Patterns of care in metastatic pancreatic cancer: patient selection in clinical routine
Wissenschaftlicher Artikel/Review - 23.09.2019
Scheithauer Werner, Putora Paul Martin, Grünberger Birgit, Eisterer Wolfgang, Wöll Ewald, Prager Gerald, Schaberl-Moser Renate, Greil Richard, Glatzer Markus
The management of patients with metastatic pancreatic cancer (mPC) is challenging, and the optimal treatment strategy is debated among experts. In an attempt to identify treatment decision criteria and to investigate variations in the first-line management of this disease, we performed an analysis of treatment algorithms among experts in the field of pancreatic cancer. The aim of this study was to identify relevant criteria in the complex process of patient selection and decision making for the management of mPC patients.
Experts from the ABCSG (Austrian Breast and Colorectal Cancer Study Group) Pancreatic Cancer Club were contacted and agreed to participate in this analysis. Eight experts from seven centers in Austria provided their decision algorithms for the first-line treatment of patients with mPC. Their responses were converted into decision trees based on the objective consensus methodology. The decision trees were used to identify consensus and discrepancies.
The final treatment algorithms included four decision criteria (performance status, age, comorbidities, and symptomatic disease) and six treatment options: mFOLFIRINOX, gemcitabine + nab-paclitaxel, gemcitabine mono, 5-FU mono, gemcitabine/erlotinib, and best supportive care (BSC).
We identified consensus for the treatment of young and fit patients with mFOLFIRINOX. With higher age and reduced performance status, gemcitabine + nab-paclitaxel was increasingly used. For patients with Eastern Co-operative Oncology Group Performance Status (ECOG PS) 4, BSC was the treatment of choice. Among experts, different decision criteria and treatment options are implemented in clinical routine. Despite multiple options in current recommendations, a consensus for specific recommendations was identified.