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High-performance pediatric surgical risk calculator: A novel algorithm based on machine learning and pediatric NSQIP

Dimitris Bertsimas1, Michael Li1, Nova Zhang1

  • 1Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA.

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Machine learning models accurately predict pediatric surgical complications, outperforming existing calculators. This tool can enhance surgical care quality for children.

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Area of Science:

  • Surgical outcomes research
  • Machine learning in healthcare
  • Pediatric surgery

Background:

  • Developing accurate prediction models for pediatric surgical complications is crucial for improving patient outcomes.
  • Machine learning offers a promising approach with fewer statistical assumptions compared to traditional methods.

Purpose of the Study:

  • To develop and evaluate a machine learning-based prediction model for pediatric surgical complications using the National Surgical Quality Improvement Program (NSQIP) database.

Main Methods:

  • Utilized data from 2012-2018 pediatric-NSQIP procedures.
  • Defined primary outcome as 30-day post-operative morbidity/mortality.
  • Trained models on 2012-2017 data and validated on 2018 data.

Main Results:

  • Included over 431,148 patients in the training set and 108,604 in the testing set.
  • Achieved high performance in mortality prediction (0.94 AUC in the testing set).
  • Outperformed the ACS-NSQIP Calculator in predicting major (0.90 AUC), any (0.86 AUC), and minor (0.69 AUC) morbidity.

Conclusions:

  • Successfully developed a high-performing pediatric surgical risk prediction model.
  • This model has the potential to significantly improve the quality of surgical care for pediatric patients.