Machine learning enhanced immunologic risk assessments for solid organ transplantation
Abstract The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient and donor human leukocyte antigen (HLA) all...
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| Format: | Article |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-92147-w |
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| author | Eric T. Weimer Katherine A. Newhall |
| author_facet | Eric T. Weimer Katherine A. Newhall |
| author_sort | Eric T. Weimer |
| collection | DOAJ |
| description | Abstract The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient and donor human leukocyte antigen (HLA) alleles and recipient HLA antibodies. An HLA allele imputation system that converts HLA antigens to alleles was developed to enhance the prediction’s performance. The imputed and known HLA alleles were combined for recipient and donor with a recipient’s HLA antibody profile. After processing, donor-specific antibodies were input into various ML models. Next, an ML model was developed and characterized based on determining donor-specific antibodies using the full HLA antibody profile of the recipient without laboratory interpretation. The models achieved an ROC-AUC of 0.975. These results demonstrate that the models can predict crossmatch reactivity and yield insight into the importance of specific HLA antibodies in the transplant-matching process. These data represent our understanding of personalized histocompatibility risk assessments. |
| format | Article |
| id | doaj-art-5ad2544673f44a0e9ccf60a9458bc5c7 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5ad2544673f44a0e9ccf60a9458bc5c72025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115111010.1038/s41598-025-92147-wMachine learning enhanced immunologic risk assessments for solid organ transplantationEric T. Weimer0Katherine A. Newhall1Department of Pathology and Laboratory Medicine, The University of North Carolina at Chapel Hill School of MedicineDepartment of Mathematics, The University of North Carolina at Chapel HillAbstract The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient and donor human leukocyte antigen (HLA) alleles and recipient HLA antibodies. An HLA allele imputation system that converts HLA antigens to alleles was developed to enhance the prediction’s performance. The imputed and known HLA alleles were combined for recipient and donor with a recipient’s HLA antibody profile. After processing, donor-specific antibodies were input into various ML models. Next, an ML model was developed and characterized based on determining donor-specific antibodies using the full HLA antibody profile of the recipient without laboratory interpretation. The models achieved an ROC-AUC of 0.975. These results demonstrate that the models can predict crossmatch reactivity and yield insight into the importance of specific HLA antibodies in the transplant-matching process. These data represent our understanding of personalized histocompatibility risk assessments.https://doi.org/10.1038/s41598-025-92147-w |
| spellingShingle | Eric T. Weimer Katherine A. Newhall Machine learning enhanced immunologic risk assessments for solid organ transplantation Scientific Reports |
| title | Machine learning enhanced immunologic risk assessments for solid organ transplantation |
| title_full | Machine learning enhanced immunologic risk assessments for solid organ transplantation |
| title_fullStr | Machine learning enhanced immunologic risk assessments for solid organ transplantation |
| title_full_unstemmed | Machine learning enhanced immunologic risk assessments for solid organ transplantation |
| title_short | Machine learning enhanced immunologic risk assessments for solid organ transplantation |
| title_sort | machine learning enhanced immunologic risk assessments for solid organ transplantation |
| url | https://doi.org/10.1038/s41598-025-92147-w |
| work_keys_str_mv | AT erictweimer machinelearningenhancedimmunologicriskassessmentsforsolidorgantransplantation AT katherineanewhall machinelearningenhancedimmunologicriskassessmentsforsolidorgantransplantation |