Systemic immune-inflammation index predicts prognosis of patients with advanced pancreatic cancer

Abstract

BackgroundSystemic inflammation and immune dysfunction have been proved to be associated with cancer progression and metastasis in various malignancies. The aim of this retrospective study was to evaluate the prognostic significance of pre-treatment systemic immune-inflammation index (SII) in patients with advanced pancreatic cancer.MethodsIn total, 419 patients diagnosed with advanced pancreatic cancer, between January 2011 and December 2015, were retrospectively enrolled. The SII was developed based on a training set of 197 patients from 2011 to 2013 and validated in an independent cohort of 222 patients from 2014 to 2015. Data on baseline clinicopathologic characteristics; pre-treatment laboratory variables such as absolute neutrophil, lymphocyte, and platelet counts; and carbohydrate antigen 19-9 (CA19-9), total bilirubin (TBIL), albumin (ALB), alkaline phosphatase (ALP), alanine transaminase (ALT), and aspartate transaminase (AST) levels were collected. The association between clinicopathologic characteristics and SII was assessed. The overall survival was calculated using the Kaplan–Meier survival curves and compared using the log-rank test. Univariate and multivariate Cox proportional hazard regression models were used to analyze the prognostic value of the SII.ResultAn optimal cutoff point for the SII of 440 stratified the patients with advanced pancreatic cancer into high (> 440) and low (≤ 440) SII groups in the training cohort. Univariate and multivariate analyses revealed that the SII was an independent predictor for overall survival. The prognostic significance of the SII was confirmed in both normal and elevated CA19-9 levels.ConclusionThe baseline SII serves as an independent prognostic marker for patients with advanced pancreatic cancer and can be used in patients with both normal and elevated CA19-9 levels.

Validation and application of a prognostic model for patients with advanced pancreatic cancer receiving palliative chemotherapy

“…In line with the GS model, the current study helped identify performance status, tumor stage, albumin, and NLR as independent prognostic factors that predict survival outcome. Several prognostic models have been developed for survival prediction among APC patients receiving first‐line palliative chemotherapy . Apart from patient factors (age, sex, performance status, weight loss), laboratory variables (albumin, white cell count, NLR, tumor markers) and tumor characteristics (stage, tumor size, metastatic organ, ascites) have all been reported to be independent prognosticators in different prognostic models .…”

“…Table summarizes and compares 13 published prognostic models for the prediction of the survival outcome of PC patients receiving palliative chemotherapy from a literature review . Only one of the models was designed prospectively ; the others were retrospective studies.…”

“…The performance reflected by C‐index ranged from 0.66 to 0.80 among these prognostic models. Only five models were validated by application to a different cohort . Because the GS model is easily applied and had good performance in term of C‐index value in the original and the current cohort, we believe this model to be widely applicable by clinicians to predict survival outcome in APC patients receiving first‐line palliative chemotherapy.…”

“…[14][15][16][17][18][19][20][21][22][23][24][25] Apart from patient factors (age, sex, performance status, weight loss), laboratory variables (albumin, white cell count, NLR, tumor markers) and tumor characteristics (stage, tumor size, metastatic organ, ascites) have all been reported to be independent prognosticators in different prognostic models. [14][15][16][17][18][19][20][21][22][23][24][25] Although no consistent prognosticators were reported among different reports, all models showed that in addition to tumor factors, patient features also were relevant to prognosis in PC patients. Consequently, the GS model was constructed by the combination of tumor-and patient-related factors, which may be more effective at predicting survival outcome than either tumor or patient factors alone.…”

“…[14][15][16][17][18][19][20][21][22][23][24][25] Only one of the models was designed prospectively 25 ; the others were retrospective studies. Of the 13 prognostic models, some were used for the prediction of survival outcome specifically for gemcitabine-based treatment, 11,14,16,19,22,24,25 while the other models consisted a broad spectrum of chemotherapeutic regimens. 15,17,18,20,21,23 The performance reflected by C-index ranged from 0.66 to 0.80 among these prognostic models.…”