A prognostic index model to predict the clinical outcomes for advanced pancreatic cancer patients following palliative chemotherapy

Abstract

PurposeTo establish a prognostic index model for advanced pancreatic cancer patients receiving palliative chemotherapy based on clinical variables.MethodsThe clinical data of 118 patients with advanced pancreatic cancer who received palliative chemotherapy between January 2006 and August 2013 in our center were retrospectively analyzed. Prognostic factors for overall survival were identified using Cox proportional hazards model. A prognostic index model was established by these pretreatment factors to predict prognosis. Kaplan–Meier estimation and log-rank test were performed to compare the overall survival difference between low-risk and high-risk group of patients.ResultsMedian overall survival time for all patients was 8.8 months [95 % confidence interval (CI) 7.0–10.6 months]. Multivariate analysis identified ECOG score = 2 (hazard ratio 2.03; 95 % CI 1.07–3.85; P = 0.030), CA19-9 levels of ≥1000 U/mL (hazard ratio 2.07; 95 % CI 1.09–3.92; P = 0.026), and CRP levels of ≥5 mg/L (hazard ratio 2.05; 95 % CI 1.06–3.96; P = 0.033) as independent poor prognostic factors for overall survival. For the three factors, ECOG score = 2, CA19-9 levels of ≥1000 U/mL, and CRP levels of ≥5 mg/L were allocated 1 point each. There were 84 (71.2 %) patients allocated to low-risk group with total score 0–1 point, and 34 (28.8 %) patients were categorized as high-risk group with total scores 2–3 points. The median overall survival for low-risk group and high-risk group was 9.9 months (95 % CI 6.8–13.0) and 5.3 months (95 % CI 4.1–6.5), respectively (hazard ratio 0.27; 95 % CI 0.14–0.52; P < 0.001). The estimated 1-year survival rates for low-risk group and high-risk group were 40.5 and 5.9 %, respectively (P < 0.05).ConclusionsA novel prognostic index model based on three clinical parameters was established to predict the prognosis of patients with advanced pancreatic cancer receiving palliative chemotherapy.

Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real‐world study

“…Up to date, various kinds of prognostic models were reported in pancreatic cancer (Table ) . Overall, there were 3 common types of models: nomogram, models based on the regression coefficient of prognostic factors and models based on the number of prognostic factors.…”

“…6 Up to date, various kinds of prognostic models were reported in pancreatic cancer (Table S1). [7][8][9][10][11][12][13][14][15] Overall, there were 3 common types of models: nomogram, models based on the regression coefficient of prognostic factors and models based on the number of prognostic factors. It seems that only nomogram can provide a valid clinical outcome for each patient and is gradually used in evaluating cancer prognosis.…”

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.…”

“…[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.…”