Machine learning model predicts the occurrence of acute kidney injury after open surgery for abdominal aortic aneurysm repair.
10.11817/j.issn.1672-7347.2023.220247
- Author:
Chang SHENG
1
;
Mingmei LIAO
2
;
Haiyang ZHOU
3
;
Pu YANG
4
Author Information
1. Department of Vascular Surgery, Xiangya Hospital, Central South University, Changsha 410008. shengchang0819@163.com.
2. Key Laboratory of Nanobiological Technology of National Health Commision, Xiangya Hospital, Central South University, Changsha 410008.
3. Department of Vascular Surgery, Xiangya Hospital, Central South University, Changsha 410008.
4. Department of Vascular Surgery, Xiangya Hospital, Central South University, Changsha 410008. puyang@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
abdominal aortic aneurysm;
acute kidney injury;
machine learning;
random forest;
support vector machine
- MeSH:
Humans;
Aortic Aneurysm, Abdominal/complications*;
Endovascular Procedures/methods*;
Retrospective Studies;
Blood Vessel Prosthesis Implantation/adverse effects*;
Acute Kidney Injury/etiology*;
Machine Learning;
Treatment Outcome;
Postoperative Complications/etiology*;
Risk Factors
- From:
Journal of Central South University(Medical Sciences)
2023;48(2):213-220
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:Abdominal aortic aneurysm is a pathological condition in which the abdominal aorta is dilated beyond 3.0 cm. The surgical options include open surgical repair (OSR) and endovascular aneurysm repair (EVAR). Prediction of acute kidney injury (AKI) after OSR is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, this study aims to perform tests on the efficacy of different machine learning models.
METHODS:Perioperative data of 80 OSR patients were retrospectively collected from January 2009 to December 2021 at Xiangya Hospital, Central South University. The vascular surgeon performed the surgical operation. Four commonly used machine learning classification models (logistic regression, linear kernel support vector machine, Gaussian kernel support vector machine, and random forest) were chosen to predict AKI. The efficacy of the models was validated by five-fold cross-validation.
RESULTS:AKI was identified in 33 patients. Five-fold cross-validation showed that among the 4 classification models, random forest was the most precise model for predicting AKI, with an area under the curve of 0.90±0.12.
CONCLUSIONS:Machine learning models can precisely predict AKI during early stages after surgery, which allows vascular surgeons to address complications earlier and may help improve the clinical outcomes of OSR.
