No abstract

SummaryDonor HLA‐specific antibodies (DSAs) can cause rejection and graft loss after renal transplantation, but their levels measured by the current assays are not fully predictive of outcomes. We investigated whether IgG subclasses of DSA were associated with early rejection and graft failure. DSA levels were determined pretreatment, at the day of peak pan‐IgG level and at 30 days post‐transplantation in eighty HLA antibody‐incompatible kidney transplant recipients using a modified microbead assay. Pretreatment IgG4 levels were predictive of acute antibody‐mediated rejection (P = 0.003) in the first 30 days post‐transplant. Pre‐treatment presence of IgG4 DSA (P = 0.008) and day 30 IgG3 DSA (P = 0.03) was associated with poor graft survival. Multivariate regression analysis showed that in addition to pan‐IgG levels, total IgG4 levels were an independent risk factor for early rejection when measured pretreatment, and the presence of pretreatment IgG4 DSA was also an independent risk factor for graft failure. Pretreatment IgG4 DSA levels correlated independently with higher risk of early rejection episodes and medium‐term death‐censored graft survival. Thus, pretreatment IgG4 DSA may be used as a biomarker to predict and risk stratify cases with higher levels of pan‐IgG DSA in HLA antibody‐incompatible transplantation. Further investigations are needed to confirm our results.

HLA antibody-incompatible renal transplantation had a high success rate if the CDC XM was negative. Further work is required to predict which CDC+ve XM grafts will be successful and to treat slowly progressive graft damage because of DSA in the first few years after transplantation.

Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

No abstract

These data suggest that the dominant method of successful transplantation was function of the transplant in the presence of circulating DSA, and they also define the period during which this occurred.

Double filtration plasmapheresis (DFPP) was used in preference to plasma exchange in our program of antibody-incompatible transplantation, to treat higher volumes of plasma. Forty-two patients had 259 sessions of DFPP, 201 pre-transplant and 58 post-transplant. At the first treatment session, the mean plasma volume treated was 3.81 L (range 3-6 L), 55.5 mL/kg (range 36.2-83.6 mL/kg). Serum IgG fell by mean 59.4% (SD 10.2%), and IgM by 69.3% (SD 16.1%). Nine patients did not require increases in plasma volumes treated, and six did not tolerate higher plasma volumes. In the remaining patients, the mean maximum plasma volume treated pre-transplant was 6.67 L (range 4-15 L), 96.1 mL/kg (range 60.2-208.9 mL/kg). The complement dependent cytotoxic crossmatch was positive in 14 cases pre-treatment, and remained positive in six (42.8%) cases. The flow cytometric crossmatch was positive in 29 cases pre-treatment, and in 21 (72.4%) after DFPP. Post-transplant, DFPP was ineffective at reducing donor specific antibody levels during periods of rapid donor specific antibody synthesis. Post-transplant, the one year graft survival rate was 94%, although there was a high rate of early rejection. In summary, DFPP enabled the treatment of plasma volumes that were almost double those that would have been feasible with plasma exchange. Despite this, most patients were transplanted with a positive crossmatch, and DFPP post-transplant was unable to control rising antibody levels.

DSA levels may change markedly in the first month after antibody incompatible transplantation, and the risk of rejection was associated with higher pretreatment and peak levels.