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Create a harmonized data infrastructure in order to normalize and precisely characterise data from heterogeneous and longitudinal sources in different formats, languages and degrees of structure.
The first objective aims to provide the data infrastructure as a prerequisite for modelling. To this end, a data warehouse that is capable of hosting and providing the data used in the study will be created. The data warehouse will be built after identification of all potential data source characteristics, user needs and data exchange needs. It will adhere to all legal and technological standards allowing state-of-the-art date storage and exchange, a crucial prerequisite for the project. Furthermore, data will be harmonized to allow pooling of similar data in heterogeneous data sources. Data harmonization efforts will revolve around collecting requirements for data integration and modelling, constructing a thesaurus for the languages of the used data sources and, consequently, a common ontology-based data model. Next to this, natural language processing will be used to structure clinical texts. Data harmonization and the development of models will represent an iterative process organised in release cycles. To allow early development of models, the first release cycle of harmonized data will comprise the structured data, whereas a later release will provide the unstructured data that need more time to extract.
Identify health factors, risk factors, resilience factors and life events in stroke affecting well-being (integrated quality-of-life-concept).
This objective is highly cross-disciplinary and needs to be performed prior to modelling, but iteratively in parallel to Objective 1. It entails defining the inputs and outputs for the modelling phase. First, the most likely scenarios and use cases for the clinical phases of stroke will be defined. Then, we will identify, rank and weigh input factors of stroke available in our databases in terms of both risk and resilience. The activity will consist of iterations of dialogue between clinicians and modellers. It will also entail experimentation with the data to identify correlations between factors and patient well-being and the use of techniques such as Latent Class Analysis and clustering to identify if masked subgroups (such as early or nonresponders to certain treatments) are present in the harmonized datasets. Defining the outputs for the models, it is crucial to understand how the model output directly links to interventions in patient treatment and how the model fits into the treatment processes within the care setting. Most of our models will provide complex outputs. For some models this added complexity in output is necessary because they explicitly take time scales into account. For other models this added complexity is required to reflect a broader understanding of the factors contributing to resilience and well-being. A central motive of our study is to use a quality-of-life framework as a proxy for resilience and wellbeing. Our models will predict a set of interrelated factors that reflect the variety and evolving factors contributing to an individual’s quality of life. Thus, in this objective the outputs variables will be identified, weighted and integrated in a quality-of-life framework.
Develop a set of predictive models that target the four different stages of a stroke patient’s trajectory (prevention, stroke treatment, stroke rehabilitation, stroke reintegration)
This objective is our core objective as it encompasses the development of the predictive models for each of the four clinical phases of stroke, i.e. prevention, treatment, rehabilitation and reintegration. Our models will be based on the harmonized data sources from objective 1 and the input/output definitions from objective 2. A major focus of this objective is hybrid modelling for the first two clinical stages of stroke. Hybrid modelling is the combined use of mechanistic and phenomenological models. Hybrid modelling combines the best of two worlds: i) the simulation-based possibilities of mechanistic models with ii) the predictive versatility of machine-learning models. In the stroke prevention stage the PRECISE4Q model will predict the evolution of key patient features over in response to different treatments, such as diet, exercise, and medication in a hybrid modelling approach. It will predict the overall risk of stroke for a patient participating in a treatment plan. Using the outputs of this model a healthcare professional will be able to develop and compare personalised intervention regimes for their patients to increase the patient’s resilience to stroke. In a hybrid modelling approach the outputs of an existing biophysiological mechanistic imaging-based model of brain blood flow and perfusion will be fused with patient-specific clinical data to predict a modified Rankin Scale for the patient 3 months post-stroke. The second model will integrate a range of heterogeneous patient-specific data (e.g. lab, OMICs etc.), information extracted from the textual records, and the modified Rankin Scale estimated by the other model. The output of this second model will be a complex quality of life (QoL) profile for the patient, including QoL indicators such the probability of the patient returning to work. For the rehabilitation stage PRECISE4Q will create a model that will generate a patient specific daily schedule of rehabilitation activities annotated with the expected performance level of the activity along with the expected cognitive and functional profile of the patient at the end of the rehabilitation programme. The output of the model will support doctors in designing personalised rehabilitation plans for each patient. The PRECISE4Q model supporting patient treatment during reintegration phase will predict a long-term (1-year post-stroke) complex QoL profile for a patient. The output of the model will inform the design of individualised support plans to long-term resilience and quality of life.
