Luana Caselli

BACKGROUND Cancer remains a leading cause of morbidity and mortality worldwide. Given its substantial burden, quality of life has become a key outcome in cancer care and research. Patient-reported outcome measures (PROMs) are commonly used to assess quality of life. Although qualitative research is essential for establishing PROM content validity, patient narratives are less often collected and analysed systematically once PROMs are implemented. Adding open-ended responses to quantitative PROM data may provide policy-relevant insights into what patients themselves prioritise. OBJECTIVE This study has aimed to identify the factors that people living with or beyond cancer across Europe perceive as having the greatest impact on their quality of life. METHODS Following a pan-European validation study of the newly developed EUonQoL-Kit – a set of questionnaires designed to assess the quality of life of people living with or beyond cancer in Europe – the responses to the final open-ended question “Having completed the questionnaire, what do you feel most impacts your quality of life?” were collected. Responses underwent qualitative thematic analysis using a coding framework iteratively developed and interpreted by researchers and people with lived experience of cancer involved as ‘co-researchers’. RESULTS Of the 4,284 cancer patients and survivors participating in the EUonQoL-Kit validation study, 3,350 (78.2%) provided a response to the final open-ended question. Factors perceived as having the greatest impact on quality of life were categorised into 23 distinct themes, covering six overarching domains: Physical health, Psychological wellbeing, Social health, Overall health, Healthcare experience and Environment. The most frequently reported factors included physical symptoms, overall health, relationships and connectivity, emotions and feelings, and impact of care pathway. CONCLUSIONS This study provides a comprehensive overview of the factors that impact quality of life most in people living with or beyond cancer across Europe. Combining structured PROMs with open-ended patient input may support more patient-centred quality of life measurement and interpretation and help align care and policy priorities with what matters most to patients.

Objectives: To estimate the incidence of healthcare-associated infections (HAIs) in Italian long-term care facilities (LTCFs) and to evaluate whether an artificial intelligence (AI) approach, through unsupervised machine learning (ML), could stratify residents into clinically distinct groups with differing susceptibility to HAIs. Design: Prospective cohort study with 12-month follow-up. Setting: 24 LTCFs in Italy, participating in the European Centre for Disease Prevention and Control 12-month longitudinal study on HAIs in LTCFs, 2022–2023. Participants: 395 residents enrolled across the participating LTCFs. Methods: Incidence measures of HAIs (rate and ratio) were estimated, using generalized estimating equations. A hierarchical cluster analysis based on residents’ clinical and demographic characteristics was implemented as an unsupervised ML approach. Results: Overall, 75 HAIs per 100 residents (95% CI, 70.3–78.3) and 0.23 HAIs per 1,000 resident-days (95% CI, 0.11–0.76) were estimated. Respiratory tract infections (29.5%, 95% CI 24.2–31.1), COVID-19 (26.3%, 95% CI 22.1–28.4), and urinary tract infections (15%, 95% CI 11.0–35.4) were the most frequent. Clustering identified two reproducible resident groups: Group 1 (39%), more independent and cognitively preserved, with fewer comorbidities and lower infection incidence; and Group 2 (61%), more dependent and clinically complex, with higher incidence of HAIs. Cluster stability was high (mean ARI = 0.83). Conclusions: This study confirms the high burden of HAIs in Italian LTCFs and provides exploratory evidence that AI-based clustering can identify reproducible HAI susceptibility profiles in a setting where such approaches have been scarcely applied.

PurposeThe European Oncology Quality of Life (EUonQoL) project aims to develop a questionnaire toolkit (EUonQoL-Kit) to assess the quality of life (QoL) of cancer patients and survivors across Europe.MethodsThe EUonQoL-Kit development used mixed-methods and a co-design approach. Data was collected in six countries (Denmark, France, Germany, Italy, Netherlands and UK). The target populations were patients in active treatment (A), survivors (B) and patients requiring palliative care (C). A review of existing QoL theoretical models produced an initial EUonQoL conceptual framework. Semi-structured interviews and a Delphi survey evaluated/modified the framework. Existing validated items were used to construct the toolkit, including Computer Adaptive Testing (CAT), where available. A usability study evaluated EUonQoL-Kit.v1. Data triangulation and consensus methodology guided EUonQoL-Kit.v2.ResultsThe initial conceptual framework covered four multi-dimensional domains: physical, social and overall health, and psychological wellbeing. The interviews and Delphi survey included 75 and 155 participants, respectively. The domain ‘healthcare experience’ was identified and included in the framework. EUonQoL-Kit.v1 resulted in three static questionnaires, one for each target population (n items- A=75; B=67; C=79). Following usability testing with 53 participants, EUonQoL-Kit.v2 was produced via a multi-stakeholder consensus development panel, creating a shortened version (n items- A=50; B=50; C=44). Dynamic versions of these questionnaires were developed using the EORTC CAT Core system.ConclusionsEUonQoL-Kit is a novel toolkit developed to assess QoL across the cancer continuum and inform health policy within Europe. Its psychometric properties are currently being evaluated using data collected on more than 4200 patients across 32 countries.