A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment

AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis

Difficulties of Diagnosing Alzheimer's Disease: The Application of Clinical Decision Support Systems

Introduction: Alzheimer's disease is one of the most common causes of dementia, which gradually causes cognitive impairment. Diagnosis of Alzheimer's disease is a complicated process performed through several tests and examinations. Design and development of Clinical Decision Support System (CDSS) could be an appropriate approach for eliminating the existing difficulties of diagnosing Alzheimer's disease. Materials and Methods: This study reviews the current problems in the diagnosis of Alzheimer's disease with an approach to the application of CDSS. The study reviewed the articles published from 1990 to 2016. The articles were identified by searching electronic databases such as PubMed, Google Scholar, Science Direct. Considering the relevance of articles with the objectives of the study, 29 papers were selected. According to the performed investigations, various reasons cause difficulty in Alzheimer's diagnosis. Results: The complexity of diagnostic process and  the similarity of Alzheimer's disease with other causes of dementia are the most important of them. The results of studies about the application of CDSSs on Alzheimer's disease diagnosis indicated that the implementation of these systems could help to eliminate the existing difficulties in the diagnosis of Alzheimer's disease. Conclusion: Developing CDSSs based on diagnostic guidelines could be regarded as one of the possible approaches towards early and accurate diagnosis of Alzheimer's disease. Applying of computer-interpretable guideline (CIG) models such as GLIF, PROforma, Asbru, and EON can help to design CDSS with the capability of minimizing the burden of diagnostic problems with Alzheimer's disease

Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer

Supervised machine learning in psychiatry:towards application in clinical practice

In recent years, the field of machine learning (often named with the more general term artificial intelligence) has literally exploded and its application has been proposed in basically all fields, including psychiatry and mental health. This has been motivated by the promise of using machine learning to develop new clinical tools that could help perform personalized predictions and recommendations, ultimately improving the results achievable in the psychiatric clinical practice that still faces only a limited success in the fight against mental diseases. However, despite this huge interest, there is still a substantial lack of tools in psychiatry that are based on machine learning algorithms. Massimiliano Grassi, in his Ph.D. thesis, investigates the challenges of translating machine learning algorithms into clinical practice and proposes innovative solutions to these challenges. The thesis presents the development and validation of new algorithms for the prediction of the onset of Alzheimer’s disease, the remission of obsessive-compulsive disorder, and the automatization of sleep staging in polysomnography, a method to diagnose sleep disorders. The results from these studies demonstrate that the use of machine learning in psychiatric clinical practice is not just a promise, and it is possible to develop machine learning algorithms that achieve clinically relevant performance even if based solely on information that can be easily accessible in the daily clinical routine

Efficacy of probucol on cognitive function in Alzheimer's disease: study protocol for a double-blind, placebo-controlled, randomised phase II trial (PIA study).

INTRODUCTION: Preclinical, clinical and epidemiological studies support the hypothesis that aberrant systemic metabolism of amyloid beta (Aβ) in the peripheral circulation is causally related to the development of Alzheimer's disease (AD). Specifically, recent studies suggest that increased plasma concentrations of lipoprotein-Aβ compromise the brain microvasculature, resulting in extravasation and retention of the lipoprotein-Aβ moiety. The latter results in an inflammatory response and neurodegeneration ensues. Probucol, a historic cholesterol-lowering drug, has been shown in murine models to suppress lipoprotein-Aβ secretion, concomitant with maintaining blood-brain-barrier function, suppressing neurovascular inflammation and supporting cognitive function. This protocol details the probucol in Alzheimer's study, a drug intervention trial investigating if probucol has potential to attenuate cognitive decline, delay brain atrophy and reduce cerebral amyloid burden in patients with mild-to-moderate AD. METHODS AND ANALYSIS: The study is a phase II, randomised, placebo-controlled, double-blind single-site clinical trial held in Perth, Australia. The target sample is 314 participants with mild-to-moderate AD. Participants will be recruited and randomised (1:1) to a 104-week intervention consisting of placebo induction for 2 weeks followed by 102 weeks of probucol (Lorelco) or placebo. The primary outcome is changed in cognitive performance determined via the Alzheimer's Disease Assessment Scales-Cognitive Subscale test between baseline and 104 weeks. Secondary outcomes measures will be the change in brain structure and function, cerebral amyloid load, quality of life, and the safety and tolerability of Lorelco, after a 104week intervention. ETHICS AND DISSEMINATION: The study has been approved by the Bellberry Limited Human Research Ethics Committee (approval number: HREC2019-11-1063; Version 4, 6 October 2021). Informed consent will be obtained from participants prior to any study procedures being performed. The investigator group will disseminate study findings through peer-reviewed publications, key conferences and local stakeholder events. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12621000726853)

Challenges in Dementia Studies

Alzheimer’s and other neurodegenerative diseases are generally incurable and often difficult to diagnose accurately. Yet early and accurate diagnosis of a neurodegenerative disease can potentially contribute to more effective treatment. Hence research efforts are moving towards early identification of high risk subjects and prevention of disease progression with biomarkers. Unfortunately dementia and biomarker studies are hampered by variables such as drop outs, challenges in comparing data sets, discordant biomarker sets, availability of histopathological confirmation at death, validity of cognitive testing, and nonlinear fluctuations in cognitive domains as disease progresses in vivo in subjects. This chapter is an assessment of the challenges in the early diagnosis of dementia, as well as a presentation of the issues faced in conducting dementia and biomarker studies

Cholinesterase inhibitors for vascular dementia and other vascular cognitive impairments:a network meta-analysis (Review)

