RT Journal Article
T1 Prediction of in-hospital mortality after pancreatic resection in pancreatic cancer patients: A boosting approach via a population-based study using health administrative data
A1 Velez-Serrano, Jose F.
A1 Vélez Serrano, Daniel
A1 Hernandez Barrera, Valentin
A1 Jimenez Garcia, Rodrigo
A1 Lopez de Andres, Ana
A1 Carrasco Garrido, Pilar
A1 Álvaro Meca, Alejandro
A2 , 
AB BackgroundOne reason for the aggressiveness of the pancreatic cancer is that it is diagnosed late, which often limits both the therapeutic options that are available and patient survival. The long-term survival of pancreatic cancer patients is not possible if the tumor is not resected, even among patients who receive chemotherapy in the earliest stages. The main objective of this study was to create a prediction model for in-hospital mortality after a pancreatectomy in pancreatic cancer patients.MethodsWe performed a retrospective study of all pancreatic resections in pancreatic cancer patients in Spanish public hospitals (2013). Data were obtained from records in the Minimum Basic Data Set. To develop the prediction model, we used a boosting method.ResultsThe in-hospital mortality of pancreatic resections in pancreatic cancer patients was 8.48% in Spain. Our model showed high predictive accuracy, with an AUC of 0.91 and a Brier score of 0.09, which indicated that the probabilities were well calibrated. In addition, a sensitivity analysis of the information available prior to the surgery revealed that our model has high predictive accuracy, with an AUC of 0.802.ConclusionsIn this study, we developed a nation-wide system that is capable of generating accurate and reliable predictions of in-hospital mortality after pancreatic resection in patients with pancreatic cancer. Our model could help surgeons understand the importance of the patients’ characteristics prior to surgery and the health effects that may follow resection.
PB Public Library of Science
SN 1932-6203
YR 2017
FD 2017-06-07
LK https://hdl.handle.net/20.500.14352/96703
UL https://hdl.handle.net/20.500.14352/96703
LA eng
DS Docta Complutense
RD 23 jul 2024