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Development and validation of machine learning-based prediction model for central venous access device-related
Maoling Fu1, Xinyu Li1, Zhuo Wang1
1Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China; School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, 13 Aviation Road, Wuhan, Hubei 430030, China.
Insights
This study identified 11 risk factors for central venous access device (CVAD)-related thrombosis (CRT) in children. A logistic regression model effectively predicts CRT risk, aiding early intervention.
Area of Science:
- Pediatric Thrombosis Research
- Medical Informatics
- Clinical Risk Prediction
Background:
- Central venous access device (CVAD)-related thrombosis (CRT) is a significant complication.
- Effective risk identification and assessment tools for pediatric CRT are lacking.
- Early detection and prevention are crucial for managing CRT in children.
Purpose of the Study:
- Identify critical risk factors for CRT in pediatric patients.
- Develop and validate machine learning-based prediction models for pediatric CRT.
- Provide a foundation for predicting and preventing CRT in children.
Main Methods:
- Retrospective and prospective data collection from pediatric patients with CVADs (2018-2024).
- LASSO regression to identify independent risk factors for CRT.
- Development and validation of four machine learning models: Logistic Regression (LR), Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting.
Main Results:
- Overall CRT incidence was 17.4% in 1445 pediatric patients.
- Eleven independent risk factors were identified, including history of thrombosis, leukemia, catheter characteristics, and specific treatments.
- The LR model demonstrated superior performance in both internal and external validation.
Conclusions:
- Eleven independent risk factors for pediatric CRT were identified.
- A logistic regression-based prediction model shows high clinical applicability for early CRT risk assessment.
- The developed model offers valuable support for the prediction and prevention of CRT in children.
Background:
Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely preventive interventions and reducing the incidence of CRT. Approaches for identifying the risk of CRT in children have not been well-researched.
Objective:
To identify the critical risk factors for CRT in children and to construct machine learning-based prediction models tailored to this group, providing a theoretical basis and technical support for the prediction and prevention of CRT in these patients.
Study Design:
Retrospective data of pediatric patients receiving CVAD catheterization from January 1, 2018 to June 31, 2023 in Tongji Hospital were collected and divided into a training set and an internal validation set in a ratio of 7:3. Relevant data from July 1, 2023 to July 1, 2024 were prospectively collected for external validation of the model. LASSO regression was applied to determine CRT independent risk factors. Subsequently, four prediction models were constructed using logistic regression (LR), random forest, artificial neural network, and eXtreme Gradient Boosting.
Results:
A total of 1445 children were included in this study and the overall incidence of CRT was 17.4 %. The LASSO regression screened out 11 critical variables, including history of thrombosis, leukemia, number of catheters, history of catheterization, chemotherapy, parenteral nutrition, mechanical prophylaxis, dialysis, hypertonic liquid, anticoagulants, and post-catheterization D-dimer. The LR model outperformed the other models in both internal and external validation and was considered the best model for this study, which was transformed into a nomogram.
Conclusions:
This study identified 11 independent risk factors for CRT in children. The prediction model developed using LR algorithm demonstrated excellent clinical applicability and may provide valuable support for early prediction of CRT.
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