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The challenges for developing prognostic prediction models for acute kidney injury in hospitalized children: A
Chen Wang1,2, Xiaohang Liu1, Chao Zhang1
1Center for Clinical Epidemiology and Evidence-based Medicine Beijing Children's Hospital, Capital Medical University, National Center for Children's Health Beijing China.
Insights
This study reviewed prognostic prediction models for acute kidney injury (AKI) in children. Most models had high risk of bias and lacked validation, indicating a need for improved development and implementation in clinical practice.
Area of Science:
- Pediatric Nephrology
- Clinical Prediction Models
- Biostatistics
Background:
- Acute kidney injury (AKI) is a significant concern in hospitalized children, potentially leading to chronic kidney disease.
- Early diagnosis and intervention are crucial for improving outcomes in pediatric AKI.
- Prognostic prediction models aim to identify children at risk for AKI and guide timely management.
Purpose of the Study:
- To systematically appraise existing prognostic prediction models for pediatric AKI.
- To identify the strengths and limitations of current models.
- To inform future development and clinical application of these tools.
Main Methods:
- Systematic search of English and Chinese databases (January 2010 - June 2022).
- Inclusion of studies developing prognostic prediction models for pediatric AKI.
- Data extraction and risk of bias assessment using established guidelines (CRC, PROBAST).
Main Results:
- Eight studies with 16 models were included, all exhibiting significant reporting deficiencies and high risk of bias.
- Model performance varied (AUC 0.69-0.95), but only one-third had internal/external validation.
- Limited calibration, bedside usability, and clinical impact evaluation were reported for most models.
Conclusions:
- Current prognostic prediction models for pediatric AKI are limited by poor reporting and lack of rigorous validation.
- Challenges include handling age-dependent biochemical data and ensuring external applicability.
- Future research should prioritize robust model development, external validation, and integration with electronic health records for clinical decision support.
Importance:
Acute kidney injury (AKI) is common in hospitalized children which could rapidly progress into chronic kidney disease if not timely diagnosed. Prognostic prediction models for AKI were established to identify AKI early and improve children's prognosis.
Objective:
To appraise prognostic prediction models for pediatric AKI.
Methods:
Four English and four Chinese databases were systematically searched from January 1, 2010, to June 6, 2022. Articles describing prognostic prediction models for pediatric AKI were included. The data extraction was based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. The risk of bias (ROB) was assessed according to the Prediction model Risk of Bias Assessment Tool guideline. The quantitative synthesis of the models was not performed due to the lack of methods regarding the meta-analysis of prediction models.
Results:
Eight studies with 16 models were included. There were significant deficiencies in reporting and all models were considered at high ROB. The area under the receiver operating characteristic curve to predict AKI ranged from 0.69 to 0.95. However, only about one-third of models have completed internal or external validation. The calibration was provided only in four models. Three models allowed easy bedside calculation or electronic automation, and two models were evaluated for their impacts on clinical practice.
Interpretation:
Besides the modeling algorithm, the challenges for developing prediction models for pediatric AKI reflected by the reporting deficiencies included ways of handling baseline serum creatinine and age-dependent blood biochemical indexes. Moreover, few prediction models for pediatric AKI were performed for external validation, let alone the transformation in clinical practice. Further investigation should focus on the combination of prediction models and electronic automatic alerts.
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