BACKGROUND: Vascular cognitive impairment (VCI) describes a broad spectrum of cognitive impairments caused by cerebrovascular disease, ranging from mild cognitive impairment to dementia. There are currently no pharmacological treatments recommended for improving either cognition or function in people with VCI. Three cholinesterase inhibitors (donepezil, galantamine, and rivastigmine) are licenced for the treatment of dementia due to Alzheimer's disease. They are thought to work by compensating for reduced cholinergic neurotransmission, which is also a feature of VCI. Through pairwise comparisons with placebo and a network meta‐analysis, we sought to determine whether these medications are effective in VCI and whether there are differences between them with regard to efficacy or adverse events. OBJECTIVES: (1) To assess the efficacy and safety of cholinesterase inhibitors in the treatment of adults with vascular dementia and other VCI. (2) To compare the effects of different cholinesterase inhibitors on cognition and adverse events, using network meta‐analysis. SEARCH METHODS: We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group's register, MEDLINE (OvidSP), Embase (OvidSP), PsycINFO (OvidSP), CINAHL (EBSCOhost), Web of Science Core Collection (ISI Web of Science), LILACS (BIREME), ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform on 19 August 2020. SELECTION CRITERIA: We included randomised controlled trials in which donepezil, galantamine, or rivastigmine was compared with placebo or in which the drugs were compared with each other in adults with vascular dementia or other VCI (excluding cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL)). We included all drug doses and routes of administration. DATA COLLECTION AND ANALYSIS: Two review authors independently identified eligible trials, extracted data, assessed risk of bias, and applied the GRADE approach to assess the certainty of the evidence. The primary outcomes were cognition, clinical global impression, function (performance of activities of daily living), and adverse events. Secondary outcomes were serious adverse events, incidence of development of new dementia, behavioural disturbance, carer burden, institutionalisation, quality of life and death. For the pairwise analyses, we pooled outcome data at similar time points using random‐effects methods. We also performed a network meta‐analysis using Bayesian methods. MAIN RESULTS: We included eight trials (4373 participants) in the review. Three trials studied donepezil 5 mg or 10 mg daily (n= 2193); three trials studied rivastigmine at a maximum daily dose of 3 to 12 mg (n= 800); and two trials studied galantamine at a maximum daily dose of 16 to 24 mg (n= 1380). The trials included participants with possible or probable vascular dementia or cognitive impairment following stroke. Mean ages were between 72.2 and 73.9 years. All of the trials were at low or unclear risk of bias in all domains, and the evidence ranged from very low to high level of certainty. For cognition, the results showed that donepezil 5 mg improves cognition slightly, although the size of the effect is unlikely to be clinically important (mean difference (MD) −0.92 Alzheimer's Disease Assessment Scale‐Cognitive Subscale (ADAS‐Cog) points (range 0 to 70), 95% confidence interval (CI) −1.44 to −0.40; high‐certainty evidence). Donepezil 10 mg (MD −2.21 ADAS‐Cog points, 95% CI −3.07 to −1.35; moderate‐certainty evidence) and galantamine 16 to 24 mg (MD −2.01 ADAS‐Cog point, 95%CI −3.18 to −0.85; moderate‐certainty evidence) probably also improve cognition, although the larger effect estimates still may not be clinically important. With low certainty, there may be little to no effect of rivastigmine 3 to 12 mg daily on cognition (MD 0.03 ADAS‐Cog points, 95% CI −3.04 to 3.10; low‐certainty evidence). Adverse events reported in the studies included nausea and/or vomiting, diarrhoea, dizziness, headache, and hypertension. The results showed that there was probably little to no difference between donepezil 5 mg and placebo in the number of adverse events (odds ratio (OR) 1.22, 95% CI 0.94 to 1.58; moderate‐certainty evidence), but there were slightly more adverse events with donepezil 10 mg than with placebo (OR 1.95, 95% CI 1.20 to 3.15; high‐certainty evidence). The effect of rivastigmine 3 to 12 mg on adverse events was very uncertain (OR 3.21, 95% CI 0.36 to 28.88; very low‐certainty evidence). Galantamine 16 to 24 mg is probably associated with a slight excess of adverse events over placebo (OR 1.57, 95% CI 1.02 to 2.43; moderate‐certainty evidence). In the network meta‐analysis (NMA), we included cognition to represent benefit, and adverse events to represent harm. All drugs ranked above placebo for cognition and below placebo for adverse events. We found donepezil 10 mg to rank first in terms of benefit, but third in terms of harms, when considering the network estimates and quality of evidence. Galantamine was ranked second in terms of both benefit and harm. Rivastigmine had the lowest ranking of the cholinesterase inhibitors in both benefit and harm NMA estimates, but this may reflect possibly inadequate doses received by some trial participants and small trial sample sizes. AUTHORS' CONCLUSIONS: We found moderate‐ to high‐certainty evidence that donepezil 5 mg, donepezil 10 mg, and galantamine have a slight beneficial effect on cognition in people with VCI, although the size of the change is unlikely to be clinically important. Donepezil 10 mg and galantamine 16 to 24 mg are probably associated with more adverse events than placebo. The evidence for rivastigmine was less certain. The data suggest that donepezil 10 mg has the greatest effect on cognition, but at the cost of adverse effects. The effect is modest, but in the absence of any other treatments, people living with VCI may still wish to consider the use of these agents. Further research into rivastigmine is needed, including the use of transdermal patches

Biomarker-And Pathway-Informed Polygenic Risk Scores for Alzheimer's Disease and Related Disorders

Indiana University-Purdue University Indianapolis (IUPUI)Determining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders