Development and validation of a novel predictive model for postpancreatectomy hemorrhage using lasso-logistic regression: an international multicenter observational study of 9,631 pancreatectomy patients

Background: Hemorrhage following pancreatectomy represents a grave complication, exerting a significant impact on patient prognosis. The formulation of a precise predictive model for postpancreatectomy hemorrhage risk holds substantial importance in enhancing surgical safety and improving patient outcomes. Materials and Methods: This study utilized the patient cohort from the American College of Surgeons National Surgical Quality Improvement Program database, who underwent pancreatectomy between 2014 and 2017 (n=5779), as the training set to establish the Lasso-logistic model. For external validation, a patient cohort (n=3852) from the Chinese National Multicenter Database of Pancreatectomy Patients, who underwent the procedure between 2014 and 2020, was employed. A predictive nomogram for postpancreatectomy hemorrhage was developed, and polynomial equations were extracted. The performance of the predictive model was assessed through the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results: In the training and validation cohorts, 9.0% (520/5779) and 8.5% (328/3852) of patients, respectively, experienced postpancreatectomy hemorrhage. Following selection via lasso and logistic regression, only nine predictive factors were identified as independent risk factors associated with postpancreatectomy hemorrhage. These included five preoperative indicators (BMI, ASA ≥3, preoperative obstructive jaundice, chemotherapy within 90 days before surgery, and radiotherapy within 90 days before surgery), two intraoperative indicators (total operation time, vascular resection), and two postoperative indicators (postoperative septic shock, pancreatic fistula). The new model demonstrated high predictive accuracy, with an area under the receiver operating characteristic curve of 0.87 in the external validation cohort. Its predictive performance significantly surpassed that of the previous five postpancreatectomy hemorrhage risk prediction models ( P <0.001, likelihood ratio test). Conclusion: The Lasso-logistic predictive model we developed, constructed from nine rigorously selected variables, accurately predicts the risk of PPH. It has the potential to significantly enhance the safety of pancreatectomy surgeries and improve patient outcomes.