Cost-Utility of Using Alzheimer's Disease Biomarkers in Cerebrospinal Fluid to Predict Progression from Mild Cognitive Impairment to Dementia

Background: Diagnostic research criteria for Alzheimer's disease support the use of biomarkers in the cerebrospinal fluid (CSF) to improve the accuracy of the prognosis regarding progression to dementia for people with mild cognitive impairment (MCI). Objective: The aim of this study was to estimate the potential incremental cost-effectiveness ratio of adding CSF biomarker testing to the standard diagnostic workup to determine the prognosis for patients with MCI. Methods: In an early technology assessment, a mathematical simulation model was built, using available evidence on added prognostic value as well as expert opinion to estimate the incremental costs and quality-adjusted life years (QALYs) of 20,000 virtual MCI patients with (intervention strategy) and without (control strategy) relying on CSF, from a health-care sector perspective and with a 5-year time horizon. Results: Adding the CSF test improved the accuracy of prognosis by 11%. This resulted in an average QALY gain of 0.046 and € 432 additional costs per patient, representing an incremental cost-effectiveness ratio of € 9,416. Conclusion: The results show the potential of CSF biomarkers in current practice from a health-economics perspective. This result was, however, marked by a high degree of uncertainty, and empirical research is required into the impact of a prognosis on worrying, false-positive/negative prognosis, and stigmatization

Stable cerebrospinal fluid neurogranin and β-site amyloid precursor protein cleaving enzyme 1 levels differentiate predementia Alzheimer's disease patients

Cerebrospinal fluid (CSF) β-site amyloid precursor protein cleaving enzyme 1 (BACE1), neurogranin and the neurogranin/BACE1 ratio are proposed markers for Alzheimer’s disease. BACE1 is also a drug target. However, CSF levels may differ between early-stage amyloid plaque formation (A) and later stage downstream tau-tangle pathology (T) and neurodegeneration (N) and may be expressed as an A/T/N stage (e.g. A+/T−/N or A+/T+/N+). Whether BACE1 and neurogranin levels are persistent traits or change with disease progression is unknown. The aim of this study was to investigate whether CSF neurogranin and BACE1 concentrations differ between A/T/N stages, whether these change over time and correlate with memory decline. This may have implications for patient selection in future trials. We used CSF markers to determine A/T/N stage using amyloid beta42/40 ratio, p-tau181 and total-tau respectively in predementia Alzheimer’s disease cases (n = 176) [including cases that progressed to dementia (n = 10)] and controls (n = 74) from the Norwegian Dementia Disease Initiation cohort. We selected cases at the presumed early (A+/T−/N−, n = 86) and late stages (A+/T+/N+, n = 90) of the Alzheimer’s disease continuum and controlled with normal markers (A−/T−/N−, n = 74). A subset of subjects in all A/T/N groups underwent repeat CSF sampling at approximately 2-year intervals up to 6 years from baseline. Using linear mixed models, longitudinal measurements of CSF BACE1 and neurogranin levels in A+/T−/N− and A+/T+/N+ as compared to A−/T−/N− healthy controls were performed. Next, we measured changes in CSF BACE1 and neurogranin levels in cases that progressed from A−/T−/N− to A+/T−/N− (n = 12), from A+/T−/N− to A+/T or N+ (n = 12), remained stable A+/T−/N− (n = 26), remained stable A+/T+/N+ (n = 28) compared with controls remaining stable A−/T−/N− (n = 33). Lastly, associations between these markers and memory decline were assessed. Compared with A−/T−/N− healthy controls, neurogranin was unaltered in A+/T−/N− (n.s.) but higher in A+/T+/N+ (P < 0.0001). In contrast, BACE1 was lower in A+/T−/N− (P < 0.05) and higher in A+/T+/N+ (P < 0.0001). The neurogranin/BACE1 ratio was increased in both A+/T−/N− (P < 0.05) and A+/T+/N+ (P < 0.0001) groups as compared to A-/T-/N- healthy controls and was more strongly associated with memory decline (b = −0.29, P = 0.0006) than neurogranin (b = −0.20, P = 0.002) and BACE1 (b = −0.13, P = 0.046). Neurogranin and BACE1 level differences remained stable over time not only within A/T/N groups but also in patients progressing to more pathological A/T/N stages (e.g. progressing from A+/T−/N− to A + T or N+) and in cases progressing to dementia. Our results suggest that neurogranin and BACE1 levels may differentiate pathomechanistic Alzheimer’s disease subgroups, putatively with different options for treatment

