Predicting the outcome of heart transplantation from expanded-criteria donors using regression analysis
https://doi.org/10.15825/1995-1191-2026-1-121-127
Abstract
There is a growing need for tools that enable objective assessment of donor heart quality. One such approach is the use of regression models incorporating donor and recipient risk factors to predict surgical outcomes and potentially expand the donor pool by increasing the number of transplantations.
Objective: to develop a model for estimating the total risk of one-year mortality in recipients using different categories of expanded-criteria donors (ECDs).
Materials and methods. The study included 1,500 recipients who underwent orthotopic heart transplantation (OHT) at Shumakov National Medical Research Center of Transplantology and Artificial Organs over an 11-year period, from January 1, 2011, to December 31, 2021. The cohort comprised 1,281 men (85.4%) and 219 women (14.6%), aged 9 to 78 years (median age 49.0 [38.0–56.0] years). The heart transplants performed (n = 1,500) were divided into two clinical groups: group 1 (main group) comprised recipients who underwent OHT from ECDs (n = 1,060; 70.6%); group 2 (control group) included recipients who underwent OHT from standard-criteria donors (n = 440; 29.4%). Donor heart suitability for transplantation was assessed according to the 2023 criteria of the International Society for Heart and Lung Transplantation (ISHLT).
Results. Donor- and recipient-related indicators were initially evaluated using univariate regression analysis. The final multivariate regression model included five donor-related factors – donor–recipient weight mismatch, donor age, high-dose cardiotonic therapy, coronary stenosis, and prolonged graft ischemia – and four recipient-related factors – total bilirubin >40 μmol/L, creatinine >110 μmol/L, international normalized ratio (INR) >1.4, and pre-transplant peripheral veno-arterial extracorporeal membrane oxygenation (pVA-ECMO). The highest odds ratios were observed for donor age, coronary stenosis, graft ischemia time exceeding 6 hours, and pre-transplant pVA-ECMO support. The predicted one-year mortality rate calculated using regression analysis showed a strong correlation (R = 0.827; p < 0.001) with the observed one-year mortality rate. Long-term survival was also analyzed across risk groups, with the worst outcomes observed in the high-risk group.
Conclusion. The proposed statistical model provides a reliable prognostic accuracy for both early and long-term post-transplant survival. Its application at the stages of donor heart evaluation and donor–recipient matching may facilitate the use of a broader donor pool while enabling an objective assessment of recipient prognosis.
About the Authors
E. A. SpirinaRussian Federation
Ekaterina Spirina
1, Shchukinskaya str., Moscow, 123182
Phone: (968) 048-35-26
D. V. Ryabtsev
Russian Federation
Moscow
S. A. Sakhovsky
Russian Federation
Moscow
A. K. Solodovnikova
Russian Federation
Moscow
A. A. Kuznetsova
Russian Federation
Moscow
А. S. Ignatkina
Russian Federation
Moscow
Ya. S. Karina
Russian Federation
Moscow
S. V. Gautier
Russian Federation
Moscow
References
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Review
For citations:
Spirina E.A., Ryabtsev D.V., Sakhovsky S.A., Solodovnikova A.K., Kuznetsova A.A., Ignatkina А.S., Karina Ya.S., Gautier S.V. Predicting the outcome of heart transplantation from expanded-criteria donors using regression analysis. Russian Journal of Transplantology and Artificial Organs. 2026;28(1):121-127. https://doi.org/10.15825/1995-1191-2026-1-121-127
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