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The development and validation of a prediction model for post-AKI outcomes of pediatric inpatients
Chao Zhang1, Xiaohang Liu1, Ruohua Yan1
1Department of Clinical Epidemiology and Evidence-based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, China.
Insights
A new prediction model accurately identifies hospital mortality and dialysis risk in children with acute kidney injury (AKI). This tool aids early detection and management of pediatric AKI patients.
Area of Science:
- Pediatric Nephrology
- Clinical Informatics
- Biostatistics
Background:
- Acute kidney injury (AKI) is a frequent complication in hospitalized children.
- Early detection of AKI outcomes is crucial for timely intervention in pediatric patients.
- A predictive model can facilitate proactive management strategies for pediatric AKI.
Purpose of the Study:
- To develop and validate a prediction model for post-acute kidney injury (AKI) outcomes in hospitalized children.
- To assess the model's ability to predict hospital mortality and the need for dialysis within 28 days of AKI onset.
- To compare the model's performance against the Pediatric Critical Illness Score (PCIS) for mortality risk stratification.
Main Methods:
- Retrospective analysis of 8205 pediatric AKI cases from two Chinese hospitals.
- Genetic Algorithm for feature selection and Random Forest model development.
- Temporal and external validation of the prediction model's performance.
Main Results:
- The model demonstrated high accuracy in predicting hospital mortality (AUROC 0.854) and dialysis (AUROC 0.889).
- Performance remained robust across temporal and external validation datasets.
- The proposed model significantly outperformed the PCIS in predicting mortality risk.
Conclusions:
- The developed post-AKI outcomes prediction model shows significant potential for clinical application.
- This model can aid in identifying high-risk pediatric AKI patients for targeted interventions.
- Further implementation and validation in diverse clinical settings are warranted.
Background:
Acute kidney injury (AKI) is common in hospitalized children. A post-AKI outcomes prediction model is important for the early detection of important clinical outcomes associated with AKI so that early management of pediatric AKI patients can be initiated.
Methods:
Three retrospective cohorts were set up based on two pediatric hospitals in China, in which 8205 children suffered AKI during hospitalization. Two clinical outcomes were evaluated, i.e. hospital mortality and dialysis within 28 days after AKI occurrence. A Genetic Algorithm was used for feature selection, and a Random Forest model was built to predict clinical outcomes. Subsequently, a temporal validation set and an external validation set were used to evaluate the performance of the prediction model. Finally, the stratification ability of the prediction model for the risk of mortality was compared with a commonly used mortality risk score, the pediatric critical illness score (PCIS).
Results:
The prediction model performed well for the prediction of hospital mortality with an area under the receiver operating curve (AUROC) of 0.854 [95% confidence interval (CI) 0.816-0.888], and the AUROC was >0.850 for both temporal and external validation. For the prediction of dialysis, the AUROC was 0.889 (95% CI 0.871-0.906). In addition, the AUROC of the prediction model for hospital mortality was superior to that of PCIS (P < .0001 in both temporal and external validation).
Conclusions:
The new proposed post-AKI outcomes prediction model shows potential applicability in clinical settings.
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