000129494 001__ 129494
000129494 005__ 20241125101132.0
000129494 0247_ $$2doi$$a10.6002/ect.2022.0372
000129494 0248_ $$2sideral$$a135794
000129494 037__ $$aART-2023-135794
000129494 041__ $$aeng
000129494 100__ $$0(orcid)0000-0002-2290-4807$$aZalba Etayo, María Begoña$$uUniversidad de Zaragoza
000129494 245__ $$aGraft Survival in Liver Transplantation: An Artificial Neuronal Network Assisted Analysis of the Importance of Comorbidities
000129494 260__ $$c2023
000129494 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129494 5203_ $$aObjectives: Liver transplant represents a widespread therapeutic option for patients with end-stage liver failure. Up to now, most of the scores describing the probability of liver graft survival have shown poor predictive performance. With this in mind, the present study seeks to analyze the predictive value of recipient comorbidities on liver graft survival within the first year.
Materials and Methods: The study included prospectively collected data from patients who received a liver transplant at our center from 2010 to 2021. A
predictive model was then developed through an Artificial Neural Network that included the parameters associated with graft loss as identified by the Spanish Liver Transplant Registry report and comorbidities with prevalence >2% present in our study cohort.
Results: Most patients in our study were men (75.5%); mean age was 54.8 ± 9.6 years. The main cause of transplant was cirrhosis (86.7%), and 67.4% of patients had some associated comorbidities. Graft loss due to retransplant or death with dysfunction occurred in 14% of cases. Of all the variables analyzed, we found 3 comorbidities associated with graft loss (as shown by informative value and normalized informative value, respectively): antiplatelet and/or anticoagulants treatments (0.124 and 78.4%), previous immunosuppression (0.110 and 69.6%), and portal thrombosis (0.105 and 66.3%). Remarkably, our model showed a C statistic of 0.745 (95% CI, 0.692-0.798; asymptotic P < .001), which was higher than others found in previous studies.
Conclusions: Our model identified key parameters that may influence graft loss, including specific recipient comorbidities. The use of artificial intelligence methods could reveal connections that may be overlooked by conventional statistics.
000129494 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000129494 590__ $$a0.7$$b2023
000129494 592__ $$a0.247$$b2023
000129494 591__ $$aTRANSPLANTATION$$b26 / 31 = 0.839$$c2023$$dQ4$$eT3
000129494 593__ $$aTransplantation$$c2023$$dQ3
000129494 594__ $$a1.4$$b2023
000129494 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000129494 700__ $$aMarín Araiz, Lucía
000129494 700__ $$0(orcid)0000-0002-0501-2057$$aMontes Aranguren, María$$uUniversidad de Zaragoza
000129494 700__ $$0(orcid)0000-0003-4672-8083$$aLorente Pérez, Sara$$uUniversidad de Zaragoza
000129494 700__ $$0(orcid)0000-0002-9031-3961$$aPalacios Gasos, Pilar$$uUniversidad de Zaragoza
000129494 700__ $$aPascual Bielsa, Ana
000129494 700__ $$aSánchez Donoso,Nuria
000129494 700__ $$0(orcid)0000-0002-7119-2244$$aSerrano Aullo, Trinidad$$uUniversidad de Zaragoza
000129494 700__ $$0(orcid)0000-0001-6033-2216$$aAraiz Burdio, Juan José$$uUniversidad de Zaragoza
000129494 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000129494 7102_ $$11013$$2090$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Cirugía
000129494 773__ $$g21, 4 (2023), 338-344$$pExperimental and Clinical Transplantation$$tExperimental and Clinical Transplantation$$x1304-0855
000129494 8564_ $$s814498$$uhttps://zaguan.unizar.es/record/129494/files/texto_completo.pdf$$yPostprint
000129494 8564_ $$s897438$$uhttps://zaguan.unizar.es/record/129494/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000129494 909CO $$ooai:zaguan.unizar.es:129494$$particulos$$pdriver
000129494 951__ $$a2024-11-22-11:59:17
000129494 980__ $$aARTICLE