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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Developing an explainable machine learning model using body composition to predict cardiovascular mortality in
Xiao-Xu Wang1, Jin-Xuan Wei2, Tian-Ke Yu2
1Department of Nephrology, Qilu Hospital of Shandong University, Shandong University, Jinan, China.
Insights
A new machine learning model uses CT scans to predict cardiovascular disease (CVD) deaths in dialysis patients. This tool aids early risk assessment for better prevention strategies at dialysis initiation.
Area of Science:
- Nephrology
- Cardiology
- Artificial Intelligence
Background:
- Cardiovascular disease (CVD) is the primary cause of mortality in patients undergoing dialysis.
- Accurate prediction of CVD risk at the initiation of dialysis is currently limited.
Purpose of the Study:
- To develop and validate a machine learning model for predicting CVD-related mortality in patients starting dialysis.
- To integrate computed tomography (CT)-derived body composition features into the predictive model.
Main Methods:
- Trained and validated eight machine learning algorithms using clinical, laboratory, and CT-derived body composition data from incident dialysis patients.
- Employed feature selection techniques (logistic regression, LASSO) and evaluated models using discrimination, calibration, and decision curve analysis.
- Utilized Shapley Additive Explanations (SHAP) for model interpretability and developed a web-based risk calculator.
Main Results:
- Identified eight key predictors: age, diabetes, CVD history, cardiac intervention history, dialysis modality, skeletal muscle density, hemoglobin, and serum creatinine.
- The CatBoost model achieved an area under the receiver operating characteristic curve of 0.843 (internal validation) and 0.799 (external validation).
- SHAP analysis highlighted CVD, skeletal muscle density, and hemoglobin as significant contributors to mortality prediction.
Conclusions:
- An explainable machine learning model integrating CT-derived body composition effectively predicts CVD-related mortality in incident dialysis patients.
- This model offers potential for early risk stratification and personalized preventive interventions upon dialysis initiation.
Introduction:
Cardiovascular disease (CVD) is the leading cause of death in patients receiving dialysis, and accurate risk prediction at dialysis initiation remains limited. We developed and validated a machine learning model integrating CT-derived body composition features to predict CVD-related mortality in initial dialysis patients.
Methods:
Patients initiating dialysis between 2014 and 2020 from three tertiary hospitals were used for model training and internal validation, with patients from a fourth center for external validation. Clinical characteristics and laboratory variables were collected, and body composition parameters were assessed using opportunistic CT scans. Feature selection was performed using univariable logistic regression and LASSO regression. Eight machine learning algorithms were trained, and model performance was assessed using discrimination, calibration, and decision curve analysis. Model interpretability was evaluated using Shapley Additive Explanations (SHAP), and a web-based risk calculator was developed.
Results:
Among 1051 incident dialysis patients, 645 were assigned to the training and internal validation cohorts and 406 to the external validation cohort. Eight key predictors were identified, including age, diabetes, CVD, history of cardiac intervention, dialysis modality, skeletal muscle density, hemoglobin, and serum creatinine. CatBoost demonstrated the best performance, with an area under the receiver operating characteristic curve of 0.843 in internal validation and 0.799 in external validation, along with good calibration and clinical net benefit. SHAP analysis identified CVD, skeletal muscle density, and hemoglobin as major contributors.
Discussion:
An explainable machine learning model incorporating CT-derived body composition features accurately predicts CVD-related mortality in initial dialysis patients. This model may facilitate early risk stratification and targeted prevention strategies at dialysis initiation.
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