Abstract: FR-PO1054
Predicting Risk of Acute Cellular Rejection After Belatacept Conversion
Session Information
- Transplantation: Clinical - Pharmacology and Nonkidney Solid Organ Transplants
November 07, 2025 | Location: Exhibit Hall, Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Transplantation
- 2102 Transplantation: Clinical
Authors
- Qian, Long, Yale School of Medicine, New Haven, Connecticut, United States
- Menon, Madhav C., Yale School of Medicine, New Haven, Connecticut, United States
- Cohen, Elizabeth A., Yale New Haven Hospital, New Haven, Connecticut, United States
- Belfield, Kristen D, Yale New Haven Hospital, New Haven, Connecticut, United States
- Wilson, Francis Perry, Yale School of Medicine, New Haven, Connecticut, United States
- Kadhim, Bashar A., Yale School of Medicine, New Haven, Connecticut, United States
- Moledina, Dennis G., Yale School of Medicine, New Haven, Connecticut, United States
Background
For kidney transplant recipients, belatacept has better side effect and cardiovascular profile compared to calcineurin inhibitors (CNIs) but confers a higher risk of acute cellular rejection (ACR). Here we developed a clinical risk prediction tool to estimate ACR risk following conversion to belatacept.
Methods
We included patients switched from another immunosuppressive agent to balatacept at a single-center from 2011-2024 and assessed the outcome of biopsy-proven ACR at any time after belatacept conversion. Potential predictors included demographic and clinical data extracted from electronic health records. We created a multivariable model using features selected using LASSO regression with 100 iterations of bootstrapping on a random 70% of samples. We created an interactive risk predictor tool using regression coefficients from this multivariable model.
Results
Among 341 patients who converted to belatacept, 59 had ACR after conversion. Key predictors of ACR were shorter time since transplant at belatacept conversion, greater number of HLA mismatches, self-identified Black race, and non-alemtuzumab induction (Table 1, Figure 1a). This model showed an AUC of 0.77 (95% CI 0.71 – 0.83). These variables were integrated into our interactive calculator to estimate risk of ACR after belatacept conversion (Figure 1b), which will be released after external validation.
Conclusion
We identified patient factors that predicted ACR risk after belatacept conversion. Future directions include external validation, as well as incorporating the predicted ACR risk of not converting to belatacept, to better inform clinical decision-making.
Funding
- NIDDK Support