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Development and validation of bleeding prediction model for percutaneous liver biopsy in children
Yuyan Huang1, Yiwen Zhou1, Xiaofeng Xu1
1Department of Hepatology, Children's Hospital of Fudan University, 399 Wanyuan Road, Shanghai, Minhang District, 201102, China.
Insights
This study developed a predictive model for percutaneous liver biopsy bleeding in children, identifying key risk factors. The model shows moderate accuracy and requires further validation to improve clinical application for pediatric liver biopsy safety.
Area of Science:
- Pediatric Gastroenterology
- Interventional Radiology
- Biostatistics
Background:
- Percutaneous liver biopsy is essential for diagnosing pediatric liver diseases.
- Bleeding is a significant complication, necessitating risk stratification.
- Current prediction methods for bleeding risk in children are limited.
Purpose of the Study:
- To identify factors influencing bleeding after percutaneous liver biopsy in children.
- To develop and validate a risk prediction model for this complication.
- To enhance the safety of pediatric liver biopsy procedures.
Main Methods:
- Retrospective analysis of clinical data from electronic medical records.
- Feature selection using Lasso regression and logistic regression modeling.
- Internal validation using ROC curves, calibration curves, and decision curve analysis.
Main Results:
- The incidence of bleeding was 13.3%, with most cases being minor.
- Seven independent risk factors were identified: Pre-Corticosteroid, Post Liver Transplantation, Needle Depth, ALT, PT, PLT, and GPR.
- The prediction model demonstrated moderate discriminative ability with AUCs of 0.720 (training) and 0.700 (validation).
Conclusions:
- A novel risk prediction model for percutaneous liver biopsy bleeding in children was developed and internally validated.
- The model shows moderate predictive performance, indicating potential clinical utility.
- Further external validation and multi-center studies are recommended to improve accuracy and applicability.
Objective:
To evaluate the current status and factors influencing the occurrence of percutaneous liver biopsy bleeding in children through a retrospective study, and to develop and validate a risk prediction model to reduce the incidence of percutaneous liver biopsy bleeding in children. METHODS: From the hospital's electronic medical record system, clinical data of the study subjects were obtained during their hospitalization. Continuous variables were described using the median (interquartile range), while categorical variables were described using frequencies, proportions, and rates. Feature variables were screened using Lasso regression, and the data were divided into training and validation sets in a 7:3 ratio. Variables with statistically significant differences were included in a binary logistic regression model, and a risk prediction model was constructed using stepwise bidirectional regression. The model was visualized using a nomogram and internally validated. The ROC curve was used to assess the model's discriminative ability, the calibration curve to evaluate its calibration, and the decision curve analysis to assess its clinical decision-making capability.
Results:
The incidence of bleeding in this study was 13.3%, most of which were minor and did not cause serious complications. Variables with meaningful Lasso regression coefficients were included in the multivariate logistic regression analysis, and the stepwise bidirectional regression ultimately yielded seven independent influencing factors: Pre-Corticosteroid, Post Liver Transplantation, Needle Depth, ALT, PT, PLT, and GPR. These factors will be used to construct a prediction model for percutaneous liver biopsy bleeding in children. In this study, the training set AUC was 0.720, with a 95% CI of 0.675-0.765, and the validation set AUC was 0.700, with a 95% CI of 0.633-0.767.
Conclusion:
This study created and internally tested a bleeding prediction model for children undergoing percutaneous liver biopsy, demonstrating moderate discriminative ability. Additional optimization and external validation are necessary. Expanding research with larger, multi-center datasets is crucial to enhancing the model's predictive accuracy and clinical applicability.
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