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Objective: Hospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric 'Proof of Principle' study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources. Materials and methods: Two quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data. Results: ETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted. Discussion and conclusion: PHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures. [ABSTRACT FROM AUTHOR]

Objective: To investigate associations between quality of life (QoL) and 1) immunotherapy and other cancer treatments received three months before QoL measurements, and 2) the comorbidities at the time of completion or in the year prior to QoL measurements, among patients with advanced cancer. Methods: A cross-sectional study is conducted on patients with advanced cancer in the Netherlands. The data come from the baseline wave of the 2017–2020 eQuiPe study. Participants were surveyed via questionnaires (including EORTC QLQ-C30). Using multivariable linear and logistic regression models, we explored statistical associations between QoL components and immunotherapy and other cancer treatments as well as pre-existing comorbidities while adjusting for age, sex, socio-economic status. Results: Of 1088 participants with median age 67 years, 51% were men. Immunotherapy was not associated with global QoL but was associated with reduced appetite loss (odds ratio (OR) = 0.6, 95%CI = [0.3,0.9]). Reduced global QoL was associated with chemotherapy (adjusted mean difference (β) = − 4.7, 95% CI [− 8.5,− 0.8]), back pain (β = − 7.4, 95% CI [− 11.0,− 3.8]), depression (β = − 13.8, 95% CI [− 21.5,− 6.2]), thyroid diseases (β = − 8.9, 95% CI [− 14.0,− 3.8]) and diabetes (β = − 4.5, 95% CI [− 8.9,− 0.5]). Chemotherapy was associated with lower physical (OR = 2.4, 95% CI [1.5,3.9]) and role (OR = 1.8, 95% CI [1.2,2.7]) functioning, and higher pain (OR = 1.9, 95% CI [1.3,2.9]) and fatigue (OR = 1.6, 95% CI [1.1,2.4]). Conclusion: Our study identified associations between specific cancer treatments, lower QoL and more symptoms. Monitoring symptoms may improve QoL of patients with advanced cancer. Producing more evidence from real life data would help physicians in better identifying patients who require additional supportive care. [ABSTRACT FROM AUTHOR]

The registration of multi-source radiation oncology data is a time-consuming and labour-intensive procedure. The standardisation of data collection offers the possibility for the acquisition of quality data for research and clinical purposes. With this study, we present an overview of the different tumour group data lists in the Dutch national proton therapy registry. Furthermore, as a representative example of the workings of these different tumour-specific knowledge graphs, we present the FAIR (Findable, Accessible, Interoperable, Reusable) data principles-compliant knowledge graph approach describing the head and neck tumour variables using radiotherapy domain ontologies and semantic web technologies. Our goal is to provide the radiotherapy community with a flexible and interoperable data model for data exchange between centres. We highlight data variables that are needed for models used in the model-based approach (MBA), which ensures a fair selection of patients that will benefit most from proton therapy. [ABSTRACT FROM AUTHOR]

BACKGROUND: There is growing evidence that exposure to ultrafine particles (UFP; particles smaller than 100 nm) may play an underexplored role in the etiology of several illnesses, including cardiovascular disease (CVD). OBJECTIVES: We aimed o investigate the relationship between long-term exposure to ambient UFP and incident cardiovascular and cerebrovascular disease (CVA). As a secondary objective, we sought to compare effect estimates for UFP with those derived for other air pollutants, including estimates from two-pollutant models. METHODS: Using a prospective cohort of 33,831 Dutch residents, we studied the association between long-term exposure to UFP (predicted via land use regression) and incident disease using Cox proportional hazard models. Hazard ratios (HR) for UFP were compared to HRs for more routinely monitored air pollutants, including particulate matter with aerodynamic diameter =10 lm (PM10), PM with aerodynamic diameter =2:5 (PM2:5), and NO2. RESULTS: Long-term UFP exposure was associated with an increased risk for all incident CVD [HR=1:18 per 10,000 particles=cm3; 95% confidence interval (CI): 1.03, 1.34], myocardial infarction (MI) (HR=1:34; 95% CI: 1.00, 1.79), and heart failure (HR=1:76; 95% CI: 1.17, 2.66). Positive associations were also estimated for NO2 (HR for heart failure=1:22; 95% CI: 1.01, 1.48 per 20 lg=m3) and coarse PM (PMcoarse; HR for all CVD=1:21; 95% CI: 1.01, 1.45 per 10 lg=m3). CVD was not positively associated with PM2:5 (HR for all CVD=0:95; 95% CI: 0.75, 1.28 per 5 lg=m3). HRs for UFP and CVAs were positive, but not significant. In two-pollutant models (UFP+NO2 and UFP+PMcoarse), positive associations tended to remain for UFP, while HRs for PMcoarse and NO2 generally attenuated towards the null. CONCLUSIONS: These findings strengthen the evidence that UFP exposure plays an important role in cardiovascular health and that risks of ambient air pollution may have been underestimated based on conventional air pollution metrics. [ABSTRACT FROM AUTHOR]

Background and Purpose: Predicting outcomes is challenging in rare cancers. Single-institutional datasets are often small and multi-institutional data sharing is complex. Distributed learning allows machine learning models to use data from multiple institutions without exchanging individual patient-level data. We demonstrate this technique in a proof-of-concept study of anal cancer patients treated with chemoradiotherapy across multiple European countries., Materials and Methods: atomCAT is a three-centre collaboration between Leeds Cancer Centre (UK), MAASTRO Clinic (The Netherlands) and Oslo University Hospital (Norway). We trained and validated a Cox proportional hazards regression model in a distributed fashion using data from 281 patients treated with radical, conformal chemoradiotherapy for anal cancer in three institutions. Our primary endpoint was overall survival. We selected disease stage, sex, age, primary tumour size, and planned radiotherapy dose (in EQD2) a priori as predictor variables., Results: The Cox regression model trained across all three centres found worse overall survival for high risk disease stage (HR = 2.02), male sex (HR = 3.06), older age (HR = 1.33 per 10 years), larger primary tumour volume (HR = 1.05 per 10 cm 3 ) and lower radiotherapy dose (HR = 1.20 per 5 Gy). A mean concordance index of 0.72 was achieved during validation, with limited variation between centres (Leeds = 0.72, MAASTRO = 0.74, Oslo = 0.70). The global model performed well for risk stratification for two out of three centres., Conclusions: Using distributed learning, we accessed and analysed one of the largest available multi-institutional cohorts of anal cancer patients treated with modern radiotherapy techniques. This demonstrates the value of distributed learning in outcome modelling for rare cancers., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)