Machine learning algorithms as early diagnostic tools for pancreatic fistula following pancreaticoduodenectomy and guide drain removal: A retrospective cohort study

Objective: Clinically relevant postoperative pancreatic fistula (CR-POPF) remains the major cause of morbidity following pancreaticoduodenectomy (PD). Several model score systems such as the Fistula Risk Score (FRS) have been developed to predict CR-POPF using preoperative and intraoperative data. Machine learning (ML) algorithms are increasingly applied in the medical field and they could be used to assess the risk of CR-POPF, identify clinically meaningful data and guide drain removal. Methods: Data from consecutive patients who underwent PD between January 1, 2010 and March 31, 2021 at a single high-volume center was collected retrospectively in this study. Demographics, clinical features, intraoperative parameters, and laboratory values were used to conduct the ML model. Four different ML algorithms (CatBoost, lightGBM, XGBoost and Random Forest) were used to train this model with cross-validation. Results: A total of 2421 patients with 62 clinical parameters were enrolled in this ML model. The majority of patients (76.3%) underwent open PD while others underwent robot-assisted PD. CR-POPF occurred in 424 (17.5%) patients. The CatBoost algorithm outperformed other algorithms with a mean area under the receiver operating characteristic curve (AUC) of 0.81 (95% confidence interval: 0.80-0.82) from the 5-fold cross-validation procedure. In the test dataset, the CatBoost algorithm also achieved the best mean-AUC of 0.83. The most important value was mean drain fluid amylase (DFA) in the first seven postoperative days (POD). The performance of models that used only preoperative data and intraoperative data was marginally lower than that of models that used combined data. Conclusion: Our ML algorithms could be applied as early diagnostic tools for CR-POPF in patients who underwent PD. Such real-time clinical decision support tools can identify patients with a high risk of CR-POPF, help in developing the perioperative management plan and guide the optimal timing of drain removal.

Automated summary

In this study, machine learning algorithms were used to predict clinically relevant postoperative pancreatic fistula (CR-POPF) and guide drain removal after pancreaticoduodenectomy (PD). The best-performing algorithm was CatBoost, which achieved a mean area under the receiver operating characteristic curve (AUC) of 0.81 in cross-validation and a mean AUC of 0.83 in the test dataset. The most important parameter was the mean drain fluid amylase (DFA) in the first seven postoperative days.