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Predicting Intraoperative Hypothermia Burden during Non-Cardiac Surgery: A Retrospective Study Comparing Regression

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Machine learning (ML) models effectively predict intraoperative hypothermia burden, outperforming logistic regression. XGBoost and Random Forest showed the highest accuracy, suggesting ML

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Area of Science:

  • Anesthesiology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Inadvertent intraoperative hypothermia is a frequent complication impacting patient outcomes.
  • Predicting hypothermia is complex, potentially benefiting from advanced machine learning (ML) approaches over traditional logistic regression.

Purpose of the Study:

  • To compare the efficacy of various ML algorithms against logistic regression in predicting intraoperative hypothermia burden.
  • To evaluate the performance of seven distinct models including XGBoost, Random Forest, and others.

Main Methods:

  • A retrospective study analyzed 71 variables from 87,116 anesthesia cases.
  • Seven models were developed to predict hypothermia severity (none, mild, moderate, severe) based on hypothermia burden.
  • Model performance was assessed using F1 score, AUC, precision, recall, and calibration metrics.

Main Results:

  • Machine learning algorithms demonstrated superior performance compared to logistic regression in predicting hypothermia burden.
  • XGBoost achieved the highest weighted F1 score (0.44), followed closely by Random Forest (0.418).
  • Logistic regression yielded a weighted F1 score of 0.397, indicating lower predictive accuracy.

Conclusions:

  • Machine learning models are well-suited for predicting intraoperative hypothermia.
  • The findings support the potential clinical application of ML for proactive hypothermia management.