A novel nomogram and risk classification system based on inflammatory and immune indicators for predicting prognosis of pancreatic cancer patients with liver metastases

BACKGROUND: The study determined to construct a novel predictive nomogram to access the prognosis of pancreatic cancer patients with liver metastases (PCLM). METHODS: Medical records included clinical and laboratory variables were collected. The patients were randomly divided into training and validation cohort. First, in the training cohort, the optimal cutoff value of SII, PNI, NLR, PLR were obtained. Then the survival analysis evaluated the effects of above indices on OS. Next, univariate and multivariate analyses were used to identify the independent factors of OS. Moreover, a nomogram was constructed based on LASSO cox analysis. Additionally, the predictive efficacy of the nomogram was evaluated by ROC curve and calibration curve in the training and validation cohort. Finally, a risk stratification system based on the nomogram was performed. RESULTS: A total of 472 PCLM patients were enrolled in the study. The optimal cutoff values of SII, PNI, PLR and NLR were 372, 43.6, 285.7143 and 1.48, respectively. By combing SII and PNI, named coSII‐PNI, we divided the patients into three groups. The Kaplan–Meier curves demonstrated above indices were correlated with OS. Univariate and multivariate analyses found the independent prognostic factors of OS. Through LASSO cox analysis, coSII‐PNI, PNI, NLR, CA199, CEA, chemotherapy and gender were used to construct the nomogram. Lastly, the ROC curve and calibration curve demonstrated that the nomogram can predict prognosis of PCLM patients. Significant differences were observed between high and low groups. CONCLUSIONS: The nomogram based on immune, inflammation, nutritional status and other clinical factors can accurately predict OS of PCLM patients.