Updated risk prediction model for pancreaticoduodenectomy using data from the National Clinical Database in Japan

Abstract

AbstractAimRisk prediction models for mortality, severe postoperative complications, and postoperative pancreatic fistula in patients undergoing pancreaticoduodenectomy were established using data from the National Clinical Database more than a decade ago, and the surgical outcomes of pancreaticoduodenectomy have improved over the years. The aim of this study is to update the risk prediction model for pancreaticoduodenectomy using National Clinical Database data.MethodsBetween 2019 and 2021, the data of 35 365 patients who underwent pancreaticoduodenectomy and who were registered in the National Clinical Database were retrospectively analyzed. According to the registration year, the patients were divided into two cohorts: the development cohort (2019–2020; n = 23 654) and the validation cohort (2021; n = 11 711). Logistic regression analyses were performed to create risk models for surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula.ResultsOverall, the rates of surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula were 1.8%, 2.2%, and 1.3%, respectively. Logistic regression analyses revealed 28, 28, and 14 risk factors for surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula, respectively. The area under the receiver operating characteristic curve of the risk model in the development cohort was 0.759 for surgical mortality, 0.712 for severe complications, and 0.699 for postoperative pancreatic fistula, comparable to the validation cohort. The calibration plots were favorable in both cohorts.ConclusionThe updated risk model for pancreaticoduodenectomy will be useful to predict surgical mortality, severe postoperative complications, and grade C postoperative pancreatic fistula.