N-terminal and mid-region tau fragments as fluid biomarkers in neurological diseases

Brain-derived tau secreted into CSF and blood consists of different N-terminal and mid-domain fragments, which may have a differential temporal course and thus, biomarker potential across the Alzheimer’s disease continuum or in other neurological diseases. While current clinically validated total tau assays target mid-domain epitopes, comparison of these assays with new biomarkers targeting N-terminal epitopes using the same analytical platform may be important to increase the understanding of tau pathophysiology. We developed three total tau immunoassays targeting specific N-terminal (NTA and NTB total tau) or mid-region (MR total tau) epitopes, using single molecule array technology. After analytical validation, the diagnostic performance of these biomarkers was evaluated in CSF and compared with the Innotest total tau (and as proof of concept, with N-p-tau181 and N-p-tau217) in three clinical cohorts (n = 342 total). The cohorts included participants across the Alzheimer’s disease continuum (n = 276), other dementias (n = 22), Creutzfeldt–Jakob disease (n = 24), acute neurological disorders (n = 18) and progressive supranuclear palsy (n = 22). Furthermore, we evaluated all three new total tau biomarkers in plasma (n = 44) and replicated promising findings with NTA total tau in another clinical cohort (n = 50). In CSF, all total tau biomarkers were increased in Alzheimer’s disease compared with controls (P </p

The reliability of a deep learning model in external memory clinic MRI data: A multi‐cohort study

AbstractBackgroundDeep learning (DL) has provided impressive results in numerous domains in recent years, including medical image analysis. Training DL models requires large data sets to yield good performance. Since medical data can be difficult to acquire, most studies rely on public research cohorts, which often have harmonized scanning protocols and strict exclusion criteria. This is not representative of a clinical setting. In this study, we investigated the performance of a DL model in out‐of‐distribution data from multiple memory clinics and research cohorts.MethodWe trained multiple versions of AVRA: a DL model trained to predict visual ratings of Scheltens' medial temporal atrophy (MTA) scale (Mårtensson et al., 2019). This was done on different combinations of training data—starting with only harmonized MRI data from public research cohorts, and further increasing image heterogeneity in the training set by including external memory clinic data. We assessed the performance in multiple test sets by comparing AVRA's MTA ratings to an experienced radiologist's (who rated all images in this study). Data came from Alzheimer's Disease Neuroimaging Initiative (ADNI), AddNeuroMed, and images from 13 European memory clinics in the E‐DLB consortium.ResultsModels trained only on research cohorts generalized well to new data acquired with similar protocols as the training data (weighted kappa κw between 0.70‐0.72), but worse to memory clinic data with more image variability (κw between 0.34‐0.66). This was most prominent in one specific memory clinic, where the DL model systematically predicted too low MTA scores. When including data from a wider range of scanners and protocols during training, the agreement to the radiologist's ratings in external memory clinics increased (κw between 0.51‐0.71).ConclusionIn this study we showed that increasing heterogeneity in training data improves generalization to out‐of‐distribution data. Our findings suggest that studies assessing reliability of a DL model should be done in multiple cohorts, and that softwares based on DL need to be rigorously evaluated prior to being certified for deployment to clinics. References: Mårtensson, G. et al. (2019) 'AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks', NeuroImage: Clinical. Elsevier, 23(March), p. 101872

