testing, with a subset of patients (n = 45) with confirmed amyloid profiles as assessed by Pittsburgh Compound B and PET. The most powerful discriminator between naPPA and lvPPA patients was a non-verbal recall measure (Rey Complex Figure delayed recall), with 81% of PPA patients classified correctly at presentation. Importantly, AD and lvPPA patients performed comparably on this measure, further highlighting the importance of underlying amyloid pathology in episodic memory profiles. The findings demonstrate that non-verbal recall emerges as the best discriminator of lvPPA and naPPA when controlling for language deficits in high load amyloid PPA cases

Early and Differential Diagnosis of Dementia and Mild Cognitive Impairment Design and Cohort Baseline Characteristics of the German Dementia Competence Network

Background: The German Dementia Competence Network (DCN) has established procedures for standardized multicenter acquisition of clinical, biological and imaging data, for centralized data management, and for the evaluation of new treatments. Methods: A longitudinal cohort study was set up for patients with mild cognitive impairment (MCI), patients with mild dementia and control subjects. The aims were to establish the diagnostic, differential diagnostic and prognostic power of a range of clinical, laboratory and imaging methods. Furthermore, 2 clinical trials were conducted with patients suffering from MCI and mild to moderate Alzheimer's Disease (AD). These trials aimed at evaluating the efficacy and safety of the combination of galantamine and memantine versus galantamine alone. Results: Here, we report on the scope and projects of the DCN, the methods that were employed, the composition and flow within the diverse groups of patients and control persons and on the clinical and neuropsychological baseline characteristics of the group of 2,113 subjects who participated in the observational and clinical trials. Conclusion: These data have an impact on the procedures for the early and differential clinical diagnosis of dementias, the current standard treatment of AD as well as on future clinical trials in AD. Copyright (C) 2009 S. Karger AG, Base

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org