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Abstract: SA-PO1164

Kinetic Characteristics and Validation of a Patient-Based Model for Therapeutic Plasma Exchange in Transplant Patients

Session Information

Category: Transplantation

  • 1902 Transplantation: Clinical

Authors

  • Galindo, Pablo E., Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, CDMX, Mexico
  • Marino-Vazquez, Lluvia A., Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, CDMX, Mexico
  • Mejia, María Fernanda, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, CDMX, Mexico
  • Correa-Rotter, Ricardo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, CDMX, Mexico
  • Morales-Buenrostro, Luis E., Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, CDMX, Mexico
Background

Therapeutic plasma exchange (TPE) has become an important tool in kidney transplantation. The American Society of Apheresis gives TPE a category 1 indication for antibody mediated rejection (AMR). Understanding the kinetics of macromolecule removal is fundamental for rational prescription, optimization, and recognizing limitations of the TPE.

Methods

We evaluated 12 patients with biopsy confirmed AMR, who had indication for TPE. Each patient received 5 treatments every other day with 1.5 plasma exchanges, and all treatments where replaced with 5% albumin solution. Considering that Luminex is not a quantitative assay, we measured immunoglobulins (IgG, IgA, IgM) by immunoturbidimetry, and LDL cholesterol before and after each treatment. By knowing the initial value of macromolecules, their intravascular distribution, and the reduction ratio, we calculated the intravascular refill between treatments. Refill was independent of time, and constant for each patient through all treatments. We also identified three refill patterns. With this information we developed a predictive model for macromolecule kinetics during TPE, and conducted an internal validation of the model.

Results

We evaluated distribution prediction of the model and we obtained good correlation: IgG (r=0.94, 95%CI=0.91-0.96, R2=0.88, P <0.001), IgA (r=0.89, 95%CI=0.84-0.92, R2=0.8, P <0.0001), IgM (r =0.89, 95%CI=0.85-0.93, R2=0.80 P <0.0001), LDL (r =0.94, 95% CI= (0.92-0.96), R2=0.89 P <0.0001). The Bland Altman plots to evaluate agreement: IgG (Bias= 0.3, SD of Bias 23.45,95% Limits of agreement (-45–46)), IgA (Bias= -8.4, SD of Bias 13.95, 95% Limits of agreement (-35 to 19)), IgM (Bias= -0.13, SD of Bias 29.4, 95% Limits of agreement (-58 to 58)), and LDL (Bias= -17, SD of Bias 31.47,95% Limits of agreement (-78 to 44)).

Conclusion

The model predicted accurately the distribution of macromolecules after multiple treatments. The correlation and agreement was especially good for IgG, and IgA. Although the correlation was good for LDL, concordance was not, this can be explained by the intravascular distribution and the short half-life of this molecule. The model was programmed in an app format for IOS to make it practical. A validation cohort is being conducted.