Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

• Published in: HPB (Oxford, England) Vol. 24; no. 8; pp. 1201 - 1216
• Main Authors: Ioannou, Liane J., Maharaj, Ashika D., Zalcberg, John R., Loughnan, Jesse T., Croagh, Daniel G., Pilgrim, Charles H., Goldstein, David, Kench, James G., Merrett, Neil D., Earnest, Arul, Burmeister, Elizabeth A., White, Kate, Neale, Rachel E., Evans, Sue M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.08.2022
• Subjects: Carcinoma, Pancreatic Ductal - surgery; Humans; Pancreatic Neoplasms; Pancreatic Neoplasms - surgery; Prognosis; Retrospective Studies
• ISSN: 1365-182X; 1477-2574
• DOI: 10.1016/j.hpb.2022.01.011

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC. We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified. 3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study. Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.