TY - JOUR
T1 - SOURCE-PANC
T2 - A prediction model for patients with metastatic pancreatic ductal adenocarcinoma based on nationwide population-based data
AU - van den Boorn, H. ctor G.
AU - Dijksterhuis, Willemieke P. M.
AU - van der Geest, Lydia G. M.
AU - de Vos-Geelen, Judith
AU - Besselink, Marc G.
AU - Wilmink, Johanna W.
AU - van Oijen, Martijn G. H.
AU - van Laarhoven, Hanneke W. M.
N1 - Publisher Copyright: © 2021 Harborside Press. All rights reserved.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Background: A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. Materials and Methods: Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal–external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. Results: Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sub-location, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal–external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. Conclusions: A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.
AB - Background: A prediction model for overall survival (OS) in metastatic pancreatic ductal adenocarcinoma (PDAC) including patient and treatment characteristics is currently not available, but it could be valuable for supporting clinicians in patient communication about expectations and prognosis. We aimed to develop a prediction model for OS in metastatic PDAC, called SOURCE-PANC, based on nationwide population-based data. Materials and Methods: Data on patients diagnosed with synchronous metastatic PDAC in 2015 through 2018 were retrieved from the Netherlands Cancer Registry. A multivariate Cox regression model was created to predict OS for various treatment strategies. Available patient, tumor, and treatment characteristics were used to compose the model. Treatment strategies were categorized as systemic treatment (subdivided into FOLFIRINOX, gemcitabine/nab-paclitaxel, and gemcitabine monotherapy), biliary drainage, and best supportive care only. Validation was performed according to a temporal internal–external cross-validation scheme. The predictive quality was assessed with the C-index and calibration. Results: Data for 4,739 patients were included in the model. Sixteen predictors were included: age, sex, performance status, laboratory values (albumin, bilirubin, CA19-9, lactate dehydrogenase), clinical tumor and nodal stage, tumor sub-location, presence of distant lymph node metastases, liver or peritoneal metastases, number of metastatic sites, and treatment strategy. The model demonstrated a C-index of 0.72 in the internal–external cross-validation and showed good calibration, with the intercept and slope 95% confidence intervals including the ideal values of 0 and 1, respectively. Conclusions: A population-based prediction model for OS was developed for patients with metastatic PDAC and showed good performance. The predictors that were included in the model comprised both baseline patient and tumor characteristics and type of treatment. SOURCE-PANC will be incorporated in an electronic decision support tool to support shared decision-making in clinical practice.
UR - http://www.scopus.com/inward/record.url?scp=85116064831&partnerID=8YFLogxK
U2 - https://doi.org/10.6004/jnccn.2020.7669
DO - https://doi.org/10.6004/jnccn.2020.7669
M3 - Article
C2 - 34293719
SN - 1540-1405
VL - 19
SP - 1045
EP - 1053
JO - Journal of the national comprehensive cancer network
JF - Journal of the national comprehensive cancer network
IS - 9
ER -