Periodic Reporting for period 3 - QUALITOP (Monitoring multidimensional aspects of QUAlity of Life after cancer ImmunoTherapy - an Open smart digital Platform for personalized prevention and patient management)
Reporting period: 2023-01-01 to 2024-06-30
Cancer immunotherapy brought about significant progress in cancer treatment, resulting in high efficacy in some cancers. Nevertheless, two main challenges impeded further improvement of cancer patients’ health status and quality of life (QoL) after immunotherapy initiation: (1) the crucial need for predictive markers of immunotherapy-related adverse events (IR-AEs) to anticipate and improve patients’ health status and promote their QoL; and (2) the lack of knowledge on patients after the start of immunotherapy outside randomized controlled trials. To address these challenges, more diversified sources of data were required.
The QUALITOP project aimed to develop an open Smart Digital Platform, leveraging big data analysis, artificial intelligence, and simulation modelling approaches. This enabled the efficient collection and aggregation of real-world data to monitor the health status and QoL of cancer patients who received immunotherapy. Through causal inference analyses, QUALITOP identified determinants of health status regarding IR-AEs and defined patient profiles in a real-world context. Heterogeneous data sources—both retrospective and prospective—were collected from clinical centres in four EU countries. Using machine learning approaches, QUALITOP provided real-time recommendations based on patient profiles and feedback via the Platform. Furthermore, increased visibility of patient behaviour, improved IR-AEs prediction, and enhanced care coordination supported the analysis of cost-effectiveness through simulation modelling. Guidelines were issued for both the short and long term.
Conclusions: The QUALITOP project successfully developed and validated a proof-of-concept tool for the secure collection, integration, and analysis of heterogeneous medical data related to cancer immunotherapy. The project included 2,930 patients (2,047 historical and 883 prospective), and enabled the identification of key determinants of immunotherapy-related adverse events and quality of life outcomes. The tool facilitated real-time recommendations and improved care coordination, demonstrating the feasibility and value of integrating big data and AI in clinical practice. The project’s findings are expected to influence policy-making and clinical guidelines for immunotherapy across Europe, promote standardized care practices, and support patients’ return to work.
The QUALITOP project aimed to develop an open Smart Digital Platform, leveraging big data analysis, artificial intelligence, and simulation modelling approaches. This enabled the efficient collection and aggregation of real-world data to monitor the health status and QoL of cancer patients who received immunotherapy. Through causal inference analyses, QUALITOP identified determinants of health status regarding IR-AEs and defined patient profiles in a real-world context. Heterogeneous data sources—both retrospective and prospective—were collected from clinical centres in four EU countries. Using machine learning approaches, QUALITOP provided real-time recommendations based on patient profiles and feedback via the Platform. Furthermore, increased visibility of patient behaviour, improved IR-AEs prediction, and enhanced care coordination supported the analysis of cost-effectiveness through simulation modelling. Guidelines were issued for both the short and long term.
Conclusions: The QUALITOP project successfully developed and validated a proof-of-concept tool for the secure collection, integration, and analysis of heterogeneous medical data related to cancer immunotherapy. The project included 2,930 patients (2,047 historical and 883 prospective), and enabled the identification of key determinants of immunotherapy-related adverse events and quality of life outcomes. The tool facilitated real-time recommendations and improved care coordination, demonstrating the feasibility and value of integrating big data and AI in clinical practice. The project’s findings are expected to influence policy-making and clinical guidelines for immunotherapy across Europe, promote standardized care practices, and support patients’ return to work.
The first period was dedicated to clinical issues (protocol, patients’ information), IT developments (platform architecture, privacy and security) and analytical researches (methodological overview, analyses of prolective historical data).
The second period was dedicated to the inclusion of patients and the collection of clinical data and quality of life questionnaires (QLQ-C30 and EQ-5D).
The architecture of the Smart Medical Data Processing Platform (SMDPP) was designed as an open-source platform with Web-presence that provides the framework to deliver better quality of care on the basis of trusted and secure sharing and exchange of normalized patient and therapy data collected. Emphasis was placed on architectural principles to acquire, manage, and processes disparate heterogeneous medical Big Data based on FAIR (Findable, Accessible, Interoperable, Reusable) principles while ensuring security & privacy.
Working on historical databases enable the QUALITOP partners to study the adverse events experienced by the patients according to the treatments received. Adverse effects and comorbidities during and after treatment were also evaluated. Based on a collaboration with the eQuiPe study research team in the Netherlands (data linked to the Netherlands Cancer Registry (NCR)), and on the French ImmuCare data, some analyses explored the timing and the determinants of IR-AEs. Self-reported components of quality of life differed among patients who received immunotherapy were also explored.
The IT teams developed a Smart Digital Platform that enables networked medical agencies to securely share and exchange of medical data. This platform integrates data from patient records, immunotherapy, clinical examinations, treatment, images, and psychosocial data. It converts this data into smart data to support informed recommendations through monitoring and data analysis.
