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Updated: Apr 23, 2026
Evaluation of Fluid Overload by Bioelectrical Impedance Vectorial Analysis
Published on: August 17, 2022
Explainable machine learning integrating bioelectrical impedance for 6-month cardiovascular risk in peritoneal
Yi Liang Tsai1,2, Chia Lin Wu1,3,4,5, Jung Hsien Chiang2
1Department of Medical Research, Renal Medicine Laboratory, Changhua Christian Hospital, Changhua, Taiwan.
Insights
An artificial intelligence model integrating bioelectrical impedance spectroscopy (BIS) and medical history can predict major adverse cardiovascular events (MACEs) in peritoneal dialysis (PD) patients. This AI tool enhances cardiovascular risk prediction for PD patients, improving clinical management.
Area of Science:
- Nephrology
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Cardiovascular disease (CVD) is a leading cause of morbidity in patients undergoing peritoneal dialysis (PD) for end-stage renal disease.
- Inadequate fluid management in PD patients is a significant risk factor for CVD.
- Bioelectrical impedance spectroscopy (BIS) is a valuable tool for assessing fluid status in PD patients.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) model for predicting major adverse cardiovascular events (MACEs) within six months in clinically stable PD patients.
- To integrate bioelectrical impedance spectroscopy (BIS) measurements with patient medical history for enhanced cardiovascular risk prediction.
- To evaluate the performance of different AI algorithms in predicting MACEs in this patient population.
Main Methods:
- Development of an AI model using logistic regression, random forest, XGBoost, and deep neural networks.
- Training and testing data split (80% training, 20% testing) with synthetic minority over-sampling technique for class imbalance.
- Model performance evaluation using area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA); feature ablation studies were conducted.
Main Results:
- The random forest model demonstrated the highest performance with an AUC of 0.88.
- Cardiovascular disease (CVD) history was identified as the most influential predictor.
- Integrating BIS features significantly improved diagnostic sensitivity from 0.68 to 0.84, highlighting the value of fluid status assessment.
Conclusions:
- A 15-feature random forest AI model, incorporating BIS measurements and medical history, accurately predicts 6-month MACE risk in stable PD patients.
- The model shows strong discriminatory ability and potential clinical utility as confirmed by DCA.
- Combining BIS data with medical history offers a significant improvement in predicting cardiovascular events for PD patients.
Abstract:
Peritoneal dialysis (PD) is a common treatment for end-stage renal disease, yet cardiovascular disease (CVD) remains a major cause of morbidity. Inadequate fluid management can elevate CVD risk, and bioelectrical impedance spectroscopy (BIS) is increasingly used to assess fluid status. This study aimed to develop an artificial intelligence model integrating BIS measurements and medical history to predict major adverse cardiovascular events (MACEs) within six months in clinically stable PD patients. Data were stratified by MACE occurrence and subject, with 80% assigned to training and 20% to testing. Class imbalance was addressed using the synthetic minority over-sampling technique. Four algorithms - logistic regression, random forest, XGBoost, and deep neural networks - were trained using fivefold cross-validation and grid search for optimal hyperparameters. Model performance was evaluated with area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Feature ablation experiments compared models using all 15 features, only 11 BIS-recorded features, and only 4 medical history features. The random forest model achieved the highest performance (AUC = 0.88), with CVD history as the most influential predictor. Isotonic regression calibration improved probability alignment (brier score = 0.0451). DCA suggested potential clinical benefit. While the model relied on four medical history features, its initial sensitivity was modest (0.68). Integrating BIS features significantly enhanced diagnostic sensitivity (0.84). The random forest model, based on 15 clinically accessible features, accurately predicts the risk of MACE within six months in clinically stable PD patients, demonstrating strong discriminatory ability with a performance reaching 88%.
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