Preoperative risk stratification for major complications following pancreaticoduodenectomy: Identification of high-risk patients

• This study presents an easily assessable risk stratification system specified to predict major complications following PD.
• The score is solely based on readily available preoperative predictive factors.
• Potential uses of this prediction tool include enhancing patient counseling and tailoring perioperative care.

IntroductionMorbidity after pancreaticoduodenectomy (PD) remains a major concern with high rates. The aim of this study was to identify preoperative risk factors and create a new risk score to predict major complications after PD.MethodsMedical records of patients undergoing PD between 1993 and 2014 were retrospectively reviewed according to survival and surgical and non-surgical complications. A split-sample cross validation was conducted in which the original cohort was randomly selected to a modelling and a validation group at a ratio of 2:1. Univariate and multivariate analysis were carried out on the modelling set to identify preoperative risk factors, which were entered into a binary logistic regression model with stepwise backward elimination to develop the risk score model. Receiver operating curve analysis was implemented to judge the model's prediction ability.ResultsPD was performed in 405 patients. A total of 29.1% (118 patients) developed major complications. On multivariate analysis, American Society of Anaesthesiologists (ASA) score and obesity as well as the presence of cardiovascular and pulmonary comorbidities were significant predictors for major complications. A risk score was derived from the regression model and successfully tested on the validation set (area under the curve = 0.84).ConclusionThe risk score showed a high accuracy to predict major complications after PD based on preoperative parameters only. This simple and quick approach allows for individualized risk assessment and may improve preoperative counselling and patient selection for perioperative treatment strategies.