Periodic Reporting for period 1 - STRATIFYHF (Artificial intelligence-based decision support system for risk stratification and early detection of heart failure in primary and secondary care)
Periodo di rendicontazione: 2023-06-01 al 2024-11-30
The STRATIFYHF project addresses the pressing global challenge of heart failure (HF), which affects an estimated 26 million people worldwide, including 15 million in Europe. HF is a complex, chronic condition associated with impaired heart function, high healthcare costs, and poor quality of life. Current approaches to risk stratification and early diagnosis in clinical settings are limited, often leading to delayed or inaccurate diagnoses, missed treatment opportunities, and unnecessary healthcare expenditures.
The motivation behind STRATIFYHF stems from the urgent need for innovative, cost-effective tools to enable early detection, risk stratification, and disease progression prediction in HF. Traditional diagnostic methods, such as physical examinations, blood tests, and echocardiography, are not only time-consuming but also rely heavily on individual practitioners’ expertise, leading to variability in outcomes. Moreover, the absence of AI-driven decision support tools in primary and secondary care has been identified as a critical barrier to improving HF diagnosis and management.
STRATIFYHF proposes a transformative solution in the form of an AI-based Decision Support System (DSS). The system will integrate patient-specific data—demographics, clinical records, genetics, lifestyle factors, and socio-economic conditions—with state-of-the-art AI algorithms and computational modeling. This integration will facilitate three core modules: risk stratification, diagnosis, and disease progression prediction. By using existing and novel technologies, including voice biomarkers and the CORS test, STRATIFYHF aims to enhance the accuracy and efficiency of HF management at both primary and secondary care levels.
The expected impacts of STRATIFYHF are significant and multifaceted:
1. For Patients:
- Earlier and more accurate diagnoses will lead to timely initiation of treatment, improving survival rates and quality of life.
- Patients will gain access to a mobile app that empowers them to monitor their health, manage risks, and adhere to preventive strategies, reducing psychological and economic burdens.
2. For Healthcare Providers:
- Clinicians will have access to robust, validated AI tools to assist in decision-making, reducing the reliance on subjective judgment.
- The DSS will streamline the diagnostic pathway, minimizing unnecessary referrals and optimizing the usage of healthcare resources.
3. For Healthcare Systems:
- The digitalization of HF care pathways will reduce healthcare costs by decreasing hospitalizations, shortening waiting times, and mitigating diagnostic errors.
- STRATIFYHF will alleviate the burden on secondary care by enabling more efficient resource allocation.
The project also aligns strategically with European healthcare goals, addressing major barriers such as limited access to diagnostic tools, under utilization of AI in clinical settings, and a growing HF burden due to aging populations and rising comorbidities. The initiative will advance the field of medical AI, set new standards for HF management, and foster cross-disciplinary collaboration.
STRATIFYHF will establish a replicable model for integrating AI into healthcare, demonstrating how technology can revolutionize chronic disease management. Its potential to reduce HF-related mortality and improve care quality positions STRATIFYHF as a transformative project with a substantial impact on patients, practitioners, and global healthcare systems.
The motivation behind STRATIFYHF stems from the urgent need for innovative, cost-effective tools to enable early detection, risk stratification, and disease progression prediction in HF. Traditional diagnostic methods, such as physical examinations, blood tests, and echocardiography, are not only time-consuming but also rely heavily on individual practitioners’ expertise, leading to variability in outcomes. Moreover, the absence of AI-driven decision support tools in primary and secondary care has been identified as a critical barrier to improving HF diagnosis and management.
STRATIFYHF proposes a transformative solution in the form of an AI-based Decision Support System (DSS). The system will integrate patient-specific data—demographics, clinical records, genetics, lifestyle factors, and socio-economic conditions—with state-of-the-art AI algorithms and computational modeling. This integration will facilitate three core modules: risk stratification, diagnosis, and disease progression prediction. By using existing and novel technologies, including voice biomarkers and the CORS test, STRATIFYHF aims to enhance the accuracy and efficiency of HF management at both primary and secondary care levels.
The expected impacts of STRATIFYHF are significant and multifaceted:
1. For Patients:
- Earlier and more accurate diagnoses will lead to timely initiation of treatment, improving survival rates and quality of life.
- Patients will gain access to a mobile app that empowers them to monitor their health, manage risks, and adhere to preventive strategies, reducing psychological and economic burdens.
