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Background: Little evidence is available on the long-term health-care utilisation of older patients with cancer and whether this is associated with geriatric screening results. We aimed to evaluate long-term health-care utilisation among older patients after cancer diagnosis and the association with baseline Geriatric 8 (G8) screening results., Methods: For this retrospective analysis, we included data from three cohort studies for patients (aged ≥70 years) with a new cancer diagnosis who underwent G8 screening between Oct 19, 2009 and Feb 27, 2015, and who survived more than 3 months after G8 screening. The clinical data were linked to cancer registry and health-care reimbursement data for long-term follow-up. The occurrence of outcomes (inpatient hospital admissions, emergency department visits, use of intensive care, contacts with general practitioner [GP], contacts with a specialist, use of home care, and nursing home admissions) was assessed in the 3 years after G8 screening. We assessed the association between outcomes and baseline G8 score (normal score [>14] or abnormal [≤14]) using adjusted rate ratios (aRRs) calculated from Poisson regression and using cumulative incidence calculated as a time-to-event analysis with the Kaplan-Meier method., Findings: 7556 patients had a new cancer diagnosis, of whom 6391 patients (median age 77 years [IQR 74-82]) met inclusion criteria and were included. 4110 (64·3%) of 6391 patients had an abnormal baseline G8 score (≤14 of 17 points). In the first 3 months after G8 screening, health-care utilisation peaked and then decreased over time, with the exception of GP contacts and home care days, which remained high throughout the 3-year follow-up period. Compared with patients with a normal baseline G8 score, patients with an abnormal baseline G8 score had more hospital admissions (aRR 1·20 [95% CI 1·15-1·25]; p<0·0001), hospital days (1·66 [1·64-1·68]; p<0·0001), emergency department visits (1·42 [1·34-1·52]; p<0·0001), intensive care days (1·49 [1·39-1·60]; p<0·0001), general practitioner contacts (1·19 [1·17-1·20]; p<0·0001), home care days (1·59 [1·58-1·60]; p<0·0001), and nursing home admissions (16·7% vs 3·1%; p<0·0001) in the 3-year follow-up period. At 3 years, of the 2281 patients with a normal baseline G8 score, 1421 (62·3%) continued to live at home independently and 503 (22·0%) had died. Of the 4110 patients with an abnormal baseline G8 score, 1057 (25·7%) continued to live at home independently and 2191 (53·3%) had died., Interpretation: An abnormal G8 score at cancer diagnosis was associated with increased health-care utilisation in the subsequent 3 years among patients who survived longer than 3 months., Funding: Stand up to Cancer, the Flemish Cancer Society., Competing Interests: Declaration of interests LD reports a research grant (via their institution) from Boehringer Ingelheim; consulting fees from Roche; lecture fees from Roche, Bristol Myers Squibb, MSD, Servier, and Sanofi; travel expenses from Roche, AstraZeneca, and MSD; and advisory board fees from MSD, Bristol Myers Squibb, and AstraZeneca. PRD reports a research grant (via their institution) from Pfizer; consulting fees from Bristol Myers Squibb, Merck/Pfizer, and Ipsen; lecture fees from Bayer; travel expenses from Janssen; and owns stock in Alkermes and Biocartis Group NV. GJ reports research grants (via their institution) from Novartis, Roche, and Pfizer; and reports consulting fees, lecture fees, travel expenses, or advisory board fees from Novartis, Amgen, Roche, Pfizer, Bristol Myers Squibb, Eli Lilly, AstraZeneca, Daiichi Sankyo, AbbVie, Seagen, Medimmune, and Merck. DB reports consulting fees from Incyte and travel expenses from the European Hematology Association, I-Well, Abbvie, and Janssen. JF received advisory board fees or lecture fees (via their institution) from Pfizer, GlaxoSmithKline, Merck, and Janssen. HW received research grants (via their institution) from Roche, Novartis, and Gilead; and received consulting fees, lecture fees, or travel expenses from AbbVie, Daiichi, Gilead, Eli Lilly, Pfizer, AstraZeneca, EISAI, Immutep Pty, MSD, AstraZeneca Ireland, and Relay Therapeutics. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

This study aims to describe end-of-life (EOL) care in older patients with cancer and investigate the association between geriatric assessment (GA) results and specialized palliative care (SPC) use. Older patients with a new cancer diagnosis (2009-2015) originally included in a previous multicentric study were selected if they died before the end of follow-up (2019). At the time of cancer diagnosis, patients underwent geriatric screening with Geriatric 8 (G8) followed by GA in case of a G8 score ≤14/17. These data were linked to the cancer registry and healthcare reimbursement data for follow-up. EOL care was assessed in the last three months before death, and associations were analyzed using logistic regression. A total of 3546 deceased older patients with cancer with a median age of 79 years at diagnosis were included. Breast, colon, and lung cancer were the most common diagnoses. In the last three months of life, 76.3% were hospitalized, 49.1% had an emergency department visit, and 43.5% received SPC. In total, 55.0% died in the hospital (38.5% in a non-palliative care unit and 16.4% in a palliative care unit). In multivariable analyses, functional and cognitive impairment at cancer diagnosis was associated with less SPC. Further research on optimizing EOL healthcare utilization and broadening access to SPC is needed.