Gather in prospective clinical studies unique personalised multi-dimensional longitudinal datasets reflecting patient’s trajectory for validation.
This objective aims to collect high-quality patient data (n=1200) from two prospective studies. The first prospective study will target stage 2, acute stroke. The other prospective study will target stage 3 and 4, stroke rehabilitation and reintegration. For both studies, the definition of study parameters will be performed according to the defined inputs and outputs in objective 2. This will ensure the seamless merging of study data with the project data warehouse and data harmonisation. Additionally, for both studies a patient-reported outcome concept will be developed. Currently, the gathering of the data is researcher-centred as data are collected topdown by the performing clinical staff. This is often inefficient and does not consider patient motivation leading to high rates of “lost-to-follow-up”. To facilitate the gathering of high quality data, end-user-based technologies wil be employed to obtain patient-centered data. To this end an electronic case report form (eCRF) will be developed allowing the fine-meshed recording of patient-reported outcome by research staff and by the patients themselves. The result will be an electronic patient recorded outcome (ePRO).
Validate the predictive models using data from prospective studies and to integrate the final models in a comprehensive platform.
The clinical data from the prospective studies will be used to validate models for stages 2, 3 and 4 that have been developed using retrospective registry data in objective 3. Once the final models are established, they will be integrated in a one-stop-shop webbased platform, coined the “Digital Stroke Patient Platform”. This platform will be an open service allowing researchers to test and apply our models on their own data.
Develop personalised and stagespecific interventions to increase resilience to stroke and increase well-being and reintegration after stroke.
The “Digital Stroke Patient Platform” will be used to simulate patient courses. Additionally, the weighting of the input factors from the models wil be taken into account. In this first round, possible factors that can be the target for interventions will be identified. Finally, these factors will be assessed for clinical feasibility. The final output of this objective is a set of clinically feasible interventions for each stage of stroke.
Develop a medical decision support system for stroke with user interfaces.
To enable the future validation of suggested interventions a set of dedicated software interfaces will be provided. These are based on the “Digital Stroke Patient Platform”. However, since the interventions will be tailored for each of the stroke phases, a unique clinical decision support systems for each of the four phases will be developed. This will allow desiging the interfaces of the clinical decision support systems in a way that fits the unique healthcare setting of each phase. In order for the software to be usable in all clinical settings, the decision support systems will also be available as stand-alone versions.
Establish the EuropeanModelling Platform for Open Stroke Research (EUROPE-Stroke), an open research platform for data aggregation, integration, analysis and publication to promote science.
This objective plans and designs an Open Innovation organisation or virtual institute for stroke prevention and treatment. The aim is to fully exploit the PRECISE4Q data eco system integration and application scenarios, to support hospitals and physicians in stroke modelling and data-guided treatment and rehabilitation planning through various measures. These include educational tools and programmes, support for in-hospital machine learning process optimisation and a community platform for peer-to-peer exchange accessible to stroke specialists.
Identify and pursue commercial exploitation of scientific results by using generated knowledge and intellectual property.
PRECISE4Q not only proposes a cost-effective Digital Stroke Patient Platform. It also provides the methods, social science and economic expertise to formatively assess clinical effectiveness, lifestyle management integration, efficacy, and socio-economic cost-benefit ratio, as a quantitative foundation for initial but solid service and development and commercialisation strategies, plans for scaling up, and actual diffusion of the innovation capacity. Coupled with clinical assessments, after inputs from validation and paired with a deployment scenario based cost-benefit analysis covering a wide range of stakeholders and potential end-users, this objective will ensure the scalability and sustainability of PRECISE4Q working under real conditions, in real healthcare and social care settings. The project objectives are fully measurable, safeguarded through a clear and manageable innovation management work plan and have been designed in line with targeted exploitation plans, ensuring a realistic and sustainable impact of the project.