Published January 30, 2024 | Version v1
Journal article Open

A novel prognostic nomogram predicts premature failure of kidney allografts with IgA nephropathy recurrence

  • 1. Institute for Clinical & Experimental Medicine (IKEM) Transplant Ctr, Dept Nephrol, Prague
  • 2. Charles Univ Prague, Med Fac 1, Prague,
  • 3. Oslo Univ Hosp, Dept Nephrol, Oslo,
  • 4. Institute for Clinical & Experimental Medicine (IKEM) Transplant Lab, Prague
  • 5. Institute for Clinical & Experimental Medicine (IKEM) Dept Informat, Prague
  • 6. Institute for Clinical & Experimental Medicine (IKEM) Transplant Pathol Ctr

Description

Background

Recurrence of immunoglobulin A nephropathy (IgAN) limits graft survival in kidney transplantation. However, predictors of a worse outcome are poorly understood.

Methods

Among 442 kidney transplant recipients (KTRs) with IgAN, 83 (18.8%) KTRs exhibited biopsy-proven IgAN recurrence between 1994 and 2020 and were enrolled in the derivation cohort. A multivariable Cox model predicting allograft loss based on clinical data at the biopsy and a web-based nomogram were developed. The nomogram was externally validated using an independent cohort (n = 67).

Results

Patient age <43 years {hazard ratio [HR] 2.20 [95% confidence interval (CI) 1.41–3.43], P < .001}, female gender [HR 1.72 (95% CI 1.07–2.76), P = .026] and retransplantation status [HR 1.98 (95% CI 1.13–3.36), P = .016] were identified as independent risk factors for IgAN recurrence. Patient age <43 years [HR 2.77 (95% CI 1.17–6.56), P = .02], proteinuria >1 g/24 hours [HR 3.12 (95% CI 1.40–6.91), P = .005] and C4d positivity [HR 2.93 (95% CI 1.26–6.83), P = .013] were found to be associated with graft loss in patients with IgAN recurrence. A nomogram predicting graft loss was constructed based on clinical and histological variables, with a C statistic of 0.736 for the derivation cohort and 0.807 for the external validation cohort.

Conclusions

The established nomogram identified patients with recurrent IgAN at risk for premature graft loss with good predictive performance.

Notes

This research was supported by the Ministry of Health of the 
Czech Republic under grants NV19-06-00031 and NU21-06-00021 
and the National Institute for Research of Metabolic and Car- 
diovascular Diseases project (Programme EXCELES, Project No. 
LX22NPO5104), funded by the European Union Next Generation 
EU. 

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