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BMC Cardiovascular Disorders

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Open Access 01-12-2024 | Research

A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

Authors: Qiuying Li, Jiaxin Li, Jiansong Chen, Xu Zhao, Jian Zhuang, Guoping Zhong, Yamin Song, Liming Lei

Published in: BMC Cardiovascular Disorders | Issue 1/2024

Abstract

Background

Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.

Methods

The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively.

Result

The Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated.

Conclusions

We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

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Title: A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records
Authors: Qiuying Li
Jiaxin Li
Jiansong Chen
Xu Zhao
Jian Zhuang
Guoping Zhong
Yamin Song
Liming Lei
Publication date: 01-12-2024
Publisher: BioMed Central
Published in: BMC Cardiovascular Disorders / Issue 1/2024
Electronic ISSN: 1471-2261
DOI: https://doi.org/10.1186/s12872-024-03723-3

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A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

Abstract

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Methods

Result

Conclusions

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