Forecasting individual progression trajectories in Alzheimer's disease

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials.

Forecasting individual progression trajectories in Huntington disease enables more powered clinical trials

Variability in neurodegenerative disease progression poses great challenges for the evaluation of potential treatments. Identifying the persons who will experience significant progression in the short term is key for the implementation of trials with smaller sample sizes. We apply here disease course mapping to forecast biomarker progression for individual carriers of the pathological CAG repeat expansions responsible for Huntington disease. We used data from two longitudinal studies (TRACK-HD and TRACK-ON) to synchronize temporal progression of 15 clinical and imaging biomarkers from 290 participants with Huntington disease. We used then the resulting HD COURSE MAP to forecast clinical endpoints from the baseline data of 11,510 participants from ENROLL-HD, an external validation cohort. We used such forecasts to select participants at risk for progression and compute the power of trials for such an enriched population. HD COURSE MAP forecasts biomarkers 5 years after the baseline measures with a maximum mean absolute error of 10 points for the total motor score and 2.15 for the total functional capacity. This allowed reducing sample sizes in trial up to 50% including participants with a higher risk for progression ensuring a more homogeneous group of participants.

AD Course Map charts Alzheimer's disease progression

Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer's disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient's cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer's disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.

Peculiarities of the psychological state of patients with chronic non-infectious liver diseases

Introduction

The prevalence of chronic liver diseases (CLD) is over 30 million people worldwide, they are associated with significant health care costs, loss of productivity of patients, and has a significant impact on the quality of life associated with health.

Objectives

The research objective was to determine current views on the psychological state of patients with CLD.

Methods

A qualitative and quantitative analysis of the content of scientific Ukrainian and English literature published from 2014 to 2020, which sets out different views on the psychological state of patients with chronic liver diseases using the PubMed and Google Scholar databases. Only concept analysis, meta-analysis, and systematic reviews published in English, presented in the scientific literature were included.

Results

The information on the peculiarities of the psychological state of patients with CLD was generalized. Based on the research, we can conclude that this group of patients is characterized by low mood, chronic fatigue, low level of social adaptation, increased anxiety, and reduced efficiency.

Conclusions

Studies by different scientists from different countries agree that patients with chronic liver disease are characterized by the above symptoms. Some emphasize psychoneurophysiology and associate these symptoms with chronic inflammation, which occurs as liver damage progresses. Other researchers suggest that it is due to the quality of life of these patients and the severity of the disease. However, the scientific community has yet to find out what exactly caused this.

Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review

We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.

; THE RIGHT TO HEALTH: UKRAINE'S INTERNATIONAL OBLIGATIONS AND FINANCIAL ACTIVITY OF PUBLIC AUTHORITIES IN THE CONTEXT OF REFORMING THE NATIONAL HEALTHCARE SYSTEM

The article explores the issue of the human right to health as a fundamental one in a democratic society, international norms and standards, international obligations of Ukraine, as well as the financial activities of public authorities in the context of radical reform of the health care system in Ukraine. It is being focused onto the WHO's "Health for All" policy, as well as "Health 2020", which became the basis for a new European Health Strategy, the nationwide Health 2020: Ukrainian Dimension. It is emphasized that the financing function of the WHO and other international organizations is considered to be the key function of the health system. The purpose of this article is of this article is to identify the peculiarities of the financial activities of public authorities in the context of a thorough reform of the healthcare system in Ukraine and to evaluate the compliance of national legislation with international norms and standards. The methodological basis of the conducted research is the general methods of scientific cognitivism as well as concerning those used in legal science: methods of analysis and synthesis, formal logic, comparative law etc.; The norms of international documents of universal and regional status, adopted by the new legislation of Ukraine in the light of radical reform of the health care system, are analyzed. The object of the study is the public relations that arise during implementation of financial activities by public authorities.; The research revealed measures taken by the state to fulfill its obligations under international treaties in the field of healthcare: amending the current legislation and practice of its implementation; making changes to administrative practice; providing legal expertise of bills; provision of training in the study of international treaties and the practice of international judicial institutions to categories of the employees whose professional activity is related to law enforcement, as well as to the detention of people (imprisoned); measures taken to ensure the elimination of systemic deficiencies and the cessation of breaches within international treaties caused by these deficiencies. Attention is drawn to the relation between the concepts of "public finances" and "public financial activities". The peculiarities of the legal organization of financial activity of the state and bodies of local self-government, the content of financial activity in the sphere of healthcare are revealed. The role of the state and local self-government bodies in financial activity as subjects of financial function is revealed. The principles, methods, forms of financial activity are described. It is emphasized that the new model of financing healthcare in Ukraine is based on the following principles: financial protection; universality of coverage and equity of access to care; transparency and accountability; efficiency; free choice; competition among suppliers; predictability of the volume of funds for medical services in the state budget; subsidiarity.

Spatiotemporal Propagation of the Cortical Atrophy: Population and Individual Patterns

Repeated failures in clinical trials for Alzheimer’s disease (AD) have raised a strong interest for the prodromal phase of the disease. A better understanding of the brain alterations during this early phase is crucial to diagnose patients sooner, to estimate an accurate disease stage, and to give a reliable prognosis. According to recent evidence, structural alterations in the brain are likely to be sensitive markers of the disease progression. Neuronal loss translates in specific spatiotemporal patterns of cortical atrophy, starting in the enthorinal cortex and spreading over other cortical regions according to specific propagation pathways. We developed a digital model of the cortical atrophy in the left hemisphere from prodromal to diseased phases, which is built on the temporal alignment and combination of several short-term observation data to reconstruct the long-term history of the disease. The model not only provides a description of the spatiotemporal patterns of cortical atrophy at the group level but also shows the variability of these patterns at the individual level in terms of difference in propagation pathways, speed of propagation, and age at propagation onset. Longitudinal MRI datasets of patients with mild cognitive impairments who converted to AD are used to reconstruct the cortical atrophy propagation across all disease stages. Each observation is considered as a signal spatially distributed on a network, such as the cortical mesh, each cortex location being associated to a node. We consider how the temporal profile of the signal varies across the network nodes. We introduce a statistical mixed-effect model to describe the evolution of the cortex alterations. To ensure a spatiotemporal smooth propagation of the alterations, we introduce a constrain on the propagation signal in the model such that neighboring nodes have similar profiles of the signal changes. Our generative model enables the reconstruction of personalized patterns of the neurodegenerative spread, providing a way to estimate disease progression stages and predict the age at which the disease will be diagnosed. The model shows that, for instance, APOE carriers have a significantly higher pace of cortical atrophy but not earlier atrophy onset.