Stroke is one of the most severe medical problems with far-reaching public health and socio-economic impact, gathering momentum in an ageing society. Over the course of four years, PRECISE4Q set out to minimise the burden of stroke for the individual and for society. It created multi-dimensional, data-driven, predictive simulation computer models enabling – for the first time – personalised stroke treatment, addressing patient’s needs in four stages: prevention, acute treatment, rehabilitation and reintegration.

Objectives and main outputs

In a value-based pricing system, PRECISE4Q, after its implementation, generated massive reductions in direct and indirect health care costs as personalised prevention, treatment, rehabilitation programmes and reintegration lead to fewer cases of stroke, better procedures, better outcomes for patients and higher return to work rates, respectively. Learn more about the project’s goals and work plan below.

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 aimed 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 was created. The data warehouse was built after identification of all potential data source characteristics, user needs and data exchange needs. It adheres to all legal and technological standards allowing state-of-the-art date storage and exchange, a crucial prerequisite for the project. Furthermore, data was harmonized to allow pooling of similar data in heterogeneous data sources. Data harmonization efforts revolved 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 was used to structure clinical texts. Data harmonization and the development of models represented an iterative process organised in release cycles. To allow early development of models, the first release cycle of harmonized data comprised of structured data, whereas a later release provided the unstructured data that took 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 was highly cross-disciplinary and had to be performed prior to modelling, but iteratively in parallel to Objective 1. It entailed defining the inputs and outputs for the modelling phase. First, the most likely scenarios and use cases for the clinical phases of stroke were defined. Then, we identified rank and weighed input factors of stroke available in our databases in terms of both risk and resilience. The activity consisted of iterations of dialogue between clinicians and modellers. It also entailed 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) were present in the harmonized datasets. Defining the outputs for the models, it was crucial to understand how the model output would directly link to interventions in patient treatment and how the model would fit into the treatment processes within the care setting. Most of our models provided complex outputs. For some models, this added complexity in output was necessary because they explicitly take time scales into account. For other models, this added complexity was required to reflect a broader understanding of the factors contributing to resilience and well-being. A central motive of our study was to use a quality-of-life framework as a proxy for resilience and wellbeing. Our models predicted 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 were 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 was 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 were based on the harmonized data sources from objective 1 and the input/output definitions from objective 2. A major focus of this objective was 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 predicts the evolution of key patient features over in response to different treatments, such as diet, exercise, and medication in a hybrid modelling approach. It predicts the overall risk of stroke for a patient participating in a treatment plan. Using the outputs of this model a healthcare professional is 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 is fused with patient-specific clinical data to predict a modified Rankin Scale for the patient 3 months post-stroke. The second model integrates 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 is 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 creates a model that generates 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 supports doctors in designing personalised rehabilitation plans for each patient. The PRECISE4Q model supporting patient treatment during reintegration phase predicts a long-term (1-year post-stroke) complex QoL profile for a patient. The output of the model informs 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.

Validate the predictive models using data from prospective studies and to integrate the final models in a comprehensive platform.

The clinical data from the studies conducted was then used to validate models for stages 2, 3 and 4 that were developed using retrospective registry data in objective 3. Once the final models were established, they were integrated in a one-stop-shop web-based platform, coined the “Digital Stroke Patient Platform”. This platform is 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” was used to simulate patient courses. Additionally, the weighting of the input factors from the models was taken into account. In this first round, possible factors that can be the target for interventions were identified. Finally, these factors were assessed for clinical feasibility. The final output of this objective was 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 was provided. These were based on the “Digital Stroke Patient Platform”. However, since the interventions were tailored for each of the stroke phases, a unique clinical decision support system for each of the four phases was developed. This allowed us to design 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 have also been made available as stand-alone versions.

Establish the European Modelling Platform for Open Stroke Research (EUROPE-Stroke), an open research platform for data aggregation, integration, analysis and publication to promote science.

This objective planned and designed an Open Innovation organisation or virtual institute for stroke prevention and treatment. The aim was 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 offers a cost-effective Digital Stroke Patient Platform, but also provides the methods, social science and economic expertise to formatively assess clinical effectiveness. Furthermore, it offers lifestyle management integration, efficacy, and socio-economic cost-benefit ratio as a quantitative foundation for initial but solid service, development and commercialisation strategies, plans for scaling up, as well as actual diffusion of the innovation capacity. Coupled with clinical assessments, inputs from validation  paired with a deployment scenario based cost-benefit analysis covering a wide range of stakeholders and potential end-users, this objective ensures 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 innovation management work plan and had been designed in line with targeted exploitation plans, ensuring a realistic and sustainable impact of the project.

