000126750 001__ 126750
000126750 005__ 20241125101133.0
000126750 0247_ $$2doi$$a10.1016/j.bspc.2023.105056
000126750 0248_ $$2sideral$$a134124
000126750 037__ $$aART-2023-134124
000126750 041__ $$aeng
000126750 100__ $$0(orcid)0000-0002-8334-4786$$aPérez, Cristina$$uUniversidad de Zaragoza
000126750 245__ $$aQT interval time lag in response to heart rate changes during stress test for Coronary Artery Disease diagnosis
000126750 260__ $$c2023
000126750 5060_ $$aAccess copy available to the general public$$fUnrestricted
000126750 5203_ $$aBackground: Slow adaptation of the QT interval to abrupt changes in heart rate (HR) can enhance ventricular heterogeneity and has been suggested as a marker of arrhythmic risk. Most investigations on QT rate adaptation lag have been performed in response to step-like HR changes. However, abrupt HR changes are difficult to induce or observe in ECG recordings under ambulatory conditions.
Objective: We aim to evaluate the power of indices related to the QT lag in response to ramp-like HR changes in stress test to assess CAD risk.
Methods: We quantified the lag between the actual QT series and the memoryless expected QT series, which was obtained by fitting a hyperbolic regression model to the instantaneous QT and HR measurements in stages where their behavior could be assumed stationary. The proposed methodology was applied to analyze ECG stress tests of a subset of 448 patients presenting different risk levels for Coronary Artery Disease (CAD). The QT lag was estimated separately in the exercise and recovery phases.
Results: An increase in the estimated QT lag during exercise (from 25 to 36 s) and a decrease during recovery (from 57 to 39 s) were associated with higher CAD risk. The difference between these lags showed significant capacity for CAD risk stratification.
Conclusion: The QT lag in response to HR changes can be quantified from a stress test. QT lag values in response to ramp-like HR changes are in ranges comparable to those quantified from abrupt HR changes and show clinical significance to stratify CAD risk.
000126750 536__ $$9info:eu-repo/grantAgreement/ES/DGA-IIU/796-2019$$9info:eu-repo/grantAgreement/ES/DGA/LMP94_21$$9info:eu-repo/grantAgreement/ES/DGA/T39-23R$$9info:eu-repo/grantAgreement/EUR/ERC-2014-StG-638284$$9info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-104881RB-I00$$9info:eu-repo/grantAgreement/ES/MICINN-FEDER/PID2019-105674RB-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-130459B-I00
000126750 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000126750 590__ $$a4.9$$b2023
000126750 592__ $$a1.284$$b2023
000126750 591__ $$aENGINEERING, BIOMEDICAL$$b30 / 123 = 0.244$$c2023$$dQ1$$eT1
000126750 593__ $$aBiomedical Engineering$$c2023$$dQ1
000126750 593__ $$aSignal Processing$$c2023$$dQ1
000126750 593__ $$aHealth Informatics$$c2023$$dQ1
000126750 594__ $$a9.8$$b2023
000126750 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000126750 700__ $$0(orcid)0000-0002-1960-407X$$aPueyo, Esther$$uUniversidad de Zaragoza
000126750 700__ $$0(orcid)0000-0002-7503-3339$$aMartínez, Juan Pablo$$uUniversidad de Zaragoza
000126750 700__ $$aViik, Jari
000126750 700__ $$0(orcid)0000-0003-3434-9254$$aLaguna, Pablo$$uUniversidad de Zaragoza
000126750 7102_ $$15008$$2800$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Teoría Señal y Comunicac.
000126750 773__ $$g86 (2023), 105056 [13 pp.]$$pBiomed. signal proces. control$$tBiomedical Signal Processing and Control$$x1746-8094
000126750 8564_ $$s1865813$$uhttps://zaguan.unizar.es/record/126750/files/texto_completo.pdf$$yVersión publicada
000126750 8564_ $$s2530449$$uhttps://zaguan.unizar.es/record/126750/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000126750 909CO $$ooai:zaguan.unizar.es:126750$$particulos$$pdriver
000126750 951__ $$a2024-11-22-11:59:31
000126750 980__ $$aARTICLE