Dueñas-Jurado, J. M.Gutiérrez, P. A.Casado-Adam, A.Santos-Luna, F.Salvatierra-Velázquez, A.Cárcel, S.Robles-Arista, C. J. C.Hervás-Martínez, C.2023-01-192023-01-192021-06-04Dueñas-Jurado JM, Gutiérrez PA, Casado-Adam A, Santos-Luna F, Salvatierra-Velázquez A, Cárcel S, et al. New models for donor-recipient matching in lung transplantations. PLoS One. 2021 Jun 4;16(6):e0252148http://hdl.handle.net/10668/4598Objective One of the main problems of lung transplantation is the shortage of organs as well asreduced survival rates. In the absence of an international standardized model for lung donor-recipient allocation, we set out to develop such a model based on the characteristics of past experiences with lung donors and recipients with the aim of improving the outcomes of the entire transplantation process. Methods This was a retrospective analysis of 404 lung transplants carried out at the Reina Sofía University Hospital (Córdoba, Spain) over 23 years. We analyzed various clinical variables obtained via our experience of clinical practice in the donation and transplantation process. These were used to create various classification models, including classical statistical meth ods and also incorporating newer machine-learning approaches. Results The proposed model represents a powerful tool for donor-recipient matching, which in this current work, exceeded the capacity of classical statistical methods. The variables that pre dicted an increase in the probability of survival were: higher pre-transplant and post-trans plant functional vital capacity (FVC), lower pre-transplant carbon dioxide (PCO2) pressure, lower donor mechanical ventilation, and shorter ischemia time. The variables that negatively influenced transplant survival were low forced expiratory volume in the first second (FEV1) pre-transplant, lower arterial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ratio, bilobar transplant, elderly recipient and donor, donor-recipient graft disproportion requiring a surgical reduction (Tailor), type of combined transplant, need for cardiopulmonary bypass during the surgery, death of the donor due to head trauma, hospi talization status before surgery, and female and male recipient donor sex. Conclusions These results show the difficulty of the problem which required the introduction of other variables into the analysis. The combination of classical statistical methods and machine learn ing can support decision-making about the compatibility between donors and recipients. This helps to facilitate reliable prediction and to optimize the grafts for transplantation, thereby improving the transplanted patient survival rate.enAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/DonorLung transplantationsTissue and organ procurementTransplantationTransplant recipientsConcepción de donantesTrasplante de pulmónObtención de tejidos y órganosTrasplanteReceptores de trasplantesMedical Subject Headings::Check Tags::FemaleMedical Subject Headings::Phenomena and Processes::Immune System Phenomena::Immune System Processes::Transplantation Immunology::Host vs Graft Reaction::Graft SurvivalMedical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::HumansMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Surgical Procedures, Operative::Transplantation::Organ Transplantation::Lung TransplantationMedical Subject Headings::Check Tags::MaleMedical Subject Headings::Geographical Locations::Geographic Locations::Europe::SpainMedical Subject Headings::Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Data Collection::Vital Statistics::Mortality::Survival RateMedical Subject Headings::Persons::Persons::Tissue DonorsMedical Subject Headings::Health Care::Health Care Facilities, Manpower, and Services::Health Services::Tissue and Organ Procurementresearch article34086705open access10.1371/journal.pone.02521481932-6203PMC8177410