Systemic inflammation-modified PG-SGA index in the survival prediction of advanced cancers.

Abstract

12056 Background: The tight association between inflammation and nutrition has been established. Whether the systemic inflammatory indicators could increase the predictive power of nutrition assessment tools in the survival of advanced cancers remains unclear. Methods: In this retrospective study, advanced cancer patients were enrolled from the West China Hospital between Nov 2019 and Sep 2023. Five systemic inflammatory indicators were selected including the systemic inflammatory index (SII), neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), and advanced lung cancer inflammation index (ALI). Nutritional status was evaluated by the PG-SGA. Kaplan-Meier curves, restricted cubic splines (RCS), and Cox regression analyses were used to estimate the effects of systemic inflammation indices as well as systemic inflammation-modified PG-SGA index on the survival of cancer patients. Stratified and sensitive analyses were conducted to determine the high-risk subgroups and check the robustness of the main findings. Results: In total, there were 1,065 advanced-stage cancer patients included in this study, with a mean age of 58.18 years old. Advanced lung cancer (265 cases, 24.9%) accounted for relatively higher proportions of the whole study population, followed by colorectal cancer (167 cases, 15.7%), esophagus cancer (143 cases, 13.4%), and gastric cancer (139 cases, 13.0%). With a median follow-up of 1,300 days, 501 deaths (47.04%) were observed. Multivariate Cox regression analysis showed that ALI maintained the best predictive power in predicting the all-cause mortality of advanced-stage cancer patients when compared with other systemic inflammation indicators (4th quartile: hazard ratio (HR)= 0.38, 95% confidence interval (CI): 0.29, 0.50, P<0.001), with a C-index of 0.615. The novel inflammation-modified PG-SGA/ALI index showed better predictive value in identifying the high-risk subgroups of advanced-stage cancer patients, with an AUC of ROC of 0.68 in 365-day mortality, 0.65 in 730-day mortality, and 0.64 in 1300-day survival prediction, respectively. The C-index (0.626), calibration curves, and decision curve analysis validated the promising predictive accuracy of the PG-SGA/ALI index in predicting the all-cause mortality of advanced-stage cancer patients. No significant interaction was observed between subgroups. A series of sensitive analyses supported the consistent findings as we determined in the main regression. Conclusions: Our study highlights that the PG-SGA/ALI index has superior predictive value than original PG-SGA criteria in short- and long-term survival prediction of advanced cancers. The PG-SGA/ALI index would be a more accurate tool for nutritional evaluation and prognosis prediction among cancer patients during clinical practice.