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Ex Vivo Porcine Experimental Model for Studying and Teaching Lung Mechanics
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Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning

Peiyi Li1, Shuanliang Gao2, Yaqiang Wang3

  • 1Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Laboratory of Anesthesia and Critical Care Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; The Research Units of West China (2018RU012)-Chinese Academy of Medical Sciences, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

British Journal of Anaesthesia
|April 18, 2024
PubMed
Summary

Machine learning models accurately predict postoperative pulmonary complications (PPCs) using intraoperative data. This enables personalized ventilation strategies to reduce lung injury in surgical patients.

Keywords:
dynamic respiratory featuresintraoperative ventilation strategymachine learning modelpostoperative pulmonary complicationsreal-time risk prediction

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Area of Science:

  • Anesthesiology and Perioperative Medicine
  • Artificial Intelligence in Medicine
  • Pulmonary Medicine

Background:

  • Postoperative pulmonary complications (PPCs) are a significant concern in elderly surgical patients.
  • Early identification of modifiable risk factors is crucial for optimizing ventilation strategies and mitigating lung injury.

Purpose of the Study:

  • To develop, validate, and internally test machine learning (ML) models for predicting PPCs.
  • To utilize intraoperative respiratory features for enhanced predictive accuracy.

Main Methods:

  • Analysis of perioperative data from 10,284 patients (aged 65+) undergoing 10,484 operations.
  • Development and comparison of linear and nonlinear ML models against the ARISCAT tool.
  • Application of Shapley additive explanations (SHAP) for feature importance interpretation.

Main Results:

  • An optimized XGBoost model achieved an AUROC of 0.878 in validation and 0.881 in prospective cohorts, significantly outperforming ARISCAT (0.496-0.533).
  • Key modifiable predictors identified include respiratory dynamic system compliance, mechanical power, and driving pressure.
  • A simplified 20-variable XGBoost model yielded an AUROC of 0.864 and has been developed into a web tool.

Conclusions:

  • Real-time prediction of PPC risk in surgical patients is feasible using ML models.
  • Personalized intraoperative ventilatory strategies guided by these models can potentially reduce PPCs.
  • The developed web-based tool requires further external validation.