Prognostic nomogram for patients undergoing resection for adenocarcinoma of the pancreas.
BACKGROUND
Predictive nomograms are becoming increasingly used to define and predict outcome. They can be developed at presentation or following treatment and include variables not conventionally used in standard staging systems.


METHODS
We use a predictive nomogram based on prospectively collected data from 555 pancreatic resections for adenocarcinoma at a single institution. At last follow-up, 481 (87%) had died, defining a mature and comprehensive database. We used a 1-, 2-, and 3-year follow-up, as the number of patients alive beyond 3 years is sufficiently limited to provide insufficient events.


RESULTS
Based on a Cox model, we then developed a nomogram that predicts the probability that a patient will survive pancreatic cancer for 1, 2, and 3 years from the time of the initial resection, assuming that there is not death from an alternate cause. Calibration between observed and corrected is good, and variables not conventionally associated with standard staging systems improved the predictivity of the model.


CONCLUSIONS
This nomogram can serve as a basis for investigating other potentially predictive variables that are proposed of prognostic importance for patients undergoing resection for adenocarcinoma of the pancreas.
Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York 10021, USA. brennanm@mskcc.org
Murray F
MF
Brennan
Michael W
MW
Kattan
David
D
Klimstra
Kevin
K
Conlon
D000230:Adenocarcinoma / Q000401:mortality* / Q000473:pathology / Q000601:surgery*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001707:Biopsy, Needle; D002423:Cause of Death; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009367:Neoplasm Staging; D010180:Pancreatectomy / Q000379:methods* / Q000401:mortality; D010190:Pancreatic Neoplasms / Q000401:mortality* / Q000473:pathology / Q000601:surgery*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012042:Registries; D018570:Risk Assessment; D016019:Survival Analysis; D013997:Time Factors