Dataset Information

A proposed model for prediction of survival based on a follow-up study in unresectable pancreatic cancer.

ABSTRACT:

Objectives

To define an easy-to-use model for prediction of survival time in patients with unresectable pancreatic cancer in order to optimise patient' care.

Design

An observational retrospective study on patients with unresectable pancreatic cancer. The initial radiographs at presentation of symptoms were reviewed and the maximum diameter of the primary tumour was determined. The occurrence of liver metastases and performance status that determines initiation of chemotherapy was also used in the regression analysis to identify prognostic subgroups.

Setting

County hospital in south-east of Sweden.

Population

Consecutive patients with unresectable pancreatic cancer who were diagnosed between January 2003 and May 2010 (n=132).

Main outcome measures

Statistical analyses were performed using Stata V.13. Survival time was assessed with Kaplan-Meier analysis, log-rank test for equality of survivor functions and Cox regression for calculation of individual hazard based on tumour diameter, presence of liver metastases and initiation of chemotherapy treatment according to patient performance status.

Results

The individual hazard was log h=0.357 tumour size+1.181 liver metastases-0.989 performance status/chemotherapy. Three prognostic groups could be defined: a low-risk group with a median survival time of 6.7 (IQR 9.7) months, a medium-risk group with a median survival time of 4.5 (IQR 4.5) months and a high-risk group with a median survival time of 1.2 (IQR 1.7) months.

Conclusions

The maximum diameter of the primary tumour and the presence of liver metastases found at the X-ray examination of patients with pancreatic cancer, in conjunction with whether or not chemotherapy is initiated according to performance status, predict the survival time for patients who do not undergo surgical resection. The findings result in an easy-to-use model for predicting the survival time.

SUBMITTER: Forssell H

PROVIDER: S-EPMC3884621 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

A proposed model for prediction of survival based on a follow-up study in unresectable pancreatic cancer.

Forssell Henrik H Wester Michael M Akesson Katrin K Johansson Sigrid S

BMJ open 20131217 12

<h4>Objectives</h4>To define an easy-to-use model for prediction of survival time in patients with unresectable pancreatic cancer in order to optimise patient' care.<h4>Design</h4>An observational retrospective study on patients with unresectable pancreatic cancer. The initial radiographs at presentation of symptoms were reviewed and the maximum diameter of the primary tumour was determined. The occurrence of liver metastases and performance status that determines initiation of chemotherapy was ...[more]

PMID: 24345902

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Project description:Although gemcitabine monotherapy is the standard treatment for advanced pancreatic cancer, patient outcome varies significantly, and a considerable number do not benefit adequately. We therefore searched for new biomarkers predictive of overall patient survival. Using LC-MS, we compared the base-line plasma proteome between 29 representative patients with advanced pancreatic cancer who died within 100 days and 31 patients who survived for more than 400 days after receiving at least two cycles of the same gemcitabine monotherapy. Identified biomarker candidates were then challenged in a larger cohort of 304 patients treated with the same protocol using reverse-phase protein microarray. Among a total of 45,277 peptide peaks, we identified 637 peaks whose intensities differed significantly between the two groups (p < 0.001, Welch's t test). Two MS peaks with the highest statistical significance (p = 2.6 x 10(-4) and p = 5.0 x 10(-4)) were revealed to be derived from alpha(1)-antitrypsin and alpha(1)-antichymotrypsin, respectively. The levels of alpha(1)-antitrypsin (p = 8.9 x 10(-8)) and alpha(1)-antichymotrypsin (p = 0.001) were significantly correlated with the overall survival of the 304 patients. We selected alpha(1)-antitrypsin (p = 0.0001), leukocyte count (p = 0.066), alkaline phosphatase (p = 8.3 x 10(-8)), and performance status (p = 0.003) using multivariate Cox regression analysis and constructed a scoring system (nomogram) that was able to identify a group of high risk patients having a short median survival time of 150 days (95% confidence interval, 123-187 days; p = 2.0 x 10(-15), log rank test). The accuracy of this model for prognostication was internally validated and showed good calibration and discrimination with a bootstrap-corrected concordance index of 0.672. In conclusion, an increased level of alpha(1)-antitrypsin is a biomarker that predicts short overall survival of patients with advanced pancreatic cancer receiving gemcitabine monotherapy. Although an external validation study will be necessary, the current model may be useful for identifying patients unsuitable for the standardized therapy.

Dataset Information

A proposed model for prediction of survival based on a follow-up study in unresectable pancreatic cancer.

Objectives

Design

Setting

Population

Main outcome measures

Results

Conclusions

Publications

A proposed model for prediction of survival based on a follow-up study in unresectable pancreatic cancer.

Similar Datasets

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