The Predictive Value of PG-SGA Criteria in Advanced Cancer Patients

To date, cancer is the leading cause of disability and death globally. Cancer-related mortality rate differs significantly across the world, reflecting varying patterns of inherent genetic heterogeneity and medical resources available factors. Considerable proportions of patients remained diagnosed with advanced-stage cancers at the initial visit, given the differences between demographic geographical and socioeconomic regions, and inadequate resources of China. For this reason, how to make adequate clinical management and prognostic prediction for advanced-stage cancers are still challenged. Malnutrition is prevalent in cancer patients, accounting for approximately 20% to 80% of the cancer population, which plays a key role in the short-and long-term clinical outcomes. Providing more real-world clinical evidence for supporting the beneficial role of nutritional screening in advanced-stage cancers can help to improve clinical nursing care for advanced-stage patients, especially in developing countries.

The malnutrition status of cancer patients is often accompanied by symptoms of poor nutrient intake, weight loss, progress of systemic inflammatory disorders, and upregulated immune responses. Emerging evidence has determined that the systemic pro-inflammation status was positively associated with elevated malnutritional scores and consistently predicted the worse survival probabilities of cancer patients. Notably, the European Society for Clinical Nutrition and Metabolism (ESPEN) expert consensus regarding the action against cancer-related malnutrition recommended that nutritionists were encouraged to use additional biomarkers to assess the severity of cancer-related systemic inflammation burdens, which might enhance the predictive value of existing malnutritional screening tools in clinical practice. Although the PG-SGA criteria, the nutrition assessment tool, has been widely interpreted and validated to be a simple and easily available tool for the prognosis prediction for varied cancers in recent years, the criteria of the PG-SGA lacked the calculation of systemic inflammatory burdens. Whether the inflammatory indicators could be the surrogate biomarkers for enhancing the prediction ability of PG-SGA for advanced-stage cancer patients remains less studied. Therefore, we aim to fill the mentioned research gaps to conduct this observational study.