RESULTS

In the initial 18-month phase, our efforts were focused on three key areas. Firstly, we tackled clinical issues such as refining protocols and managing patient information. Secondly, we dedicated resources to enhancing our IT infrastructure, particularly focusing on platform architecture and bolstering privacy and security measures. Lastly, we conducted analytical research to develop a comprehensive understanding, including methodological reviews and analysis of historical data for insights. Our communication strategy included leveraging a website, social media platforms, and disseminating information through the QUALITOP newsletter.

During the following 18 months, our focus shifted to involving patients directly and gathering crucial clinical data along with quality-of-life questionnaires. We made sure to keep track of patients over time to monitor for any negative effects or side effects related to their treatment.

The Smart Medical Data Processing Platform (SMDPP) was built as an open-source system accessible online. It’s designed to improve healthcare by securely sharing and exchanging standardized patient and treatment data. The platform focuses on gathering, organizing, and analyzing diverse medical information, following the FAIR principles (making data Findable, Accessible, Interoperable, and Reusable). It also prioritizes security and privacy to ensure sensitive data remains protected.

We suggested a monitoring system architecture that enables data collection, quality checks, and data analysiswhile safeguarding sensitive information. Furthermore, we put into action and evaluated clustering algorithms on existing datasets. This helped us grasp how each algorithm works and enhance our ability to forecast patient paths in upcoming situations.

Our research into existing literature revealed a significant gap in knowledge regarding immunotherapy-related adverse effects (IR-AEs). This gap arises due to several factors, including limited follow-up duration in randomized controlled trials, infrequent occurrence of these events, and insufficient reporting of safety outcomes. This underscores the critical importance of the objectives of QUALITOP, which aim to go deeper into the causal relationships among immunotherapy, IR-AEs, and subsequent quality of life.

In the second phase, the QUALITOP partners worked on implementing a smart digital platform. They paid special attention to aspects like medical interoperability language, security, privacy mechanisms, and legal considerations. Additionally, they developed methodologies to tackle issues such as causal inference, analyzing diverse data types, and creating simulation models to predict policy effects and improve resource allocation. Efforts were also made to facilitate data transfer between different centres to kickstart data analysis.

QUALITOP is the first research endeavour aiming to provide detailed information on the impact of immunotherapy on QoL (during and after treatment) by incorporating a wide range of medical and psychosocial data and employing both qualitative and quantitative approaches. The QUALITOP cohort is the first European cohort dedicated to assessing and predicting QoL during and after immunotherapy.

Below you will find progress on each work packages:

WP2 Clinical Results

Melanoma, a type of skin cancer, has been on the rise, with an annual increase of 2 to 3%. Early-stage treatment typically involves surgery, while advanced cases require targeted therapies and immunotherapy, which have improved survival rates. However, these treatments can cause significant side effects, especially immune-related issues linked to immune checkpoint inhibitors (ICIs), affecting different parts of the body. Hence, it’s vital to consider patients’ quality of life alongside treatment effectiveness and safety. Various questionnaires, like the Functional Assessment of Cancer Therapy-General (FACT-G), help assess this aspect comprehensively.

Our study found no notable differences between patient groups based on cancer stage, presence of other cancers, or type of drug used. However, women reported lower functional well-being compared to men, especially in the functional well-being aspect. Monitoring quality of life during treatment is crucial to understand the impact of side effects. Future analyses will look into quality of life at different stages of treatment and recovery. Real-world studies are also needed to fully grasp patients’ quality of life.

WP3 Qualitop Psychosocial determinants results

WP3 has conducted a thorough review of existing literature on cancer patients’ quality of life, with a focus on those undergoing immunotherapy treatment. This review helped us identify a range of psychosocial factors that may influence the quality of life of patients participating in QUALITOP. Collaborative meetings with QUALITOP partners, including clinicians from WP2 and the data analysis team from WP5, allowed us to agree upon a minimum set of data required for each patient in QUALITOP. This set includes both medical and psychosocial information.

Questionnaires were completed by all QUALITOP patients in France, Portugal, Spain, and the Netherlands as part of a prospective study. Patients filled out these questionnaires online at the start of immunotherapy and at months 3, 6, 12, and 18 afterwards. The questionnaires collected various types of data, including sociodemographic information (like gender, age, and marital status), details about gender roles, health status, family history of cancer, quality of life, anxiety/depression levels, tolerance to uncertainty, social support, health literacy, beliefs and behaviours related to medication, relationship with their main physician, and expectations toward immunotherapy.

WP 4 Qaulitop Platform and Shared Data Lake

As part of our work in package 4, we’ve set up a framework for a data lake. This serves as a central hub where data from various sources are harmonized, organized, and analyzed. The data lake directly accesses different data sources using a shared ontology, which means we don’t need to store all data in one place. Instead, we can analyze data directly from where it’s stored.

Our framework is designed to handle all types of data, no matter their structure or format. This means we can easily bring together data from different sources for analysis, giving us a more comprehensive view. Additionally, we’ve developed Machine Learning models that use the data lake. Stakeholders can interact with these models using our Domain-Specific Language (MiLA), which allows them to explore the data and use the model’s results. MiLA helps stakeholders gain insights and make data-driven decisions more effectively.

WP 5 Qualitop determinants of QoL and its relationships with IR-AEs results

WP5 have received data that was collected for QUALITOP from Spain, Portugal, France and Netherlands. Our aim has been to combine the databases from all four countries and describe patients’ psychosocial behaviours after receiving immunotherapy for their cancer. We have also been exploring the psychosocial determinants of patients’ health-related quality of life at baseline (when they receive immunotherapy), which is measured by their overall FACT-G (Functional Assessment of Cancer Therapy – General) score.

WP7 Recommendations for improved Quality of life

Immunotherapy has revolutionized cancer treatment, but while we have clear clinical measures of its success, we’ve paid less attention to how it affects patients’ overall well-being, as captured by their health-related quality of life (HRQoL). Since a patient’s HRQoL is influenced by many factors in a complex system, we’ve developed a systems-level model to better understand this complexity. Using existing research, we’ve constructed a causal loop diagram (CLD) that illustrates how patients’ well-being is impacted at different levels: individual, immediate social circle, and community. By identifying feedback loops in the CLD, we can see how changing one variable can affect the entire system. This diagram helps us pinpoint areas where more research is needed and serves as a valuable communication tool for everyone involved in patient care. It also lays the groundwork for a simulation modelthat can predict the effects of different policies and help allocate resources more effectively. Although we currently lack enough quality-of-life data for patients undergoing immunotherapy, ongoing research efforts should provide the data needed to develop this simulation model soon.