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Development of a Prognostic Model That Predicts Survival After Pancreaticoduodenectomy for Ampullary Cancer.

Development of a Prognostic Model That Predicts Survival After Pancreaticoduodenectomy for Ampullary Cancer.
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Feretis M, Wang T, Iype S, Duckworth A, Brais R, Basu B, Jamieson NV, Huguet E, Balakrishnan A, Jah A, Praseedom RK, Harper SJ, Liau SS,


Feretis M, Wang T, Iype S, Duckworth A, Brais R, Basu B, Jamieson NV, Huguet E, Balakrishnan A, Jah A, Praseedom RK, Harper SJ, Liau SS, (click to view)

Feretis M, Wang T, Iype S, Duckworth A, Brais R, Basu B, Jamieson NV, Huguet E, Balakrishnan A, Jah A, Praseedom RK, Harper SJ, Liau SS,

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Pancreas 2017 09 12() doi 10.1097/MPA.0000000000000929
Abstract
OBJECTIVES
The aims of this study were to (i) identify independent predictors of survival after pancreaticoduodenectomy for ampullary cancer and (ii) develop a prognostic model of survival.

METHODS
Data were analyzed retrospectively on 110 consecutive patients who underwent pancreaticoduodenectomy between 2002 and 2013. Subjects were categorized into 3 nodal subgroups as per the recently proposed nodal subclassification: N0 (node negative), N1 (1-2 metastatic nodes), or N2 (≥3 metastatic nodes). Clinicopathological features and overall survival were compared by Kaplan-Meier and Cox regression analyses.

RESULTS
The overall 1-, 3-, and 5-year survival rates were 79.8%, 42.2%, and 34.9%, respectively. The overall 1-, 3-, and 5-year survival rates for the N0 group were 85.2%, 71.9%, and 67.4%, respectively. The 1-, 3-, 5-year survival rates for the N1 and N2 subgroups were 84.6%, 58.4%, and 56.4% and 80.2%, 38.8%, and 8.0%, respectively (log rank, P < 0.0001). After performing a multivariate Cox regression analysis, vascular invasion and lymph node ratio were the only independent predictors of survival. Hence, a prediction model of survival was constructed based on those 2 variables. CONCLUSIONS
Using data from a carefully selected cohort of patients, we created a pilot prognostic model of postresectional survival. The proposed model may help clinicians to guide treatments in the adjuvant setting.This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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