Browsing Han Publications without organization data by Author "Schueren, M. de van der"

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The impact of nutrition on the lives of patients with digestive cancers: a position paper.
Vitaloni, M. ; Caccialanza, R. ; Ravasco, P ; Carrato, A. ; Kapala, A. ; Schueren, M. de van der ; Constantinides, D. ; Backman, E. ; Chuter, D. ; Santangelo, C. ; Maravic, Z. et al. sine al.
2022, Article / Letter to editor (Supportive Care in Cancer, (2022))Nutritional intervention is an essential part of cancer treatments. Research and clinical evidence in cancer have shown that nutritional support can reduce length of hospitalisation, diminish treatment-related toxicity, and improve nutrient intake, quality of life, and physical function. Nutritional intervention can improve outcomes and help patients in the successful completion of oncological treatments by preventing malnutrition. Malnutrition is a very common hallmark in patients with cancers. Almost one-fourth of cancer patients are at risk of dying because of the consequences of malnutrition, rather than cancer itself. Patients with digestive cancers are at higher risk of suffering malnutrition due to the gastrointestinal impairment caused by their disease. They are at high nutritional risk by definition, yet the majority of them have insufficient or null access to nutritional intervention.Inadequate resources are dedicated to implementing nutritional services in Europe. Universal access to nutritional support for digestive cancer patients is not a reality in many European countries. To change this situation, health systems should invest in qualified staff to reinforce or create nutritional teams' experts in digestive cancer treatments. We aim to share the patient community's perspective on the status and the importance of nutritional intervention. This is an advocacy manuscript presenting data on the topic and analysing the current situations and the challenges for nutrition in digestive cancers. It highlights the importance of integrative nutrition in the treatment of digestive cancers and advocates for equitable and universal access to nutritional intervention for all patients.
Comparison of the prevalence of 21 GLIM phenotypic and etiologic criteria combinations and association with 30-day outcomes in people with cancer: A retrospective observational study.
Kiss, N ; Steer, B. ; Schueren, M. de van der ; Loeliger, J ; Alizadehsani, R. ; Edbrooke, L. ; Deftereos, I ; Laing, E. ; Khosravi, A.
2022, Article / Letter to editor (Clinical Nutrition, vol. 41, iss. 5, (2022), pp. 1102-1111)BACKGROUND & AIMS: The Global Leadership Initiative on Malnutrition (GLIM) criteria require validation in various clinical populations. This study determined the prevalence of malnutrition in people with cancer using all possible diagnostic combinations of GLIM etiologic and phenotypic criteria and determined the combinations that best predicted mortality and unplanned hospital admission within 30 days. METHODS: The GLIM criteria were applied, in a cohort of participants from two cancer malnutrition point prevalence studies (N = 2801), using 21 combinations of the phenotypic (≥5% unintentional weight loss, body mass index [BMI], subjective assessment of muscle stores [from PG-SGA]) and etiologic (reduced food intake, inflammation [using metastatic disease as a proxy]) criteria. Machine learning approaches were applied to predict 30-day mortality and unplanned admission. RESULTS: We analysed 2492 participants after excluding those with missing data. Overall, 19% (n = 485) of participants were malnourished. The most common GLIM combinations were weight loss and reduced food intake (15%, n = 376), and low muscle mass and reduced food intake (12%, n = 298). Machine learning models demonstrated malnutrition diagnosis by weight loss and reduced muscle mass plus either reduced food intake or inflammation were the most important combinations to predict mortality at 30-days (accuracy 88%). Malnutrition diagnosis by weight loss or reduced muscle mass plus reduced food intake was most important for predicting unplanned admission within 30-days (accuracy 77%). CONCLUSIONS: Machine learning demonstrated that the phenotypic criteria of weight loss and reduced muscle mass combined with either etiologic criteria were important for predicting mortality. In contrast, the etiologic criteria of reduced food intake in combination with weight loss or reduced muscle mass was important for predicting unplanned admission. Understanding the phenotypic and etiologic criteria contributing to the GLIM diagnosis is important in clinical practice to identify people with cancer at higher risk of adverse outcomes.

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