2. For Healthcare Providers:
- Clinicians will have access to robust, validated AI tools to assist in decision-making, reducing the reliance on subjective judgment.
- The DSS will streamline the diagnostic pathway, minimizing unnecessary referrals and optimizing the usage of healthcare resources.
3. For Healthcare Systems:
- The digitalization of HF care pathways will reduce healthcare costs by decreasing hospitalizations, shortening waiting times, and mitigating diagnostic errors.
- STRATIFYHF will alleviate the burden on secondary care by enabling more efficient resource allocation.
The project also aligns strategically with European healthcare goals, addressing major barriers such as limited access to diagnostic tools, under utilization of AI in clinical settings, and a growing HF burden due to aging populations and rising comorbidities. The initiative will advance the field of medical AI, set new standards for HF management, and foster cross-disciplinary collaboration.
STRATIFYHF will establish a replicable model for integrating AI into healthcare, demonstrating how technology can revolutionize chronic disease management. Its potential to reduce HF-related mortality and improve care quality positions STRATIFYHF as a transformative project with a substantial impact on patients, practitioners, and global healthcare systems.
In WP1 (Clinical study to collect, integrate and validate data) clinical partners have completed retrospective phase of the study and have initiated implementation of the prospective multicentre clinical study according to the adopted and approved research study protocol.
In WP2 (Digital patient library, AI-driven algorithms and analytics) patient-specific database and fusion of patient data from heterogeneous data sources have been established. In WP3 (Development of STRATIFYHF decision support system-DSS) description of the details has been performed in relation to:
(1) the voice processing workflow,
(2) conceptualization of the development, and architecture of the DSS and
(3) blueprint of the web-design of the DSS.
In WP4 (Development of STRATIFYHF mobile app) user requirements and analysis have been performed and the core architectural components defined.
In WP5 (Clinical study delivery and regulatory development), the regulatory classification roadmap for the DSS and Mobile app have been completed. Support for clinical evaluation, plans for DSS implementation and mobile app development have been provided.
In WP6 (Health economics and cost-effectiveness analysis), adescription of the current standard of care for heart failure and an evaluation of its burden on EU healthcare systems is ongoing.
In WP2 (Digital patient library, AI-driven algorithms and analytics) patient-specific database and fusion of patient data from heterogeneous data sources have been established. In WP3 (Development of STRATIFYHF decision support system-DSS) description of the details has been performed in relation to:
(1) the voice processing workflow,
(2) conceptualization of the development, and architecture of the DSS and
(3) blueprint of the web-design of the DSS.
In WP4 (Development of STRATIFYHF mobile app) user requirements and analysis have been performed and the core architectural components defined.
In WP5 (Clinical study delivery and regulatory development), the regulatory classification roadmap for the DSS and Mobile app have been completed. Support for clinical evaluation, plans for DSS implementation and mobile app development have been provided.
In WP6 (Health economics and cost-effectiveness analysis), adescription of the current standard of care for heart failure and an evaluation of its burden on EU healthcare systems is ongoing.
The STRATIFYHF project will integrate three core modules: risk stratification, diagnosis, and disease progression prediction, using AI and computational modeling, that will improve the accuracy and efficiency of HF management at both primary and secondary care levels.
Key Exploitable results are software as a medical device: Clinical Decision Support System and Mobile App for monitoring and risk stratification. Results beyond state of the art are:
• Voice biomarkers and the CORS test, to be used in clinical practice
• AI-Enhanced Risk Stratification where we introduce an advanced machine-learning model trained on a diverse, real-world dataset, integrating clinical, imaging, biomarker, and wearable data.
Targeted end-users are the primary and secondary care general practitioners and cardiologists, who will use a scalable model tailored for both segments. Implementation of real-time monitoring using wearable sensors, facilitating early interventions, and reducing hospitalizations are additional values for real-time predictive analytics for HF.
Key Exploitable results are software as a medical device: Clinical Decision Support System and Mobile App for monitoring and risk stratification. Results beyond state of the art are:
• Voice biomarkers and the CORS test, to be used in clinical practice
• AI-Enhanced Risk Stratification where we introduce an advanced machine-learning model trained on a diverse, real-world dataset, integrating clinical, imaging, biomarker, and wearable data.
Targeted end-users are the primary and secondary care general practitioners and cardiologists, who will use a scalable model tailored for both segments. Implementation of real-time monitoring using wearable sensors, facilitating early interventions, and reducing hospitalizations are additional values for real-time predictive analytics for HF.