Establishment and validation of a nomogram based on coagulation parameters to predict the prognosis of pancreatic cancer.

BACKGROUND: In recent years, multiple coagulation and fibrinolysis (CF) indexes have been reported to be significantly related to the progression and prognosis of some cancers. OBJECTIVE: The purpose of this study was to comprehensively analyze the value of CF parameters in prognosis prediction of pancreatic cancer (PC). METHODS: The preoperative coagulation related data, clinicopathological information, and survival data of patients with pancreatic tumor were collected retrospectively. Mann Whitney U test, Kaplan-Meier analysis, and Cox proportional hazards regression model were applied to analyze the differences of coagulation indexes between benign and malignant tumors, as well as the roles of these indexes in PC prognosis prediction. RESULTS: Compared with benign tumors, the preoperative levels of some traditional coagulation and fibrinolysis (TCF) indexes (such as TT, Fibrinogen, APTT, and D-dimer) were abnormally increased or decreased in patients with pancreatic cancer, as well as Thromboelastography (TEG) parameters (such as R, K, α Angle, MA, and CI). Kaplan Meier survival analysis based on resectable PC patients showed that the overall survival (OS) of patients with elevated α angle, MA, CI, PT, D-dimer, or decreased PDW was markedly shorter than other patients; moreover, patients with lower CI or PT have longer disease-free survival. Further univariate and multivariate analysis revealed that PT, D-dimer, PDW, vascular invasion (VI), and tumor size (TS) were independent risk factors for poor prognosis of PC. According to the results of modeling group and validation group, the nomogram model based on independent risk factors could effectively predict the postoperative survival of PC patients. CONCLUSION: Many abnormal CF parameters were remarkably correlated with PC prognosis, including α Angle, MA, CI, PT, D-dimer, and PDW. Furthermore, only PT, D-dimer, and PDW were independent prognostic indicators for poor prognosis of PC, and the prognosis prediction model based on these indicators was an effective tool to predict the postoperative survival of PC.

Development and application of data standards for real world studies of pancreatic diseases / 中华消化外科杂志

Pancreatic diseases is a kind of complex, high-risk gastrointestinal diseases. Pancreatic cancer is highly malignant and seriously endangers people′s health. Developing multi-center, large-scale real world research can better understand the incidence patterns and treatment outcomes of pancreatic diseases. Based on the multi-center and heterogeneous data, the authors for-mulate data standards for real world studies of pancreatic diseases, and build a database of pancreatic cancer, integrating and sharing data from multi-center sources, in order to fully explore the scientific research value of pancreatic cancer clinical information, and provide experience and reference for the construction of other real world research specific disease databases in the future.