Systematic review of clinical prediction models for survival after surgery for resectable pancreatic cancer

Background As more therapeutic options for pancreatic cancer are becoming available, there is a need to improve outcome prediction to support shared decision‐making. A systematic evaluation of prediction models in resectable pancreatic cancer is lacking. Methods This systematic review followed the CHARMS and PRISMA guidelines. PubMed, Embase and Cochrane Library databases were searched up to 11 October 2017. Studies reporting development or validation of models predicting survival in resectable pancreatic cancer were included. Models without performance measures, reviews, s or more than 10 per cent of patients not undergoing resection in postoperative models were excluded. Studies were appraised critically. Results After screening 4403 studies, 22 (44 319 patients) were included. There were 19 model development/update studies and three validation studies, altogether concerning 21 individual models. Two studies were deemed at low risk of bias. Eight models were developed for the preoperative setting and 13 for the postoperative setting. Most frequently included parameters were differentiation grade (11 of 21 models), nodal status (8 of 21) and serum albumin (7 of 21). Treatment‐related variables were included in three models. The C‐statistic/area under the curve values ranged from 0·57 to 0·90. Based on study design, validation methods and the availability of web‐based calculators, two models were identified as the most promising. Conclusion Although a large number of prediction models for resectable pancreatic cancer have been reported, most are at high risk of bias and have not been validated externally. This overview of prognostic factors provided practical recommendations that could help in designing easily applicable prediction models to support shared decision‐making. A systematic review of prediction models for survival in resectable pancreatic cancer was performed. Two models that could function as a starting point for future validation and update studies were identified. Moreover, it was found that current models are not ready for use in clinical practice, mostly because of low methodological quality, lack of validation and limited clinical applicability. Models emerging