4.7 Article

Conditional Survival After Resection for Pancreatic Cancer: A Population-Based Study and Prediction Model

Journal

ANNALS OF SURGICAL ONCOLOGY
Volume 27, Issue 7, Pages 2516-2524

Publisher

SPRINGER
DOI: 10.1245/s10434-020-08235-w

Keywords

Pancreatic cancer; Conditional survival; Survival; Prediction model

Funding

  1. Dutch Cancer Society (KWF Kankerbestrijding) [UVA2013-5842]

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Background Conditional survival is the survival probability after already surviving a predefined time period. This may be informative during follow-up, especially when adjusted for tumor characteristics. Such prediction models for patients with resected pancreatic cancer are lacking and therefore conditional survival was assessed and a nomogram predicting 5-year survival at a predefined period after resection of pancreatic cancer was developed. Methods This population-based study included patients with resected pancreatic ductal adenocarcinoma from the Netherlands Cancer Registry (2005-2016). Conditional survival was calculated as the median, and the probability of surviving up to 8 years in patients who already survived 0-5 years after resection was calculated using the Kaplan-Meier method. A prediction model was constructed. Results Overall, 3082 patients were included, with a median age of 67 years. Median overall survival was 18 months (95% confidence interval 17-18 months), with a 5-year survival of 15%. The 1-year conditional survival (i.e. probability of surviving the next year) increased from 55 to 74 to 86% at 1, 3, and 5 years after surgery, respectively, while the median overall survival increased from 15 to 40 to 64 months at 1, 3, and 5 years after surgery, respectively. The prediction model demonstrated that the probability of achieving 5-year survival at 1 year after surgery varied from 1 to 58% depending on patient and tumor characteristics. Conclusions This population-based study showed that 1-year conditional survival was 55% 1 year after resection and 74% 3 years after resection in patients with pancreatic cancer. The prediction model is available via to inform patients and caregivers.

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