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Predicting Severe Postoperative Complications after CRS-HIPEC: An Externally Validated Machine-Learning Tool.

Amir Ashraf Ganjouei1, Jane Wang1, Christopher Yi2

  • 1Department of Surgery, University of California, San Francisco, California, USA.

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|April 28, 2025
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Summary

We developed a machine-learning tool to predict severe postoperative complications after cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC). This model aids in patient selection and anticipating complications, improving surgical outcomes.

Keywords:
CRS‐HIPECcomplicationsexternal validationmachine learningprediction tool

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Area of Science:

  • Oncology
  • Surgical Oncology
  • Medical Informatics

Background:

  • Current decision support tools for predicting postoperative complications after cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) have limitations due to small sample sizes and lack of external validation.
  • Accurate prediction of severe complications (Clavien-Dindo grade ≥3) is crucial for patient management and resource allocation in CRS-HIPEC procedures.

Purpose of the Study:

  • To develop and externally validate a machine-learning (ML) tool for predicting severe postoperative complications following CRS-HIPEC.
  • To improve patient selection and postoperative care by providing a reliable predictive model.

Main Methods:

  • Utilized a large dataset of adult patients who underwent CRS-HIPEC at the University of Pittsburgh Medical Center (UPMC) for training and internal validation (80:20 split).
  • Employed the US HIPEC collaborative dataset for external validation.
  • Trained and tested three ML models, using SHAP values to determine variable importance and recursive feature elimination for optimal model performance.

Main Results:

  • Severe postoperative complications occurred in 37% of the UPMC cohort and 22% of the external validation cohort.
  • A random forest model, optimized with 15 variables, achieved the highest area under the ROC curve (AUC) of 0.71 in internal validation and a mean AUC of 0.65 in external validation.
  • Key predictors of severe complications included high peritoneal cancer index (PCI), subtotal gastrectomy, low albumin levels, small bowel resection, and high Charlson Comorbidity Index.

Conclusions:

  • Developed and externally validated the largest ML model to date for predicting severe complications after CRS-HIPEC in the US.
  • The model can assist clinicians in patient selection and anticipating postoperative complications, potentially enhancing patient outcomes.