Abstract
Background: The timing of surgery for necrotizing pancreatitis remains a matter of controversial debate, which has not been resolved by randomized controlled trial (RCT). This study aims to classify surgical timing within or beyond 4 weeks for patients with infected necrotizing pancreatitis by using machine learning methods. Methods: This study analyzed 223 patients who underwent surgery for infected pancreatic necrosis at West China Hospital of Sichuan University. We used logistic regression, support vector machine, and random forest with/without the simulation of generative adversarial networks to classify the surgical intervention within or beyond 4 weeks in the patients with infected necrotizing pancreatitis. Results: Our analyses showed that interleukin 6, infected necrosis, the onset of fever and C-reactive protein were important factors in determining the timing of surgical intervention (< 4 or ≥ 4 weeks) for the patients with infected necrotizing pancreatitis. The main factors associated with postoperative mortality in patients who underwent early surgery (< 4 weeks) included modified Marshall score on admission and preoperational modified Marshall score. Preoperational modified Marshall score, time of surgery, duration of organ failure and onset of renal failure were important predictive factors for the postoperative mortality of patients who underwent delayed surgery (≥ 4 weeks). Conclusions: Machine learning models can be used to predict timing of surgical intervention effectively and key factors associated with surgical timing and postoperative survival are identified for infected necrotizing pancreatitis.