Neurodegenerative diseases, is a collective term for a group of disorders characterized by the progressive neurodegeneration of brain cells, leading to dysfunction(s) and abnormal structure(s) in the central and/or peripheral nervous systems[1]. One of the main causes of neurodegeneration is the aggregation of several misfolded proteins, which in spite of their different type and distribution area, seem to be involved in common overlapping pathways, resulting to a pathogenesis spectrum of neurodegenerative syndromes[2][3]. The most common among them are Alzheimer’s disease, Parkinson’s disease, Amyotrophic lateral sclerosis, Huntington’s disease and Multiple sclerosis. Neurodegenerative diseases are life-threatening due to rapid progressiveness, complexity, heterogeneity and differentialclinical outcomes, affecting several functions and multiple organs[4][5]. Additionally, they cause deficits in both motor and cognitive domains, in combination with dramatic emotional and social changes, leading to implications for the etiology, the early diagnosis and the establishment of effective treatment approach[6]. These facts, in combination with the lack of cureand understanding of external and internal pathological features, contribute to the development and progression of neurodegenerative diseases andcreate an urgent need and intense interest for a leading role in medical research[7].
Although, involvement of protein aggregation is known, the mechanism of cascade activation is not fully understood and knowledge gaps remain[8], leading to the need for novel strategies for the effective development and implementation of standard policies for disease management; effective treatment through the establishment of globally standardized options; and the effective development of diagnostic and prognostic tools[9].
A Complex Field Challenged by Unfit Data
One of the main hidden problems in research, arises from missing data in observational processes and in many cases such missing data can be extremely difficult to be found retrospectively[10][11]. Considering the complexity of research in neurodegenerative diseases and the multidimensional approaches needed in order to create disease-specific treatment management plans; the aggregation of different types of missing data during each step can accumulate towards major uncertainties in the context of global health surveys[12].
Data errors and data gaps occur due to three main reasons: a) improper handling of patients and their data from health and research entities[13]; b) missing demographic parameters[14][15]; and c) variability in healthcare capacities within current global healthcare systems[16]. Patients’ inadequate stratification, is one of the reasons in clinical trials’ failure. For example, poor evidence-based information about patients’ phenotypic and genotypic profiles, could lead to their inclusion in wrong clinical trials and can create bias in downstream research and observational studies, leading to slower progression and/or inaccurate results[17][18]. Furthermore, the absence of a formal reporting system for diagnosed cases in a number of neurodegenerative diseases, the potential misrepresentation of disease prevalence due to unrecognized cases and the variability inhealth care capacities, can all influence data collections. This lack of harmonized collection and management of data is amplified by different policies relating to the later data sharing needs[19]. As a result of all the above, accurate, disease-specific information is often missing, causing decreased dissemination of scientific knowledge, slower progression in research and healthcare decision making[20][21].
The Real-World Data Concept
For effective future treatment management and in order to decrease non-specific medical interventions, accelerate linkage between clinical trials and the real world (i.e. treatment implementation), strong evidence based practices should be implemented[22]. These future practices will form from the conjugation of Real-World Data (RWD) records (Fig.1), collected in non-randomized trials settings from multiple sources, as well as records of Patients Generated Health Data (PGHD) for targeted guidance in health care decisions[23][24]. Patients are enabled to utilize a broad spectrum of digital tools in order to be fully responsible for the capturing and recording of any data generated by them, increasing their engagement in clinical decision making[25].
Among the several sources for RWD collection, PGHD can prove a valuable tool for patient care improvement[26]. These real-time routinely based data records, provide insights on patients’ experiences that would not be otherwise recorded. They increase the validation of Randomized clinical control trials (RCT), by adding more information and giving answers to the same research question[27]. By comparing multiple clinical strategies and examining multiple clinical outcomes from a broad range of clustered groups of patients from diverse demographic areas, the discordance between RWD and RCT is decreased[28]. Based on systematically RWD and PGHD collection, both Research and Healthcare entities will be able to have access to Real-time multi-national, large scale and disease-specific data sets, in order to evaluate common disease pathways and enhance public health surveillance, reaching the goal for personalized medicine and development of novel diagnostic and prognostic tools[29][30].
According to the World Health Organization’s estimation on the global burden from neurodegenerative disorders, it is important to set abridge between public health specialists and neurologists[31]. The inclusion of PGDR can act as such a bridge, especially in neurodegenerative diseases research, where multidisciplinary assessments should be evaluated through many different lenses, addressing the current complexities in disease management resulting from the differential patients’ outcomes and diverse sequelae[32].
Despite the number of potential applications that have been introduced to capture patients RWD data, many limitations and challenges have been reported, considering four very important factors; accessibility, presentiveness, validation and privacy[33][34].
Tools for Real-World Data Collection
Hence, new approaches and innovative systems for Real-World Data collection should be considered to complement current systems, in order to overcome the reported barriers in data processing management and to decrease the number of scuttered, incomplete data. Qualitative studies associated with adoption of complete Electronic Health Records (EHRs) in China’s health care system, reflected on barriers leading to lower data quality such as data standardization, extraction, verification and high-intensity work environment, as well as patients reporting the need for processes for daily data recordings through the improvement of software functionalities[35]. The affected as described structure of clinical trial data collection, could be enhanced via the systematical involvement of patients in the data collection processes and the implementation of a system and process for overcoming any interoperability problems. Barriers are reported for routinely collected public health data sharing in global framework policies through international centralized platforms, including technical difficulties in data processing, presentiveness and format, as well as lack of availability of standardized technical solutions[36]. Tools used to capture, analyze and share RWD data should take into consideration their dynamism and be able to capture their changes in longitudinal manner; missing in many organizations that have implemented RWD data into their healthcare management system [37]. e-MetaBio®, an innovative platform for biomedical data collection, introduces a system for longitudinal capture of harmonized data from Patients, Biobanks, Healthcare Providers and Researchers in order to create disease specific data sets (Fig. 2). These data sets are ontologically synchronized and unified, providing a novel aggregator for data storage processing and share.