The second period was dedicated to the inclusion of patients and the collection of clinical data and quality of life questionnaires (QLQ-C30 and EQ-5D).
The architecture of the Smart Medical Data Processing Platform (SMDPP) was designed as an open-source platform with Web-presence that provides the framework to deliver better quality of care on the basis of trusted and secure sharing and exchange of normalized patient and therapy data collected. Emphasis was placed on architectural principles to acquire, manage, and processes disparate heterogeneous medical Big Data based on FAIR (Findable, Accessible, Interoperable, Reusable) principles while ensuring security & privacy.
Working on historical databases enable the QUALITOP partners to study the adverse events experienced by the patients according to the treatments received. Adverse effects and comorbidities during and after treatment were also evaluated. Based on a collaboration with the eQuiPe study research team in the Netherlands (data linked to the Netherlands Cancer Registry (NCR)), and on the French ImmuCare data, some analyses explored the timing and the determinants of IR-AEs. Self-reported components of quality of life differed among patients who received immunotherapy were also explored.
The IT teams developed a Smart Digital Platform that enables networked medical agencies to securely share and exchange of medical data. This platform integrates data from patient records, immunotherapy, clinical examinations, treatment, images, and psychosocial data. It converts this data into smart data to support informed recommendations through monitoring and data analysis.
An important result so far is the development of a proof-of-concept tool for securely managing and sharing distributed data, and a set of data analysis tools. The data used are those from the hospitals involved in this project, collected from patients. Users of the platform authenticate themselves, and an access control mechanism restricts access to only data authorized for a given user. The PoC also includes an AI companion for expressing queries in natural language and then producing formal queries that is used by the system to synthesize answers from data sources.
Through a comprehensive analysis of diverse medical and psychosocial data using both qualitative and quantitative methods, QUALITOP intends to develop personalized treatment approaches tailored to individual patient needs. This approach not only aims to improve treatment adherence and outcomes but also seeks to enhance overall patient care and satisfaction.
Health data are increasingly collected and stored in warehouses, requiring cumbersome administrative procedures before they can be used in projects. On the other hand, the lack of interoperability between data and the systems that manage them does not facilitate their joint exploitation. The Qualitop platform, based on the Polystore approach, offers a declarative and secure approach for integrating heterogeneous data sources. This approach could open up new prospects for large-scale data sharing of sensitive data. Moreover, the development of the platform provided an excellent opportunity to explore new techniques to bridge the gap between data management and data analysis, coupled with machine learning.
Our project has developed qualitative and quantitative models that provide a systems perspective on the quality of life for cancer patients undergoing immunotherapy. These models help stakeholders understand the complex interactions between various factors affecting patient well-being and suggest ways to improve it. By offering insights into the dynamics of these factors, the interactions can be better understood, aiding policymakers in making informed decisions about cost-effective strategies. Overall, this systems approach promotes a better understanding for improved patient support and potential enhancements in healthcare.
The project's findings are expected to influence policy-making and clinical guidelines for immunotherapy across Europe, promoting standardized care practices. Additionally, by examining factors influencing patients' return to work and integrating these insights, QUALITOP aims to support initiatives that facilitate patients in resuming their professional lives, benefiting both individuals and the economy.
Dissemination of QUALITOP's findings through various channels, including publications, conferences, workshops, and public engagement activities, will raise awareness and educate stakeholders about the challenges and successes of immunotherapy.
Through a comprehensive analysis of diverse medical and psychosocial data using both qualitative and quantitative methods, QUALITOP intends to develop personalized treatment approaches tailored to individual patient needs. This approach not only aims to improve treatment adherence and outcomes but also seeks to enhance overall patient care and satisfaction.
Health data are increasingly collected and stored in warehouses, requiring cumbersome administrative procedures before they can be used in projects. On the other hand, the lack of interoperability between data and the systems that manage them does not facilitate their joint exploitation. The Qualitop platform, based on the Polystore approach, offers a declarative and secure approach for integrating heterogeneous data sources. This approach could open up new prospects for large-scale data sharing of sensitive data. Moreover, the development of the platform provided an excellent opportunity to explore new techniques to bridge the gap between data management and data analysis, coupled with machine learning.
Our project has developed qualitative and quantitative models that provide a systems perspective on the quality of life for cancer patients undergoing immunotherapy. These models help stakeholders understand the complex interactions between various factors affecting patient well-being and suggest ways to improve it. By offering insights into the dynamics of these factors, the interactions can be better understood, aiding policymakers in making informed decisions about cost-effective strategies. Overall, this systems approach promotes a better understanding for improved patient support and potential enhancements in healthcare.
The project's findings are expected to influence policy-making and clinical guidelines for immunotherapy across Europe, promoting standardized care practices. Additionally, by examining factors influencing patients' return to work and integrating these insights, QUALITOP aims to support initiatives that facilitate patients in resuming their professional lives, benefiting both individuals and the economy.
Dissemination of QUALITOP's findings through various channels, including publications, conferences, workshops, and public engagement activities, will raise awareness and educate stakeholders about the challenges and successes of immunotherapy.