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Validation and Optimisation of the ISGPS Risk Classification for Postoperative Pancreatic Fistula after Pancreatoduodenectomy for Periampullary Tumours. | LitMetric

AI Article Synopsis

  • This study aims to validate the International Study Group of Pancreatic Surgery (ISGPS) classification for predicting clinically relevant pancreatic fistula (CRPF) specifically in patients with periampullary tumors (P-amps).
  • The authors tested the ISGPS model on data from 1,422 patients and found a CRPF rate of 22.2%, which is higher (25.8%) for P-amps, revealing the need for a tailored prediction model.
  • By optimizing the classification for P-amps, the adjusted ISGPS showed improved predictive ability (AUC=0.672) compared to the original model, highlighting the need for further research with larger, multicenter studies.

Article Abstract

Objectives: 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 tumours (P-amps).

Background: The ISGPS is a simple two-factor, four-tier classification of pancreas-related risk for CRPF after a pancreatoduodenectomy (PD). 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.

Methods: 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 analysing the factors predicting CRPF, the model was optimised for P-amps.

Results: CRPF rate was 22.2% (315/1422), for P-amps being 25.8%. The ISGPS model performed moderately (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 (OR 1.689, 95% CI 1.136-2.512, P=0.010), body mass index ≥23 kg/m2 (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 optimised. 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.

Conclusion: The adjusted ISPGS performs better than the original ISGPS in predicting CRPF for P-amps. Large-scale multicenter data is needed to generate and validate site-specific predictive models.

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Source
http://dx.doi.org/10.1097/SLA.0000000000006485DOI Listing

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