Specifically, Metabio created the e-MetaHealth®, a platform for PGHD, aiming to decrease the gaps in knowledge and to provide a multi-dimensional environment for future monitoring and analysis of big data, associated with neurodegenerative disorders. Socioeconomic, health-related, environmental and RWD data parameters are implemented to create fully informed patients’ profiles for a spectrum of neurodegenerative disorders. This digital tool for longitudinal data records enables them to monitor and record their own health status.They are not only enabled to record data on their own and decide on the access rights of the usage chain over their data and donation, but can also monitor their disease, by comparing themselves to other patients and interact with the healthcare entities for personalized medicine management.
In short, after the successful registration and entrance to the system, patients are enabled with the Dynamic Real-time e-consent[38][39], in order to create their consent status. Patients can select their data donation status, their relationship with entities within the network (e.g., coded consent status, acceptance for further communication) for future contact, access on biosamples and data, as well as the type of research upon those samples and data that might have been collected. The Dynamic Real-time e-consent, not only provides patients with complete control, but also healthcare and research entities with a valuable tool, from which any changes on consent status can be tracked in real-time. Patients can consent to more than one research projects, either simultaneously or consecutively. Re-consent process is automatically achieved as new research proposals are created and entered into the functionality; thus, institutions, through a simple process, inform patients of novel research questions and can acquire consent acceptance instantly. Patients can withdraw at any time and can be easily informed for any changes in research and re-consented if necessary.
e-MetaHealth® is designed to capture different types of patients’ data in a structured manner, in order to link them with potential downstream analytical options. The platform is enabled with a patient-oriented interface from which data records and clinical data can be captured in real-time. For example, patients can record their demographic data, disease specific data, including symptomatology, vitals and current treatment approaches, genealogy data, physical data, psychology data and lifestyle factors that can contribute, or not, to the severity of their symptoms or the disease. The system is designed in order to be convenient, adaptable and easy to be used; all menus include prefixed options and workflows, drag and drop tools and functionalities that do not require advanced IT skills and complex sets of commands. In this user-friendly environment, specific visual analytics are implemented, enabling patients to monitor their vital status, disease progression, symptoms severity, in a single or combined mode, with their current treatment approach, to compare their treatment with previously treatment approaches as well as with that of others dynamically and in real-time. The aim is for patients to be informed and involved within their healthcare planning processes, and through such data capture, approached increased value is given to their routinely based experiences. Furthermore, the availability of such granular data directly to patients could foster citizen science as has happened in other clinical fields[40].
The platform is GDPR and HIPPA compliant, following all cybersecurity, data protection and privacy requirements. The application, service and data layers are supported by interoperable and harmonized technologies and standards, such as HAPI FHIR server, providing a tool for longitudinal data capturing, monitoring and analysis, which follows international HL7 standards for clinical data management[41][42].
Linking Two Complementary Data Worlds
Such a PGHD approach, as the one described above, follows on the principles of RWD and can offer gains for both the research community and patients, summarized in Table 1. Especially in the case of epidemiological aspects of neurodegenerative diseases, it enables the “crowd-sourcing” of epidemiological data over time and addresses the feasibility of data collection in a dynamic manner. Therefore, routinely collected clinical data through patient registries can be complemented by data cohorts, associated with the different patient groups’ outcomes and clinical phenotypes. Self-reported data on disease progression can add to an increased understanding of variables among clinical cases, their etiology and the unique progression of the disease of certain cohorts from different demographic areas with different genetic backgrounds and hereditary traits. On the other hand, several factors that can contribute to biases that seriously affect the research validity based on PGHD should be, also considered and carefully planned, such as personally traits, psychologically traits, comprehensiveness, self-reporting estimation, social-desirability/improvement, ability to recall, physical and cognitive status[43][44][45]. Balanced usage and implementation should be designed in order to decrease any kind of additional bias in research and health care management.
It is anticipated that future, collective intelligence methodologies for patients and healthcare systems, can be based on the generation of valuable real-time patient data sets. Major data cohorts’ analyses and access to comprehensive real-time RWD data from patients with neurodegenerative disorders, research and development is promoted strongly for current health systems[46].
| Research Community | Patients |
|
|
Table 1. e-MetaHealth® gains for Research Community and Patients
The technical solution can be in the form of an internet-based federated service, including a digital rights management system distributed to various management points, controlling the acceptance and release of patient-related health data. e-MetaHealth®, is one such potential solution, empowering patients, and connecting PGHD with all available sources of clinical data. As such it is designed to offer interoperability and services to patients and the healthcare chain, thus addressing the gaps highlighted by the availability of fit-for-purpose data in the complex field of neurodegenerative diseases.
Authors: Panagiotis Katsaounis, Desislava Ivanova
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