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Validation of the children international IgA nephropathy prediction tool based on data in Southwest China
Xixi Yu1, Jiacheng Li2, Chengrong Tao1
1Department of Nephrology, Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, Chongqing, China.
Insights
An international prediction tool for childhood Immunoglobulin A nephropathy (IgAN) showed limited effectiveness in a Chinese cohort. Further research is needed to develop tailored predictive models for pediatric IgAN patients in China.
Area of Science:
- Nephrology
- Pediatric kidney disease
- Predictive modeling in medicine
Background:
- Immunoglobulin A nephropathy (IgAN) is a common cause of pediatric kidney injury, with a significant progression rate to end-stage kidney disease (ESKD).
- Early prediction and intervention are crucial for managing childhood IgAN and preventing long-term renal damage.
- This study aimed to validate an existing international predictive tool for IgAN in a cohort of Chinese children.
Purpose of the Study:
- To assess the predictive performance of an international IgAN prediction tool in a cohort of Chinese children.
- To evaluate the tool's accuracy with and without considering racial differences.
- To determine the applicability of existing adult-derived IgAN prediction models to pediatric populations.
Main Methods:
- An external validation cohort of 210 Chinese children diagnosed with IgAN was established.
- The predictive performance of two models (with and without race) was assessed using area under the curve (AUC), prediction interval (PI), survival analysis, and R2D.
- Key outcome measures included a 30% decrease in glomerular filtration rate (GFR) or progression to ESKD.
Main Results:
- The AUC for the model with race was 0.685 and without race was 0.640, indicating moderate predictive ability.
- Both models demonstrated significant prediction intervals (PI > 0.751) but struggled to effectively differentiate between low-risk and high-risk groups in survival analyses.
- Model fit was 66.5% with race and 56.2% without race, suggesting limitations in applicability.
Conclusions:
- The international IgAN prediction tool, primarily based on adult data, showed suboptimal performance in this cohort of Chinese children.
- Discrepancies in demographic, clinical, and pathological characteristics between derivation and validation cohorts may explain the limited applicability.
- Development of novel, tailored prediction models using specific data from Chinese pediatric IgAN populations is recommended.
Background:
Immunoglobulin A nephropathy (IgAN) is one of the most common kidney diseases leading to renal injury. Of pediatric cases, 25%-30% progress into end-stage kidney disease (ESKD) in 20-25 years. Therefore, predicting and intervening in IgAN at an early stage is crucial. The purpose of this study was to validate the availability of an international predictive tool for childhood IgAN in a cohort of children with IgAN treated at a regional medical centre.
Methods:
An external validation cohort of children with IgAN from medical centers in Southwest China was formed to validate the predictive performance of the two full models with and without race differences by comparing four measures: area under the curve (AUC), the regression coefficient of linear prediction (PI), survival analysis curves for different risk groups, and R2D.
Results:
A total of 210 Chinese children, including 129 males, with an overall mean age of 9.43 ± 2.71 years, were incorporated from this regional medical center. In total, 11.43% (24/210) of patients achieved an outcome with a GFR decrease of more than 30% or reached ESKD. The AUC of the full model with race was 0.685 (95% CI: 0.570-0.800) and the AUC of the full model without race was 0.640 (95% CI: 0.517-0.764). The PI of the full model with race and without race was 0.816 (SE = 0.006, P < 0.001) and 0.751 (SE = 0.005, P < 0.001), respectively. The results of the survival curve analysis suggested the two models could not well distinguish between the low-risk and high-risk groups (P = 0.359 and P = 0.452), respectively, no matter the race difference. The evaluation of model fit for the full model with race was 66.5% and without race was 56.2%.
Conclusions:
The international IgAN prediction tool has risk factors chosen based on adult data, and the validation cohort did not fully align with the derivation cohort in terms of demographic characteristics, clinical baseline levels, and pathological presentation, so the tool may not be highly applicable to children. We need to build IgAN prediction models that are more applicable to Chinese children based on their particular data.
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