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Predictive ability of HLA antigen incompatibility prediction algorithms for donor heart rejection risk stratification

https://doi.org/10.18705/2311-4495-2025-12-5-463-474

EDN: MDFWEH

Abstract

Background. Standard HLA typing and simple allele mismatch counts do not always provide an objective assessment of rejection risk. Platforms PIRCHE (Predicted Indirectly ReCognizable HLA Epitopes) and HLAEMMA (HLA Epitope MisMatch Algorithm) estimate donor–recipient HLA incompatibility at the epitope level, based on low-resolution HLA typing.

Objective: To evaluate the predictive ability of PIRCHE and HLA-EMMA for stratifying the risk of heart allograft rejection within the first year after orthotopic heart transplantation.

Materials and Methods. This retrospective study included 160 heart recipients. During the first-year post-transplantation 77 recipients (control group) showed no rejection (ACR 0R–1R and/or pAMR 0–1). Eighty-three recipients experienced an adverse event: a rejection episode (ACR 2R–3R and/or pAMR 2–3) or death from any cause without preceding rejection. HLA incompatibility was calculated using the PIRCHE-T2, PIRCHE-B, and six HLA-EMMA algorithms. The prognostic significance was assessed using the Kaplan-Meier method.

Results. Statistically significant differences between groups were observed for five algorithms: Allele-MM-L2, AA-MM-L1, AA-MM-L2, SA-L2, and PIRCHE-T2. PIRCHE-T2 demonstrated the strongest predictive value with a threshold >80: adverse events occurred in 73.7 % (28/38) of recipients with PIRCHE-T2 >80 versus 45 % (55/122) with PIRCHE-T2 ≤80.

Conclusion. The obtained results indicate the potential utility of the PIRCHE-T2 with a threshold value of >80 for risk stratification during the first post-transplant year.

About the Authors

D. A. Kilina
Almazov National Medical Research Centre
Russian Federation

Daria A. Kilina, Head of the Tissue Typing Laboratory, Central Clinical Diagnostic Laboratory

2 Akkuratova str., St. Petersburg, 197341


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



A. A. Chaykina
Almazov National Medical Research Centre
Russian Federation

Alexandra A. Chaikina, Resident in Laboratory Genetics, Department of Laboratory Medicine, Institute of Medical Education

St. Petersburg


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



L. O. Korneva
Almazov National Medical Research Centre, Saint Petersburg, Russia
Russian Federation

Liubov O. Korneva, Junior Researcher, Laboratory of High-Tech Methods for Heart Failure Treatment, Institute of Heart and Vessels

St. Petersburg


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



Yu. S. Sazonova
Almazov National Medical Research Centre, Saint Petersburg, Russia
Russian Federation

Yulia V. Sazonova, Assistant, Department of Faculty Therapy, Institute of Medical Education

St. Petersburg


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



A. S. Golovkin
Almazov National Medical Research Centre, Saint Petersburg, Russia
Russian Federation

Golovkin Alexey S., MD, PhD, Associate Professor, Head of the Laboratory of Microvesicular Signaling, Institute of Molecular Biology and Genetics

St. Petersburg


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



P. A. Fedotov
Almazov National Medical Research Centre, Saint Petersburg, Russia
Russian Federation

Petr A. Fedotov, MD, PhD, Leading Researcher, Head of the Laboratory of High-Tech Methods for Heart Failure Treatment, Institute of Heart and Vessels, Associate Professor of the Department of Faculty Therapy, Institute of Medical Education

St. Petersburg


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



O. V. Kalinina
Almazov National Medical Research Centre
Russian Federation

Olga V. Kalinina, DSc in Biology, Associate Professor, Leading Researcher, Laboratory of Microvesicular Signaling, Institute of Molecular Biology and Genetics, Professor, Department of Laboratory Medicine, Institute of Medical Education

St. Petersburg


Competing Interests:

The authors declare no use of Generative AI in the preparation of this manuscript.



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For citations:


Kilina D.A., Chaykina A.A., Korneva L.O., Sazonova Yu.S., Golovkin A.S., Fedotov P.A., Kalinina O.V. Predictive ability of HLA antigen incompatibility prediction algorithms for donor heart rejection risk stratification. Translational Medicine. 2025;12(5):463-474. (In Russ.) https://doi.org/10.18705/2311-4495-2025-12-5-463-474. EDN: MDFWEH

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ISSN 2311-4495 (Print)
ISSN 2410-5155 (Online)