Introduction: Geriatric screening and geriatric assessment (GS/GA) have proven their benefits in the care for older patients with cancer. However, less is known about the predictive value of GS/GA for outcomes. To research this, clinical data on GS/GA can be enriched with population-based data. In this article we describe the methods and feasibility of data linkage, and first clinical outcomes (GS/GA results and overall survival)., Materials and Methods: A large cohort study consisting of patients aged ≥70 years with a new cancer diagnosis was established using linked data from clinical and population-based databases. Clinical data were derived from a previous prospective study where older patients with cancer were screened with G8, followed by GA in case of an abnormal result (GS/GA study; 2009-2015). These data were linked to cancer registration data from the Belgian Cancer Registry (BCR), reimbursement data of the health insurance companies (InterMutualistic Agency, IMA), and hospital discharge data (Technical Cell, TCT). Cox regression analyses were conducted to evaluate the prognostic value of the G8 geriatric screening tool., Results: Of the 8067 eligible patients with a new cancer diagnosis, linkage of data from the GS/GA study and data from the BCR was successful for 93.7%, resulting in a cohort of 7556 patients available for the current analysis. Further linkage with the IMA and TCT database resulted in a cohort of 7314 patients (96.8%). Based on G8 geriatric screening, 67.9% of the patients had a geriatric risk profile. Malnutrition and functional dependence were the most common GA-identified risk factors. An abnormal baseline G8 score (≤14/17) was associated with lower overall survival (adjusted HR [aHR] = 1.62 [1.50-1.75], p < 0.001)., Discussion: Linking clinical and population-based databases for older patients with cancer has shown to be feasible. The GS/GA results at cancer diagnosis demonstrate the vulnerability of this population and the G8 score showed prognostic value for overall survival. The established cohort of almost 8000 patients with long-term follow-up will serve as a basis in the future for detailed analyses on long-term outcomes beyond survival., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data., Methods: The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy., Results: Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%)., Conclusions: Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Breast cancer cells closely interact with different cell types of the surrounding adipose tissue to favor invasive growth and metastasis. Extracellular vesicles (EVs) are nanometer-sized vesicles secreted by different cell types that shuttle proteins and nucleic acids to establish cell-cell communication. To study the role of EVs released by cancer-associated adipose tissue in breast cancer progression and metastasis a standardized EV isolation protocol that obtains pure EVs and maintains their functional characteristics is required. We implemented differential ultracentrifugation as a pre-enrichment step followed by OptiPrep density gradient centrifugation (dUC-ODG) to isolate EVs from the conditioned medium of cancer-associated adipose tissue. A combination of immune-electron microscopy, nanoparticle tracking analysis (NTA) and Western blot analysis identified EVs that are enriched in flotillin-1, CD9 and CD63, and sized between 20 and 200 nm with a density of 1.076-1.125 g/ml. The lack of protein aggregates and cell organelle proteins confirmed the purity of the EV preparations. Next, we evaluated whether dUC-ODG isolated EVs are functionally active. ZR75.1 breast cancer cells treated with cancer-associated adipose tissue-secreted EVs from breast cancer patients showed an increased phosphorylation of CREB. MCF-7 breast cancer cells treated with adipose tissue-derived EVs exhibited a stronger propensity to form cellular aggregates. In conclusion, dUC-ODG purifies EVs from conditioned medium of cancer-associated adipose tissue, and these EVs are morphologically intact and biologically active.