ObjectiveTo investigate value of drainage fluid amylase content and other risk factors in predicting clinically relevant postoperative pancreatic fistula (CR-POPF) after laparoscopic pancreaticoduodenectomy (LPD).MethodsThe clinical data of 166 patients who underwent LPD in this hospital from January 2017 to December 2019 were retrospectively analyzed. The independent risk factors of CR-POPF after LPD were analyzed. And the sensitivity and specificity of drainage fluid amylase content obtained on postoperative day 1 and 3 (Abbreviated as DFA1 and DFA3, respectively) in predicting CR-POPF by receiver operating characteristic (ROC) curve.ResultsA total of 166 patients underwent LPD were collected in this study. The CR-POPF occurred in 16 (9.6%) patients and all of them were grade B. The DFA1 (P=0.037), DFA3 (P<0.001), and positive bacterial culture of drainage fluid after operation (P=0.020) were the independent risk factors of CR-POPF after LPD (P<0.05) by the logistic regression multivariate analysis. The area under the ROC curve of the DFA1 and DFA3 in predicting CR-POPF was 0.880 [95%CI (0.812, 0.949)] and 0.912 [95%CI (0.853, 0.971)] respectively, and the corresponding best critical value was 3 925 and 939 U/L, and the sensitivity was 100% and 100%, specificity was 64.0% and 70.7%, respectively.ConclusionFor patients with DFA1>3 925 U/L, DFA3>939 U/L, and positive bacterial culture of drainage fluid after operation, preventive measures should be made to avoid CR-POPF.
ObjectiveTo study the predictability of dynamic prediction model of clinical pancreatic fistula in patients with or without pancreatic duct stent in laparoscopic pancreaticoduodenectomy (LPD).MethodsA total of 66 patients who underwent LPD in West China Hospital of Sichuan University from November 2019 to October 2020 were enrolled in the randomized controlled trial (registration number: ChiCTR1900026653). The perioperative data of the patients were collected in real time. The patients were divided into groups according to whether the pancreatic duct support tube was retained during the operation, and the probability prediction value was output according to the model formula. The specificity, sensitivity, accuracy, discrimination, and stability of the prediction results were analyzed.ResultsFor the group with pancreatic stent tubes, the specificity, sensitivity, and accuracy of the model at the model cut-off points on the postoperative day 2, 3 and 5 were 92.0%, 76.7% and 57.1%, 50.0%, 100% and 66.7%, and 88.8%, 78.8% and 61.3%, respectively. The areas under the ROC curve were 0.870, 0.956 and 0.702, respectively. The kappa values of the prediction result based on model cut-off point and cut-off point of ROC curve were 0.308, 0.582 and 0.744, respectively. Whereas for those who without the stent tube, the specificity, sensitivity, and prediction accuracy of the model on the postoperative day 5 were 66.7%, 100% and 72%, respectively. The area under curve at different time points were 0.304, 0.821, and 0.958, respectively. The kappa values at the last two time points were 0.465 and 0.449, respectively.ConclusionsFor patients with pancreatic duct support during LPD operation, the dynamic model of clinical pancreatic fistula can more accurately screen high-risk groups of clinical pancreatic fistula, and has better stability of prediction results. For patients without supporting tube, in the case of flexible adjustment of the boundary point, the model can also be more accurate screening on the 3rd and 5th days after operation.
ObjectiveTo review the recent research progress on prediction models for pancreatic fistula after pancreaticoduodenectomy and explore the potential application of prediction models in personalized treatment, aiming to provide useful reference information for clinical doctors to improve patient treatment outcomes and quality of life. MethodsWe systematically searched and reviewed the literature on various prediction models for pancreatic fistula after pancreaticoduodenectomy in recent years domestically and internationally. ResultsSpecifically, the fistula risk score (FRS) and the alternative fistula risk score (a-FRS), as widely used tools, possessed a certain degree of subjectivity due to the lack of an objective evaluation standard for pancreatic texture. The updated alternative fistula risk score (ua-FRS) had demonstrated superior predictive efficacy in minimally invasive surgery compared to the original FRS and a-FRS. The NCCH (National Cancer Center Hospital) prediction system, based on preoperative indicators, showed high predictive accuracy. Prediction models based on CT imaging informatics had improved the accuracy and reliability of predictions. Prediction models based on elastography had provided new perspectives for the assessment of pancreatic texture and the prediction of clinically relevant postoperative pancreatic fistula. The Stacking ensemble machine learning model contributed to the individualization and localization of prediction models. The existing pancreatic fistula prediction models showed satisfactory predictive efficacy, but there were still limitations in identifying high-risk populations for pancreatic fistula. ConclusionsAfter pancreaticoduodenectomy, pancreatic fistula remains a major complication that is difficult to overcome. The prevention of pancreatic fistula is crucial for improving postoperative recovery and reducing mortality rates. Future research should focus on the development and validation of pancreatic fistula prediction models, thereby enhancing their predictive power and increasing their predictive efficacy in different regional populations, providing a scientific basis for medical decision-making.