Risk prediction models for renal injury in children with immunoglobulin A (IgA) vasculitis: a systematic review and meta-analysis
Authors' conclusion
In spite of the fact that the renal injury prediction model was found to be somewhat predictive in children with IgAV, all of them had a high risk of bias according to the PROBAST checklist.
Study context
- Population
- Studies on the construction of predictive models for renal injury in children with IgA vasculitis (IgAV)11 studies
- Outcome
- Pooled AUC of renal injury prediction models
Methodological detailsPROBAST risk-of-bias assessment, Most common predictors: age, recurrent or persistent purpura, IgA, D-dimer, serum albumin
- PROBAST risk-of-bias assessment
- Most common predictors: age, recurrent or persistent purpura, IgA, D-dimer, serum albumin
Pooled result
As reported in the abstract
Pooled AUC of five models
Pooled AUC (95% CI: 0.80-0.92), good predictive power
0.86
Further pooled results
Renal injury incidence across included studies
- Renal injury incidence
- 26.7-63.8%
- Sample sizes ranged
- 155 to 583
Risk of bias (PROBAST)
- Good overall applicability, but all studies highly biased (inadequate data sources, poor reporting of analysis)
Original publication
Risk prediction models for renal injury in children with IgA vasculitis: a systematic review and meta-analysis. Pediatr Rheumatol Online J. Published 28 July 2025. doi:10.
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