000125738 001__ 125738
000125738 005__ 20241125101124.0
000125738 0247_ $$2doi$$a10.1007/s11517-022-02713-x
000125738 0248_ $$2sideral$$a130988
000125738 037__ $$aART-2023-130988
000125738 041__ $$aeng
000125738 100__ $$aSaiz Vivó, Javier
000125738 245__ $$aAtrial fibrillation episode patterns as predictor of clinical outcome of catheter ablation
000125738 260__ $$c2023
000125738 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125738 5203_ $$aMethods for characterization of atrial fibrillation (AF) episode patterns have been introduced without establishing clinical significance. This study investigates, for the first time, whether post-ablation recurrence of AF can be predicted by evaluating episode patterns. The dataset comprises of 54 patients (age 56 ± 11 years; 67% men), with an implantable cardiac monitor, before undergoing the first AF catheter ablation. Two parameters of the alternating bivariate Hawkes model were used to characterize the pattern: AF dominance during the monitoring period (log(mu)) and temporal aggregation of episodes (beta1). Moreover, AF burden and AF density, a parameter characterizing aggregation of AF burden, were studied. The four parameters were computed from an average of 29 AF episodes before ablation. The risk of AF recurrence after catheter ablation using the Hawkes parameters log(mu) and beta1, AF burden, and AF density was evaluated. While the combination of AF burden and AF density is related to a non-significant hazard ratio, the combination of log(mu) and beta1 is related to a hazard ratio of 1.95 (1.03–3.70; p < 0.05). The Hawkes parameters showed increased risk of AF recurrence within 1 year after the procedure for patients with high AF dominance and high episode aggregation and may be used for pre-ablation risk assessment.
000125738 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group$$9info:eu-repo/grantAgreement/EC/H2020/766082/EU/MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression/MY-ATRIA$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 766082-MY-ATRIA$$9info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-104881RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/FJC-2018-037369-I
000125738 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125738 590__ $$a2.6$$b2023
000125738 592__ $$a0.641$$b2023
000125738 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b82 / 170 = 0.482$$c2023$$dQ2$$eT2
000125738 593__ $$aComputer Science Applications$$c2023$$dQ2
000125738 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b21 / 66 = 0.318$$c2023$$dQ2$$eT1
000125738 593__ $$aBiomedical Engineering$$c2023$$dQ2
000125738 591__ $$aMEDICAL INFORMATICS$$b26 / 44 = 0.591$$c2023$$dQ3$$eT2
000125738 591__ $$aENGINEERING, BIOMEDICAL$$b71 / 123 = 0.577$$c2023$$dQ3$$eT2
000125738 594__ $$a6.0$$b2023
000125738 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125738 700__ $$aCorino, Valentina D. A.
000125738 700__ $$0(orcid)0000-0003-0226-4950$$aMartín Yebra, Alba$$uUniversidad de Zaragoza
000125738 700__ $$aMainardi, Luca T.
000125738 700__ $$aHatala, Robert
000125738 700__ $$aSörnmo, Leif
000125738 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000125738 773__ $$g61, 2 (2023), 317-327$$pMed. biol. eng. comput.$$tMEDICAL & BIOLOGICAL ENGINEERING & COMPUTING$$x0140-0118
000125738 8564_ $$s1448745$$uhttps://zaguan.unizar.es/record/125738/files/texto_completo.pdf$$yVersión publicada
000125738 8564_ $$s2408291$$uhttps://zaguan.unizar.es/record/125738/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125738 909CO $$ooai:zaguan.unizar.es:125738$$particulos$$pdriver
000125738 951__ $$a2024-11-22-11:57:18
000125738 980__ $$aARTICLE