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Enabling personalized perioperative risk prediction by using a machine-learning model based on preoperative data.

Martin Graeßner^1,2, Bettina Jungwirth^1,2, Elke Frank^2,3

¹Department of Anaesthesiology and Intensive Care Medicine, School of Medicine, Technical University of Munich, Munich, Germany.

Scientific Reports

|May 2, 2023

Summary

This study developed an interpretable machine learning model for predicting patient risk of postoperative mortality using preoperative data. The model identifies individual risk factors, aiding personalized care and optimization strategies.

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Area of Science:

Medical Informatics
Machine Learning in Healthcare
Surgical Risk Assessment

Background:

Current preoperative risk scores have limited predictive accuracy and personalization.
Accurate risk assessment is crucial for shared decision-making and perioperative care.

Purpose of the Study:

To develop an interpretable machine learning (ML) model for predicting individual patient risk of postoperative mortality.
To identify key preoperative factors influencing patient risk.

Main Methods:

An extreme gradient boosting model was trained on preoperative data from 66,846 patients undergoing elective non-cardiac surgery.
Model performance was evaluated using ROC and PR curves; feature importance was analyzed.

Main Results:

The ML model demonstrated strong predictive performance (AUROC=0.95, AUPRC=0.109).
Key predictors included preoperative red blood cell concentrate orders, age, and C-reactive protein.
Individual patient risk factors were successfully identified.

Conclusions:

A highly accurate and interpretable ML model can predict postoperative in-hospital mortality.
The model aids in identifying modifiable risk factors for preoperative optimization.
This tool supports personalized risk assessment and improved patient care.