MeSsAGe risk score: tool for renal biopsy decision in steroid-dependent nephrotic syndrome
CY Chan, LP Resontoc, MA Qader, YH Chan… - Pediatric …, 2019 - nature.com
Pediatric Research, 2019•nature.com
Background A lack of consensus exists as to the timing of kidney biopsy in children with
steroid-dependent nephrotic syndrome (SDNS) where minimal change disease (MCD)
predominates. This study aimed at examining the applicability of a biomarker-assisted risk
score model to select SDNS patients at high risk of focal segmental glomerulosclerosis
(FSGS) for biopsy. Methods Fifty-five patients with SDNS and biopsy-proven MCD (n= 40) or
FSGS (n= 15) were studied. A risk score model was developed with variables consisting of …
steroid-dependent nephrotic syndrome (SDNS) where minimal change disease (MCD)
predominates. This study aimed at examining the applicability of a biomarker-assisted risk
score model to select SDNS patients at high risk of focal segmental glomerulosclerosis
(FSGS) for biopsy. Methods Fifty-five patients with SDNS and biopsy-proven MCD (n= 40) or
FSGS (n= 15) were studied. A risk score model was developed with variables consisting of …
Background
A lack of consensus exists as to the timing of kidney biopsy in children with steroid-dependent nephrotic syndrome (SDNS) where minimal change disease (MCD) predominates. This study aimed at examining the applicability of a biomarker-assisted risk score model to select SDNS patients at high risk of focal segmental glomerulosclerosis (FSGS) for biopsy.
Methods
Fifty-five patients with SDNS and biopsy-proven MCD (n = 40) or FSGS (n = 15) were studied. A risk score model was developed with variables consisting of age, sex, eGFR, suPAR levels and percentage of CD8+ memory T cells. Following multivariate regression analysis, total risk score was calculated as sum of the products of odds ratios and corresponding variables. Predictive cut-off point was determined using receiver operator characteristics (ROC) curve analysis.
Results
Plasma suPAR levels in FSGS patients were significantly higher, while percentage of CD45RO+CD8+CD3+ was significantly lower than in MCD patients and controls. ROC analysis suggests the risk score model with threshold score of 16.7 (AUC 0.84, 95% CI 0.72–0.96) was a good predictor of FSGS on biopsy. The 100% PPV cut-off was >24.0, while the 100% NPV was <13.3.
Conclusion
A suPAR and CD8+ memory T cell percentage-based risk score model was developed to stratify SDNS patients for biopsy and for predicting FSGS.
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