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

__Abstract__ 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

Trajectories and Determinants of Quality of Life in Dementia with Lewy Bodies and Alzheimer's Disease

Background: Quality of Life (QoL) is an important outcome measure in dementia, particularly in the context of interventions. Research investigating longitudinal QoL in dementia with Lewy bodies (DLB) is currently lacking. Objective: To investigate determinants and trajectories of QoL in DLB compared to Alzheimer’s disease (AD) and controls. Methods: QoL was assessed annually in 138 individuals, using the EQ5D-utility-score (0–100) and the health-related Visual Analogue Scale (VAS, 0–100). Twenty-nine DLB patients (age 69 ± 6), 68 AD patients (age 70 ± 6), and 41 controls (age 70 ± 5) were selected from the Dutch Parelsnoer Institute-Neurodegenerative diseases and Amsterdam Dementia Cohort. We examined clinical work-up over time as determinants of QoL, including cognitive tests, neuropsychiatric inventory, Geriatric Depression Scale (GDS), and disability assessment of dementia (DAD). Results: Mixed models showed lower baseline VAS-scores in DLB compared to AD and controls (AD: ±SE = -7.6 ± 2.8, controls: ±SE = -7.9 ± 3.0, p < 0.05). An interaction between diagnosis and time since diagnosis indicated steeper decline on VAS-scores for AD patients compared to DLB patients (±SE = 2.9 ± 1.5, p < 0.1). EQ5D-utility-scores over time did not differ between groups. Higher GDS and lower DAD-scores were independently associated with lower QoL in dementia patients (GDS: VAS ±SE = -1.8 ± 0.3, EQ5D-utility ±SE = -3.7 ± 0.4; DAD: VAS = 0.1 ± 0.0, EQ5D-utility ±SE = 0.1 ± 0.1, p < 0.05). No associations between cognitive tests and QoL remained in the multivariate model. Conclusion: QoL is lower in DLB, while in AD QoL shows steepe

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

The Dutch Parelsnoer Institute - Neurodegenerative diseases; methods, design and baseline results

Background: The is a collaboration between 8 Dutch University Medical Centers in which clinical data and biomaterials from patients suffering from chronic diseases (so called "Pearls") are collected according to harmonized protocols. The Pearl Neurodegenerative Diseases focuses on the role of biomarkers in the early diagnosis, differential diagnosis and in monitoring the course of neurodegenerative diseases, in particular Alzheimer's disease. Methods: The Pearl Neurodegenerative Diseases is a 3-year follow-up study of patients referred to a memory clinic with cognitive complaints. At baseline, all patients are subjected to a standardized examination, including clinical data and biobank materials, e.g. blood samples, MRI and cerebrospinal fluid. At present, in total more than 1000 patients have been included, of which cerebrospinal fluid and DNA samples are available of 211 and 661 patients, respectively. First descriptives of a subsample of the data (n = 665) shows that patients are diagnosed with dementia (45%), mild cognitive impairment (31%), and subjective memory complaints (24%). Discussion: The Pearl Neurodegenerative Diseases is an ongoing large network collecting clinical data and biomaterials of more than 1000 patients with cognitive impairments. The project has started with data analyses of the baseline characteristics and biomarkers, which will be the starting point of future specific research questions that can be answered by this unique dataset

Potentially inappropriate medication use in older adults with mild-moderate Alzheimer's disease:Prevalence and associations with adverse events

Aim: Potentially inappropriate medication (PIM) use is prevalent in older adults and is associated with adverse events, hospitalisation and mortality. We assessed the patterns and associations of PIM use in older adults with mild-to-moderate Alzheimer's Disease (AD), who may represent a particularly vulnerable group. Design: Analysis of data from NILVad, an 18-month Randomised Control Trial of Nilvadapine in mild-to-moderate AD. The v2 STOPP criteria were applied in duplicate to identify PIM use. Associations between PIM use and adverse events/unscheduled healthcare visits in addition to the associations between PIM use and AD progression were evaluated. Setting and Participants: 448 older adults with mild-to-moderate AD from 23 centres in nine European countries. Results: Of 448 participants (mean age: 72.56 ± 8.19 years), over half (55.8%) were prescribed a PIM with 30.1% being prescribed 2+ PIMs. The most frequent PIMs were (i) long-term benzodiazepines (11.6% N = 52/448), (ii) selective serotonin reuptake inhibitors without appropriate indication (11.1% N = 50/448), and (iii) Proton-Pump Inhibitors (PPIs) without appropriate indication (10.7% N = 48/448). Increasing number of PIMs was associated with a greater risk of adverse events (IRR 1.17, 1.13-1.19, P &lt; 0.001), serious adverse events (IRR 1.27; 1.17-1.37, P &lt; 0.001), unscheduled hospitalisations (IRR 1.16, 1.03-1.30, P = 0.016) and GP visits (IRR 1.22, 1.15-1.28, P &lt; 0.001). PIM use was not associated with dementia progression. Conclusions and Implications: PIM use is highly prevalent in mild-to-moderate AD and is associated with adverse events and unscheduled healthcare utilisation. Further attention to de-prescribing in this vulnerable group is warranted

Diagnostic and economic evaluation of new biomarkers for Alzheimer's disease: the research protocol of a prospective cohort study

Doc number: 72 Abstract Background: New research criteria for the diagnosis of Alzheimer's disease (AD) have recently been developed to enable an early diagnosis of AD pathophysiology by relying on emerging biomarkers. To enable efficient allocation of health care resources, evidence is needed to support decision makers on the adoption of emerging biomarkers in clinical practice. The research goals are to 1) assess the diagnostic test accuracy of current clinical diagnostic work-up and emerging biomarkers in MRI, PET and CSF, 2) perform a cost-consequence analysis and 3) assess long-term cost-effectiveness by an economic model. Methods/design: In a cohort design 241 consecutive patients suspected of having a primary neurodegenerative disease are approached in four academic memory clinics and followed for two years. Clinical data and data on quality of life, costs and emerging biomarkers are gathered. Diagnostic test accuracy is determined by relating the clinical practice and new research criteria diagnoses to a reference diagnosis. The clinical practice diagnosis at baseline is reflected by a consensus procedure among experts using clinical information only (no biomarkers). The diagnosis based on the new research criteria is reflected by decision rules that combine clinical and biomarker information. The reference diagnosis is determined by a consensus procedure among experts based on clinical information on the course of symptoms over a two-year time period. A decision analytic model is built combining available evidence from different resources among which (accuracy) results from the study, literature and expert opinion to assess long-term cost-effectiveness of the emerging biomarkers. Discussion: Several other multi-centre trials study the relative value of new biomarkers for early evaluation of AD and related disorders. The uniqueness of this study is the assessment of resource utilization and quality of life to enable an economic evaluation. The study results are generalizable to a population of patients who are referred to a memory clinic due to their memory problems. Trial registration: NCT0145089