Validation and Optimization of the International Study Group of Pancreatic Surgery Risk Classification of High-risk Pancreas for Postoperative Pancreatic Fistula After Pancreatoduodenectomy for Periampullary Tumors.

Kapoor, Deeksha; Desiraju, Yajushi; Chaudhari, Vikram A; Bagwan, Afroj Ismail; Chopde, Amit; Namachivayam, Arun K; Bhandare, Manish S; Shrikhande, Shailesh V · Ann Surg · 2026

retrospective_cohort · Level III

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Abstract

To externally validate the International Study Group of Pancreatic Surgery (ISGPS) classification and test its performance for predicting clinically relevant pancreatic fistula (CRPF) for periampullary (P-amp) tumors. The ISGPS is a simple 2-factor, 4-tier classification of pancreas-related risk for CRPF after a pancreatoduodenectomy. External validation and performance of the classification specific to P-amps are lacking. P-amps have different disease biology, lesser need for neoadjuvant therapy, softer pancreas, and a higher rate of CRPF, underscoring the importance of site-specific prediction. Validation was performed in a cohort of 1422 patients, with CRPF as the primary outcome. Model performance was tested by plotting the receiver operating curve and calibration plots. After analyzing the factors predicting CRPF, the model was optimized for P-amps. CRPF rate was 22.2% (315/1422), for P-amps being 25.8%. The ISGPS model performed moderately [area under the curve (AUC) = 0.632, 95% CI: 0.598-0.666, P < 0.001], with worse performance for P-amps (AUC = 0.605, 95% CI: 0.566-0.645, P < 0.001). On multivariate analysis, soft pancreas [odds ratio (OR): 1.689, 95% CI: 1.136-2.512, P = 0.010], body mass index ≥ 23 kg/m 2 (OR: 2.112, 95% CI: 1.464-3.046, P < 0.001) and pancreatic duct ≤ 3 mm (OR: 2.113, 95% CI: 1.457-3.064, P < 0.001) emerged as independent predictors, and the model was optimized. The adjusted ISGPS for P-amps showed improved discrimination (AUC = 0.672, P < 0.001, 95% CI: 0.637-0.707), with adequate performance on internal validation. The adjusted ISPGS performs better than the original ISGPS in predicting CRPF for P-amps. Large-scale multicenter data are needed to generate and validate site-specific predictive models.

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