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Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning

  • 0Atidia Health, Melbourne, Australia. gideon@atidia.health.
Bmc Medical Informatics and Decision Making +

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March 12, 2024

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March 12, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning algorithms effectively predict post-operative complications and length of stay, aiding surgical preparation. Further research with larger datasets is recommended to enhance predictive accuracy for specific outcomes like readmission and mortality.

Area Of Science

  • Medical Informatics
  • Machine Learning in Healthcare
  • Surgical Risk Assessment

Background

  • Pre-operative risk assessment is crucial for surgical patient preparation.
  • It helps reduce perioperative complications, hospital stay, readmissions, and mortality.
  • It also supports collaborative decision-making and operational planning.

Purpose Of The Study

  • To develop Machine Learning (ML) algorithms, named Patient Optimizer (POP), for pre-operative risk assessment.
  • To predict post-operative complications and inform future large-scale prospective studies.
  • To utilize ML for enhanced prediction of surgical outcomes.

Main Methods

  • Developed a base model combining patient and procedure risk, then automated with additional variables.
  • Employed 10-fold cross-validation and feature selection for model optimization.
  • Utilized logistic regression (LR) and extreme gradient-boosted trees (XGBoost) for model creation.
  • Evaluated model performance using Area Under the Receiver Operating Characteristic Curve (AUROC) and Area Under the Precision-Recall Curve (AUPRC).

Main Results

  • XGBoost and LR models showed similar performance across various endpoints.
  • POP with XGBoost achieved AUROC of 0.755 for any post-operative complication, 0.869 for kidney failure, and 0.841 for length of stay.
  • For 30-day readmission and mortality, POP with XGBoost achieved AUROC of 0.610 and 0.866, respectively.

Conclusions

  • The developed POP algorithms demonstrate effectiveness in predicting post-operative complications, kidney failure, and length of stay.
  • A larger study is warranted to refine the algorithms for improved prediction accuracy.
  • Expanded datasets could enhance predictions for specific complications, readmission, and mortality.

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