Work Packages

Lead: Institut Guttmann
Duration: M1-M48

Objectives achieved

  1. State of the art factors that influence stroke and related conditions regarding the four phases of the patient journey (1-Prevention, 2-Acute disease, 3-Rehabilitation, 4-Follow-up), Quality of Life (QoL) and their respective risk factors.
  2. Ranked unmet clinical needs regarding main stakeholders along the four patient journey phases.
  3. Identified scenarios and use cases with their inputs and outputs
  4. Specified end users’ requirements by means of dynamic and iterative feedback
  5. Devised a set of appropriate ethical guidelines to account for the specificities of data-driven research in personalized medicine and for multi-dimensional prediction
  6. Analyzed patients’ attitudes towards multi-dimensional prediction
  7. Assessed the ethical frameworks, and created a deliberative dashboard to discuss and resolve further ethical issues.

Lead: Charité
Duration: M1-M48

Objectives achieved

  1. Specified interfaces for data integration from partners and existing local data collections
  2. Created secure data exchange and integration systems
  3. Flexible and scalable data storage
  4. User and rights administration
  5. Maintained compliance with data protection provisions
  6. Designed interfaces of DSS for clinical application
  7. Assessed usability and acceptance of the DSS as minimal viable product in clinical settings

Lead: Medical University of Graz
Duration: M4-M48

Objectives achieved 

  1. Collected requirements for data integration, driven by machine learning / prediction use cases
  2. Constructed a thesaurus for seven languages covering all use cases
  3. Constructed a common, ontology-based data model, linked to the thesaurus, based on standards
  4. Developed extractors using shallow NLP for clinical texts in several languages
  5. Designed a semantic warehouse in cooperation with WP2

Lead: Dublin Institute of Technology
Duration: M1-M48

Objectives achieved

  1. Identified robust predictors for resilience and recovery from stroke
  2. Created a set of integrative multi-scale predictive models that together cover the life-stages of stroke treatment in a manner that enables health-care professionals to model individual patient trajectories and thereby design personalised interventions plans for stroke prevention, treatment, rehab, and post-rehab integration
  3. Validated the predictive capability of these models against data from state-of-the-art clinical and/or laboratorial studies and/or against large health registries
  4. Contributed to the design of a set of novel interventions for stroke prevention and treatment
  5. Developed an integrated open web-platform that supports stroke treatment and research

Lead: Charité
Duration: M1-M36

Objectives achieved

  1. Designed and conducted a clinical validation study for stroke treatment
  2. Designed and conducted a clinical validation study for rehabilitation/reintegration
  3. Developed an electronic patient-reported outcome concept
  4. Assessed usability and clinical integration of predictive models
  5. Developed and implemented a clinical decision support system

Lead: empirica
Duration: M1-M48

Objectives achieved

  1.  Modeled and simulated economic and social data, cost predictions and benefits of long-term care dependency, re-integration to work, and health care provider spending.
  2. Conceptualised, developed and tested an economic model to generate a planning and care management guidance tool for decision-makers, including prediction and evaluation.
  3. Strategically managed innovation output and exploitation with a clear focus of impacting the care delivery value chain for stroke.
  4. Developed healthcare, lifestyle, and well-being deployment scenarios and assessed benefits and costs for all PRECISE4Q application scenarios and related personalised interventions.

Lead: empirica
Duration: M1-M48

Objectives achieved

  1. Set up a project management structure that ensures efficient operational management according to the work plan, including administrative, financial and legal issues.
  2. Oversaw resources, procedures (e.g. cost control, quality control procedures on deliverables) and tools for ensuring that all results are delivered on time.
  3. Supported the external communication of project results and supported work dynamics to help drive the whole consortium as a team.
  4. Initiated a European open innovation and science initiative for stroke.

Lead: Charité
Duration: M1-M48

Objectives achieved

  1. Set up ‘ethics requirements’ that the project must comply with.
  2. Ensured project work was compliant with the established ‘ethics requirements’.