Related Experiment Videos
Risk prediction of pediatric intensive care unit admission in children with respiratory syncytial virus infection
Junyu Dong1, Jingwen Ni1, Mengxin Zhao1
1PICU of Luoyang Maternal and Child Health Hospital, Luoyang, Henan, China.
Insights
This study developed a machine learning model to predict pediatric intensive care unit (PICU) transfer in children with respiratory syncytial virus (RSV) infections. The model shows promise for early clinical decision-making and resource allocation.
Area of Science:
- Pediatric critical care medicine
- Infectious diseases
- Machine learning in healthcare
Background:
- Respiratory syncytial virus (RSV) is a leading cause of severe respiratory illness in children.
- Predicting pediatric intensive care unit (PICU) admission is crucial for timely clinical decisions and resource management.
- Interpretable machine learning offers a novel approach to predicting PICU transfer in pediatric RSV cases.
Purpose of the Study:
- To develop and validate an interpretable machine learning model for predicting PICU admission in hospitalized children with RSV.
- To identify key clinical and laboratory predictors of PICU transfer.
- To assess the model's performance and clinical utility through rigorous validation methods.
Main Methods:
- Inclusion of hospitalized children (29 days-18 years) with confirmed RSV.
- Development of a machine learning model using day-one clinical and laboratory data.
- Internal validation via random split-sample and 10-fold cross-validation; temporal external validation.
- Evaluation using AUROC, average precision, classification metrics, calibration curves, and decision curve analysis; SHAP for interpretation.
Main Results:
- A random forest model identified dyspnea, serum ferritin, wheezing, immunoglobulin G, interleukin-6, preterm birth, and personal history of wheezing as key predictors.
- The model achieved high performance in internal testing (AUROC 0.94) and temporal external validation (AUROC 0.92).
- SHAP analysis indicated model interpretability, and decision curve analysis suggested potential clinical utility.
Conclusions:
- An interpretable random forest model effectively predicts PICU transfer in pediatric RSV patients.
- The model demonstrates strong performance and potential clinical applicability.
- Further validation across diverse settings is recommended prior to widespread clinical implementation.
Background:
Respiratory syncytial virus (RSV) is a major cause of pediatric acute lower respiratory infection. Early prediction of pediatric intensive care unit (PICU) transfer may support clinical decision-making and resource allocation. We aimed to develop and temporally validate an interpretable machine learning model for predicting PICU admission in hospitalized children with RSV.
Methods:
We included children aged 29 days-18 years with laboratory-confirmed RSV at a single center (January 2023-April 2025). The prediction target was PICU admission within 48 h of hospital admission. Internal validation was performed using random split-sample (80% training / 20% internal test set) and 10-fold cross-validation during hyperparameter tuning. The development cohort (January 2023-March 2025; n = 1,606, 209 PICU admissions) was randomly split into training (80%, n = 1,285, 167 events) and internal test (20%, n = 321, 42 events) sets. A separate temporal external validation cohort (April 2025; n = 94, 12 events) from the same institution was used for temporal external validation. Ten machine learning algorithms were trained using day-one clinical and laboratory variables and evaluated by AUROC, average precision, classification metrics, calibration curves, and decision curve analysis. SHAP was used for interpretation.
Results:
Among 1,606 children, 209 required PICU admission. Final predictors included dyspnea, serum ferritin, wheezing, immunoglobulin G, interleukin-6, preterm birth, and personal history of wheezing. Random forest performed best among ten algorithms, with AUROC 0.94, average precision 0.87, accuracy 0.95, precision 0.86, recall 0.76, and F1 score 0.81 in the internal test set. In the temporal external validation cohort, the model achieved an AUROC of 0.92, average precision of 0.82, accuracy of 0.95, precision of 0.94, recall of 0.68, and F1 score of 0.79. Calibration plots, decision curve analysis, and SHAP analysis suggested acceptable calibration, potential clinical utility, and interpretability.
Conclusion:
We developed an interpretable machine learning model to predict PICU transfer in children with RSV. The random forest model performed well, but development estimates may remain optimistic without bootstrap-based optimism correction, and the small temporal external validation cohort limits precision. Further geographical, institutional, and healthcare-system validation is needed before clinical implementation.
Related Concept Videos
01:29Respiratory Syncytial Virus Disease
01:20Assessment of Ventilation I: Respiratory Rate
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation: