Optimal predicting model for cholecystocardiac syndrome in elderly patients based on machine learning algorithms and predicting factors
10.3760/cma.j.cn321463-20240708-00076
- VernacularTitle:基于机器学习算法的老年胆心综合征最优预测模型的筛选及预测因素分析
- Author:
Xiao CHEN
1
;
Hongchen ZHANG
;
Xiaochen ZHANG
;
Hongzhang SHEN
;
Xiaofeng ZHANG
Author Information
1. 浙江省天台县人民医院（浙江省人民医院天台分院）消化内科，台州 317200
- Publication Type:Journal Article
- Keywords:
Aged;
Cholecystocardiac syndrome;
Machine learning;
Early diagnosis;
Endoscopic treatment
- From:
Chinese Journal of Digestive Endoscopy
2025;42(9):715-721
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To identify the optimal machine learning model for predicting cholecystocardiac syndrome (CCS) in elderly patients and determine key predictive factors.Methods:A total of 150 elderly patients diagnosed as having CCS who presented with chest discomfort as the initial symptom were retrospectively included at Hangzhou First People's Hospital from July 2021 to December 2023, with 150 propensity score-matched controls (1∶1 matching for age, gender, smoking, and drinking history) with chest discomfort but without CCS during the same period. Demographic characteristics, vital signs at admission, laboratory test results, and electrocardiogram (ECG) findings of all patients were collected. The predictive performance of six machine learning models—gradient boosting, logistic regression, random forest (RF), k-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM) for early CCS recognition was compared. The model with the best overall performance was selected according to area under the curve (AUC), and its key predictive factors were identified based on feature importance ranking.Results:Among the six machine learning models, the RF model demonstrated the highest AUC (0.84) and lowest Brier score (0.165) and was therefore identified as the optimal model. In the independent test set, the RF model achieved an AUC of 0.87 (95% CI: 0.735-0.908), with a sensitivity of 86.7% (39/45), specificity of 80.0% (36/45), positive predictive value of 81.3% (39/48) and negative predictive value of 85.7% (36/42). The six most important predictive variables were procalcitonin (PCT), serum amyloid A (SAA), heart rate variability low-frequency to high-frequency ratio (LF/HF), percentage of successive normal RR intervals differing by >50 ms (PMN50), γ-glutamyltransferase/aspartate aminotransferase ratio (GGT/AST), and direct bilirubin (DBIL). Conclusion:The RF model can predict early risk of CCS in elderly patients, with the key predictive factors including PCT, SAA, LF/HF, PMN50, GGT/AST, and DBIL.