Pre‐screening models for patient engagement: The MOPEAD project

AbstractBackgroundAlzheimer's disease (AD) is a devastating condition that not only impacts greatly on the patient's health but also poses an important burden on the patient's immediate family circle. Early detection of AD allows patients to have an active role in managing their condition, and to plan how to minimize the strain on their dear ones. Despite known benefits, a large proportion of dementia cases remains undiagnosed or receives a late stage diagnosis. The MOPEAD project aims to address this issue by exploring innovative strategies to emerge "hidden" cases of cognitive impairment.MethodMemory clinics located in five different European countries participated in the project. Four innovative pre‐screening strategies were implemented to detect cognitive decline among individuals aged 65‐85 years who had never received a dementia related diagnosis: a) a web‐based pre‐screening tool along with an online marketing campaign, b) open house initiatives where people with memory complaints were invited to receive a quick evaluation at participating memory clinics, c) a primary care‐based protocol for early detection of cognitive decline using easily administered tools, and d) a tertiary care‐based pre‐screening at diabetologist clinics specifically designed to assess risk of dementia among patients with diabetes. A positive pre‐screening result implied that individuals were at high risk of having mild cognitive impairment or AD.ResultThe number of individuals enrolled, and the proportion of those with positive pre‐screening results varied across strategies. The web‐based tool evaluated the largest number of individuals (n=1487) and yielded 547 positive results (36.8%). The Open house initiative pre‐screened 661 subjects of whom 235 (35.6%) obtained a positive result. A total of 435 patients were pre‐screened in the primary care‐based strategy and 193 of them (44.4%) were found to have a positive result. Finally, 264 patients from diabetes clinics underwent pre‐screening and 154 (58.3%) showed a positive result.ConclusionUsing innovative pre‐screening strategies, we were able to identify 1129 individuals at high risk of having dementia who had otherwise remained unnoticed. Initiatives like this, show us the way to go in order to shift the paradigm of AD towards an earlier diagnosis

Mild cognitive impairment and deficits in instrumental activities of daily living: a systematic review

Introduction: There is a growing body of evidence that subtle deficits in instrumental activities of daily living (IADL) may be present in mild cognitive impairment (MCI). However, it is not clear if there are IADL domains that are consistently affected across patients with MCI. In this systematic review, therefore, we aimed to summarize research results regarding the performance of MCI patients in specific IADL (sub)domains compared with persons who are cognitively normal and/or patients with dementia. Methods: The databases PsycINFO, PubMed and Web of Science were searched for relevant literature in December 2013. Publications from 1999 onward were considered for inclusion. Altogether, 497 articles were retrieved. Reference lists of selected articles were searched for potentially relevant articles. After screening the abstracts of these 497 articles, 37 articles were included in this review. Results: In 35 studies, IADL deficits (such as problems with medication intake, telephone use, keeping appointments, finding things at home and using everyday technology) were documented in patients with MCI. Financial capacity in patients with MCI was affected in the majority of studies. Effect sizes for group differences between patients with MCI and healthy controls were predominantly moderate to large. Performance-based instruments showed slight advantages (in terms of effect sizes) in detecting group differences in IADL functioning between patients with MCI, patients with Alzheimer’s disease and healthy controls. Conclusion: IADL requiring higher neuropsychological functioning seem to be most severely affected in patients with MCI. A reliable identification of such deficits is necessary, as patients with MCI with IADL deficits seem to have a higher risk of converting to dementia than patients with MCI without IADL deficits. The use of assessment tools specifically designed and validated for patients with MCI is therefore strongly recommended. Furthermore, the development of performance-based assessment instruments should be intensified, as they allow a valid and reliable assessment of subtle IADL deficits in MCI, even if a proxy is not available. Another important point to consider when designing new scales is the inclusion of technology-associated IADL. Novel instruments for clinical practice should be time-efficient and easy to administer

Complementary pre-screening strategies to uncover hidden prodromal and mild Alzheimer's disease : Results from the MOPEAD project

The Models of Patient Engagement for Alzheimer's Disease (MOPEAD) project was conceived to explore innovative complementary strategies to uncover hidden prodromal and mild Alzheimer's disease (AD) dementia cases and to raise awareness both in the general public and among health professionals about the importance of early diagnosis. Four different strategies or RUNs were used: (a) a web-based (WB) prescreening tool, (2) an open house initiative (OHI), (3) a primary care-based protocol for early detection of cognitive decline (PC), and (4) a tertiary care-based pre-screening at diabetologist clinics (DC). A total of 1129 patients at high risk of having prodromal AD or dementia were identified of 2847 pre-screened individuals (39.7%). The corresponding proportion for the different initiatives were 36.8% (WB), 35.6% (OHI), 44.4% (PC), and 58.3% (DC). These four complementary pre-screening strategies were useful for identifying individuals at high risk of having prodromal or mild AD

