Abstract
BACKGROUND: Clinically relevant postoperative pancreatic fistula (CR-POPF) remains a significant complication after pancreaticoduodenectomy (PD), leading to prolonged hospital stays, increased healthcare costs, and higher mortality rates. Timely recognition of patients at high risk for CR-POPF is critical for the implementation of personalized management strategies. This study aimed to develop and validate a predictive nomogram using preoperative factors to accurately predict CR-POPF after PD. METHODS: A total of 262 consecutive patients who underwent PD between February 2021 and December 2022 at a single institution were enrolled and divided into a training cohort (n=209) and a validation cohort (n=53). Preoperative risk factors for CR-POPF were identified through least absolute shrinkage and selection operator (LASSO) regression and further evaluated using univariate and multivariate logistic regression models. A predictive nomogram was constructed based on the training cohort and validated internally using the validation cohort, with additional cross-validation. The nomogram's discriminative performance was evaluated using the area under the curve (AUC), sensitivity, specificity, calibration curves, and decision curve analysis (DCA). RESULTS: Overall, 36.2% (n=95) patients developed CR-POPF. The nomogram identified several preoperative factors, including triglycerides (TGs), neutrophils, the size of the main pancreatic duct (MPD), pancreatic index (PI), and thickness of the pancreas (TP), as independent risk factors for CR-POPF. Internal and cross-validation of receiver operating characteristic (ROC) curves yielded statistically significant results (AUC =0.761 and 0.812, respectively). Calibration curves demonstrated strong agreement between the nomogram's predictions and actual outcomes. DCA confirmed the nomogram's substantial clinical relevance. The sensitivity and specificity of the nomogram in the validation cohort were 60.9% and 90.0%, respectively. CONCLUSIONS: This predictive nomogram, based on preoperative risk factors such as TG, neutrophils, the size of MPD, PI, and TP, provides a simple and accurate method for predicting CR-POPF after PD, aiding clinicians in identifying high-risk patients and optimizing preoperative management strategies to improve decision-making.