A prognostic model using the neutrophil-albumin ratio and PG-SGA to predict overall survival in advanced palliative lung cancer

OBJECTIVE: Inflammation and malnutrition are common in patients with advanced lung cancer undergoing palliative care, and their survival time is limited. In this study, we created a prognostic model using the Inflam-Nutri score to predict the survival of these patients. METHODS: A retrospective cohort study was conducted on 223 patients with advanced, histologically confirmed unresectable lung cancer treated between January 2017 and December 2018. The cutoff values of the neutrophil-albumin ratio (NAR) and Patient-Generated Subjective Global Assessment (PG-SGA) score were determined by the X-tile program. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analysis were performed to identify prognostic factors of overall survival (OS). We then established a nomogram model. The model was assessed by a validation cohort of 72 patients treated between January 2019 and December 2019. The predictive accuracy and discriminative ability were assessed by the concordance index (C-index), a plot of the calibration curve and risk group stratification. The clinical usefulness of the nomogram was measured by decision curve analysis (DCA). RESULTS: The nomogram incorporated stage, supportive care treatment, the NAR and the PG-SGA score. The calibration curve presented good performance in the validation cohorts. The model showed discriminability with a C-index of 0.76 in the training cohort and 0.77 in the validation cohort. DCA demonstrated that the nomogram provided a higher net benefit across a wide, reasonable range of threshold probabilities for predicting OS. The survival curves of different risk groups were clearly separated. CONCLUSIONS: The NAR and PG-SGA scores were independently related to survival. Our prognostic model based on the Inflam-Nutri score could provide prognostic information for advanced palliative lung cancer patients and physicians.