Using machine learning to predict bleeding after cardiac surgery.

Area of Science:

• Medical Informatics
• Cardiovascular Surgery
• Machine Learning in Healthcare

Background:

• Post-cardiac surgery bleeding is a significant complication.
• Accurate prediction of bleeding is crucial for patient management.
• Existing prediction methods may not fully utilize comprehensive patient data.

Purpose of the Study:

• To develop and evaluate machine learning models for predicting perioperative bleeding after cardiac surgery.
• To integrate data from multiple sources including surgical, perfusion, ICU, and laboratory records.
• To compare the performance of different machine learning algorithms in bleeding prediction.

Main Methods:

• Utilized data from 2000 cardiac surgery patients (February 2015 - March 2022).
• Trained machine learning models to predict bleeding using Papworth and Dyke et al. definitions.
• Assessed model performance using metrics like AUROC and AUPRC.

Main Results:

• The Ensemble Voting Classifier demonstrated the best performance.
• Achieved AUPRC of 0.310 (AUROC 0.738) for Papworth definition.
• Achieved AUPRC of 0.452 (AUROC 0.797) for Dyke definition of bleeding.

Conclusions:

• Machine learning effectively predicts post-cardiac surgery bleeding.
• Routinely collected data from various sources can be integrated for prediction.
• This predictive capability can aid in clinical decision-making and patient care.