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Machine learning model for predicting severe infection in children with idiopathic nephrotic syndrome: multicenter
Sijie Yu1, Wenhao Tang1, De Zhang2
1Department of Nephrology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan 2nd Road, Yuzhong District, Chongqing, China.
Insights
Machine learning accurately predicts severe infections in children with idiopathic nephrotic syndrome (INS). This tool aids early detection, improving patient outcomes by identifying high-risk individuals for prompt intervention.
Area of Science:
- Pediatric Nephrology
- Infectious Diseases
- Machine Learning in Healthcare
Background:
- Infection is a significant complication in children with idiopathic nephrotic syndrome (INS).
- Early identification of severe infections is crucial for improving patient outcomes in INS.
Purpose of the Study:
- To develop and validate machine learning (ML) models for predicting severe infection in pediatric INS patients.
- To identify key predictors for severe infection in this vulnerable population.
Main Methods:
- A multicenter retrospective study involving 2357 patients for model derivation and 372 for external validation.
- Data from 41 variables were analyzed; 10 were selected using univariate analysis and LASSO regression.
- Ten ML models were compared, with the best selected via ROC analysis.
Main Results:
- The Light Gradient Boosting Machine (LightGBM) model demonstrated superior predictive performance (AUROC: 0.912, AUPRC: 0.915 in derivation; AUROC: 0.979, AUPRC: 0.842 in validation).
- Key predictors included C-reactive protein, hemoglobin, white blood cell count, and immunosuppressant use.
- The model achieved high accuracy (0.843) and sensitivity (0.842) in predicting severe infections.
Conclusions:
- The developed LightGBM model shows excellent performance in predicting severe infections in children with INS.
- This ML tool offers a potentially effective, convenient, and cost-effective method for early infection detection.
Background:
Infection is a common complication of idiopathic nephrotic syndrome (INS), and early identification of severe infection can improve patient outcome.
Methods:
This multicenter retrospective study developed and validated machine learning (ML) models that predict severe infection in children with INS. The derivation cohort (n = 2357) consisted of INS patients at one institution, and was separated into a training set and testing set. The external validation set (n = 372) consisted of INS patients from three other hospitals. Data were collected for 41 variables, and ten of them were then selected by univariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Ten ML models were compared, and the best one was identified using receiver operating characteristic (ROC) analysis and other methods.
Results:
The incidence rate of severe infection was 6.8% in the derivation cohort. The Light Gradient Boosting Machine (LightGBM) model had the best predictive performance (accuracy: 0.843, precision: 0.843, recall: 0.842, F1: 0.843, sensitivity: 0.842, specificity: 0.844, AUROC:0.912, AUPRC:0.915). The ten predictors were C-reactive protein, hemoglobin, white blood cells, activated partial thromboplastin time, creatinine, high-density lipoprotein, corrected serum calcium, complement 3, and number of immunosuppressants, and incidence of SRNS. This model had an AUROC of 0.979 and AUPRC of 0.842 in the external validation cohort.
Conclusion:
A LightGBM model for predicting severe infection in patients with INS had excellent performance. Future applications of this model may provide an effective, convenient, and cost-effective approach for early identification of severe infection in children with INS.
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