000168383 001__ 168383
000168383 005__ 20260204153543.0
000168383 0247_ $$2doi$$a10.1109/TBME.2025.3607144
000168383 0248_ $$2sideral$$a147832
000168383 037__ $$aART-2025-147832
000168383 041__ $$aeng
000168383 100__ $$aPérez, Cristina$$uUniversidad de Zaragoza
000168383 245__ $$aSudden cardiac death risk prediction from QT–RR adaptation time lag computed from exercise stress test ECG
000168383 260__ $$c2025
000168383 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168383 5203_ $$aObjetive: This study aims to evaluate the QT adaptation time following gradual heart rate changes estimated from exercise stress test (EST) ECGs as a marker of sudden cardiac death (SCD) risk. The predicted risk value for any cardiovascular death (CVD) is also evaluated. Methods: Three ECG-derived markers related to QT-RR adaptation time were estimated during the exercise phase of EST, τe, during the recovery phase, τr, and as the difference between them, Δτ. The values were computed from patients with coronary artery disease (CAD) from ARTEMIS study (N=1472; median follow-up of 8.9 years). These markers are calculated as the delay between the observed RR interval series and a patient-specific memoryless RR series computed from the QT interval (RR-based strategy). Alternatively, the estimates were calculated as the delay between the observed QT intervals and a patient-specific instantaneous QT series computed from the RR intervals (QT-based strategy). The relation between the markers and CAD or SCD was evaluated. Results: The marker τr is more robustly estimateed with the RR-based strategy and is able to stratify survivors and victims of either SCD or CVD (p-value equal to 0.022 and 0.065, respectively). Multivariable regression models for predicting SCD and CVD include the QT-RR adaptation time estimated in the recovery phase of EST using the RR-based strategy. Conclusion: A prolonged QT-RR adaptation time during the recovery phase of the EST ECG, calculated with the RR-based strategy, can predict SCD and CVD, providing complementary information to other clinical markers
000168383 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2021-128972OA-I00$$9info:eu-repo/grantAgreement/ES/AEI/PID2023-148975OB-I00$$9info:eu-repo/grantAgreement/ES/DGA-FEDER/T39-23R-BSICoS$$9info:eu-repo/grantAgreement/ES/DGA/LMP 94-21$$9info:eu-repo/grantAgreement/ES/MICINN/CNS2023-143599$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-140556OB-I00$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-031413-I$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130459B-I00
000168383 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168383 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168383 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, Juan Pablo$$uUniversidad de Zaragoza
000168383 700__ $$0(orcid)0000-0003-4130-5866$$aRamírez, Julia$$uUniversidad de Zaragoza
000168383 700__ $$aKenttä, Tuomas
000168383 700__ $$aJunttila, Juhani
000168383 700__ $$aKiviniemi, Antti
000168383 700__ $$aPerkiömäki, Juha
000168383 700__ $$aHuikuri, Heikki
000168383 700__ $$0(orcid)0000-0002-1960-407X$$aPueyo, Esther$$uUniversidad de Zaragoza
000168383 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000168383 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000168383 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000168383 773__ $$g(2025), 1-12$$pIEEE trans. biomed. eng.$$tIEEE Transactions on Biomedical Engineering$$x0018-9294
000168383 8564_ $$s1246166$$uhttps://zaguan.unizar.es/record/168383/files/texto_completo.pdf$$yVersión publicada
000168383 8564_ $$s3698615$$uhttps://zaguan.unizar.es/record/168383/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168383 909CO $$ooai:zaguan.unizar.es:168383$$particulos$$pdriver
000168383 951__ $$a2026-02-04-13:14:56
000168383 980__ $$aARTICLE