18 May 2019 : Clinical Research
Med Sci Monit 2019; 25:3683-3691
BACKGROUND: The primary objective of this study was to assess the cumulative incidence of cause-specific mortality (CSM) and other causes of mortality (OCM) for patients with metastatic pancreatic duct adenocarcinoma (mPDAC). The secondary objective was to calculate the probability of CSM and build a competing risk nomogram to predict CSM for mPDAC.
MATERIAL AND METHODS: We identified patients with mPDAC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. We assessed the cumulative incidence function (CIF) for cause-specific mortality and other causes of mortality. We used Gray’s test to investigate the differences. The Fine and Gray proportional subdistribution hazard model was applied to model CIF. And a competing risk nomogram was built to predict the probability of CSM for mPDAC.
RESULTS: There were 10 527 eligible patients diagnosed with mPDAC from 2010 to 2015 who were included in our formal analysis. The 6-month cumulative incidence of CSM was 60.3% and 5.9% for other causes. Predictors of SCM for mPDAC included surgery, age, tumor size, chemotherapy, radiation therapy, bone metastasis, and liver metastasis. The nomogram was proven to be well calibrated, and had good model discriminative ability.
CONCLUSIONS: We assessed the CIF of CSM and competing risk mortality in patients with mPDAC using the SEER database. The Fine and Gray proportional subdistribution hazard model performance was good, with a concordance index of 0.74, and the competing-risks nomogram was built, which can be a helpful predictive tool for cases with mPDAC. However, a validation sample data set and further verification are still needed to assess a profile for prognostic use in a prospective study.
Keywords: nomograms, Pancreatic Neoplasms, SEER Program, Adenocarcinoma, Aged, Aged, 80 and over, Carcinoma, Pancreatic Ductal, Cause of Death, Incidence, Middle Aged, Neoplasm Metastasis, Neoplasm Staging, Probability, Prognosis, Proportional Hazards Models, Prospective Studies, Risk Factors
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