In:
Annals of Surgery, Ovid Technologies (Wolters Kluwer Health)
Abstract:
To develop a prediction model for long-term (≥5 years) disease-free survival (DFS) after resection of pancreatic ductal adenocarcinoma (PDAC). Summary Background Data: Despite high recurrence rates, approximately 10% of patients have long-term DFS after PDAC resection. A model to predict long-term DFS may aid individualized prognostication and shared decision-making. Methods: This nationwide cohort study included all consecutive patients who underwent PDAC resection in the Netherlands (2014-2016). The best performing prognostic model was selected by Cox-proportional hazard analysis and Akaike’s Information Criterion, presented by hazard ratio’s (HR) with 95% confidence interval (CI). Internal validation was performed, and discrimination and calibration indices were assessed. Results: 836 patients with a median follow-up of 67 months (interquartile range 51-79) were analyzed. Long-term DFS was seen in 118 patients (14%). Factors predictive of long-term DFS were low preoperative carbohydrate antigen 19-9 (logarithmic; HR1.21; 95%CI 1.10-1.32), no vascular resection (HR1.33; 95%CI 1.12-1.58), T1 or T2 tumor stage (HR1.52; 95%CI 1.14-2.04, and HR1.17; 95%CI 0.98-1.39, respectively), well/moderate tumor differentiation (HR1.44; 95%CI 1.22-1.68), absence of perineural and lymphovascular invasion (HR1.42; 95%CI 1.11-1.81 and HR1.14; 95%CI 0.96-1.36, respectively), N0 or N1 nodal status (HR1.92; 95%CI 1.54-2.40, and HR1.33; 95%CI 1.11-1.60, respectively), R0 resection margin status (HR1.25; 95%CI 1.07-1.46), no major complications (HR1.14; 95%CI 0.97-1.35) and adjuvant chemotherapy (HR1.74; 95%CI 1.47-2.06). Moderate performance (concordance index 0.68) with adequate calibration (slope 0.99) was achieved. Conclusions: The developed prediction model, readily available at www.pancreascalculator.com, can be used to estimate the probability of long-term disease-free survival after resection of pancreatic ductal adenocarcinoma.
Type of Medium:
Online Resource
ISSN:
0003-4932
DOI:
10.1097/SLA.0000000000006004
Language:
English
Publisher:
Ovid Technologies (Wolters Kluwer Health)
Publication Date:
2023
detail.hit.zdb_id:
2002200-1
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