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Biomarker counseling, disclosure of diagnosis and follow-up in patients with mild cognitive impairment:A European Alzheimer's Disease Consortium survey

Objectives: Mild cognitive impairment (MCI) is associated with an increased risk of further cognitive decline, partly depending on demographics and biomarker status. The aim of the present study was to survey the clinical practices of physicians in terms of biomarker counseling, management, and follow-up in European expert centers diagnosing patients with MCI. Methods: An online email survey was distributed to physicians affiliated with European Alzheimer's disease Consortium centers (Northern Europe: 10 centers; Eastern and Central Europe: 9 centers; and Southern Europe: 15 centers) with questions on attitudes toward biomarkers and biomarker counseling in MCI and dementia. This included postbiomarker counseling and the process of diagnostic disclosure of MCI, as well as treatment and follow-up in MCI. Results: The response rate for the survey was 80.9% (34 of 42 centers) across 20 countries. A large majority of physicians had access to biomarkers and found them useful. Pre- and postbiomarker counseling varied across centers, as did practices for referral to support groups and advice on preventive strategies. Less than half reported discussing driving and advance care planning with patients with MCI. Conclusions: The variability in clinical practices across centers calls for better biomarker counseling and better training to improve communication skills. Future initiatives should address the importance of communicating preventive strategies and advance planning

P3‐209: Impact of Biomarkers On Diagnostic Confidence in Clinical Assessment of Patients with Suspected Alzheimer's Disease and High Diagnostic Uncertainty: An EADC Study

Background: NIA-AA and IWG diagnostic criteria for Alzheimer's Disease (AD) include core structural, functional, and CSF biomarkers. The impact of core biomarkers in clinical settings is still unclear. This study aimed at measuring the impact of core biomarkers on the diagnostic confidence of uncertain AD cases in a routine memory clinic setting. // Methods: 356 patients with mild dementia (MMSE = 20) or Mild Cognitive Impairment possibly due to AD were recruited in 17 European Alzheimer's Disease Consortium (EADC) memory clinics. The following variables were collected: age; sex; MMSE; neuropsychological evaluation including long term memory, executive functions, language and visuospatial abilities. Core biomarkers were collected following local practices: Scheltens’s visual assessment of medial temporal atrophy (MTA) on MR scan; visual assessment of hypometabolism/hypoperfusion on FDG-PET/SPECT brain scan; CSF Aß1-42, tau and phospho-tau levels. At diagnostic workup completion, an estimate of confidence that cognitive complaints were due to AD was elicited from clinicians on a structured scale ranging from 0 to 100. Only cases with uncertain diagnoses (confidence between 15% and 85%) were retained for analysis. Generalized linear models were used to describe the relationship between the collected measures and the diagnostic confidence of AD. // Results: Neuropsychological assessment was carried out in almost all cases (98% of the cases). Medial temporal atrophy ratings were done in 40% of cases, assessment of cortical hypometabolism/hypoperfusion in 34%, and CSF Aß and tau levels in 26%. The markers that better explained the variability of diagnostic confidence were CSF Aß1-42 level (R2=0.46) and hypometabolism/hypoperfusion (R2=0.45), followed by CSF tau level (R2=0.35), MTA assessment (R2=0.32) and. All figures were highly significant, at p<<0.001. The diagnostic confidence variability due to neuropsychological tests for different domains was lower: MMSE (R2=0.29); long term memory (R2=0.23); executive functions (R2=0.05); language (R2=0.02); visuospatial abilities (R2=0.04) even if significant (p<0.01). // Conclusions: The use of core biomarkers in the clinical assessment of subjects with suspected AD and high diagnostic uncertainty is still limited. However, when assessed, these biomarkers show a higher impact on diagnostic confidence of AD than the most widespread clinical measures