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Congenital Heart Surgery Machine Learning-Derived In-Depth Benchmarking Tool.

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Summary

Machine learning using optimal classification trees (OCTs) now provides actionable hospital performance analysis for congenital heart surgery. This tool assesses individual hospital outcomes against a virtual hospital benchmark, aiding self-improvement.

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Area of Science:

  • Cardiovascular Surgery
  • Machine Learning
  • Health Services Research

Background:

  • Optimal Classification Trees (OCTs) previously demonstrated accuracy in predicting risk and assessing performance in congenital heart surgery.
  • This methodology is extended to provide comprehensive, interpretable, and actionable hospital performance analysis across all procedures.

Purpose of the Study:

  • To extend machine learning-based OCT methodologies for interpretable and actionable hospital performance analysis in congenital heart surgery.
  • To establish case-adjusted benchmarking standards using a 'virtual hospital' concept.

Main Methods:

  • Analysis of 172,888 congenital cardiac surgical procedures from the European Congenital Heart Surgeons Association database (1989-2022).
  • Development of OCT models to predict hospital mortality (AUC, 0.866), prolonged mechanical ventilation (AUC, 0.851), and length of stay (AUC, 0.818).
  • Creation of an online, interactive tool for hospital self-assessment by risk-matched patient cohorts.

Main Results:

  • OCT models established case-adjusted benchmarks against a 'virtual hospital' aggregate.
  • 20.5% of 146 centers statistically overperformed and 20.5% underperformed their predicted hospital mortality benchmark.
  • An interactive tool reveals 14 hospital-specific patient cohorts for performance assessment.

Conclusions:

  • Machine learning-based OCT benchmarking offers automatic, case-adjusted performance assessment for hospitals performing congenital heart surgery.
  • Analysis extends beyond overall performance to include specific, risk-matched patient cohorts.
  • The user-accessible online platform facilitates hospital self-assessment and performance improvement.