Value of Donor-Specific Anti-HLA Antibody Monitoring and Characterization for Risk Stratification of Kidney Allograft Loss

Abstract

The diagnosis system for allograft loss lacks accurate individual risk stratification on the basis of donor-specific anti-HLA antibody (anti-HLA DSA) characterization. We investigated whether systematic monitoring of DSA with extensive characterization increases performance in predicting kidney allograft loss. This prospective study included 851 kidney recipients transplanted between 2008 and 2010 who were systematically screened for DSA at transplant, 1 and 2 years post-transplant, and the time of post-transplant clinical events. We assessed DSA characteristics and performed systematic allograft biopsies at the time of post-transplant serum evaluation. At transplant, 110 (12.9%) patients had DSAs; post-transplant screening identified 186 (21.9%) DSA-positive patients. Post-transplant DSA monitoring improved the prediction of allograft loss when added to a model that included traditional determinants of allograft loss (increase in c statistic from 0.67; 95% confidence interval [95% CI], 0.62 to 0.73 to 0.72; 95% CI, 0.67 to 0.77). Addition of DSA IgG3 positivity or C1q binding capacity increased discrimination performance of the traditional model at transplant and post-transplant. Compared with DSA mean fluorescence intensity, DSA IgG3 positivity and C1q binding capacity adequately reclassified patients at lower or higher risk for allograft loss at transplant (category-free net reclassification index, 1.30; 95% CI, 0.94 to 1.67; P<0.001 and 0.93; 95% CI, 0.49 to 1.36; P<0.001, respectively) and post-transplant (category-free net reclassification index, 1.33; 95% CI, 1.03 to 1.62; P<0.001 and 0.95; 95% CI, 0.62 to 1.28; P<0.001, respectively). Thus, pre- and post-transplant DSA monitoring and characterization may improve individual risk stratification for kidney allograft loss.

Keywords: immunology; kidney transplantation; transplant outcomes.

Figure 1.

Prospective post-transplant anti-HLA DSA screening using single-antigen Luminex technique identified 110/851 (12.9%) patients with circulating anti–HLA DSA at the time of transplantation and 186/851 (21.9%) patients with circulating anti-HLA DSA after transplantation. Tx, transplant.

Figure 2.

Hierarchical ranking of anti–HLA iDSA characteristics on the basis of their ability to classify patients according to their risk of allograft loss using random survival forest modeling. (A) At the time of transplantation (n=110). (B) Post-transplantation (n=186).

Figure 3.

Predictive value for allograft loss of a strategy on the basis of a systematic monitoring of anti-HLA DSAs and integration of anti–HLA DSA characteristics in an unselected population of kidney transplant recipients (n=851). Predictive value for allograft loss was assessed by Cox model Harrell c statistics in the overall study population (n=851). Day 0 anti–HLA DSA characteristics (IgG3 positivity and C1q binding) were added to the day 0 reference model, which was on the basis of a conventional strategy. Post–transplant anti–HLA DSA characteristics (IgG3 positivity and C1q binding) were added to the post–Tx DSA model. In the day 0 reference model and the post–Tx DSA model, anti-HLA DSAs were detected using the single–antigen Luminex technique. A c statistic of 0.5 indicated that the model is no better than chance at predicting membership in a group, and a value of one indicates that the model perfectly identifies those within a group and those not in a group. Percentile 95% CIs for c statistics were derived using 1000 bootstrap samples. The differences in c statistics were replicated 1000 times using bootstrap samples to derive 95% CIs.

Figure 4.

Improvement in calculated risk of allograft loss by considering IgG3 and C1q binding anti–HLA DSA status in addition to anti–HLA DSA MFI level at (A) the time of transplantation and (B) post-transplantation. Improvement in calculated risk of allograft loss was assessed by the IDI. The IDI integrates the change in mean predicted probability of allograft loss in patients with allograft loss and those without allograft loss. The change in the mean predicted probability of allograft loss is adequate if it is positive for patients with allograft loss (increased calculated risk) and negative for those without allograft loss (decreased calculated risk). Tx, transplant.

Figure 5.

Individual additive value of IgG3 and C1q binding anti–HLA DSA status to MFI level for stratifying the risk of allograft loss at the time of transplantation ([A] IgG3 status and [B] C1q binding status) and post-transplantation ([C] IgG3 status and [D] C1q binding status). Additive value of IgG3 and C1q binding anti–HLA DSA status to MFI level was assessed by category-free NRI. The NRI integrates the direction of change in the probability of allograft loss for every individual. The change in individual calculated risk is in the correct direction if it is greater for patients with allograft loss and less for those without allograft loss. Blue lines in patients without allograft loss indicate that IgG3 and C1q binding anti–HLA iDSA status moved the individual predicted probability of allograft loss in the correct (downward) direction. Red lines in patients with allograft loss indicate a correct (upward) change in the predicted probability of allograft loss when adding IgG3 and C1q binding anti–HLA iDSA status to anti–HLA iDSA MFI level. Pts, patients; Tx, transplant.