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QT interval time lag in response to heart rate changes during stress test for Coronary Artery Disease diagnosis

  • Cristina Pérez*
  • , Esther Pueyo
  • , Juan Pablo Martínez
  • , Jari Viik
  • , Pablo Laguna
  • *Tämän työn vastaava kirjoittaja

    Tutkimustuotos: ArtikkeliTieteellinenvertaisarvioitu

    8 Sitaatiot (Scopus)
    14 Lataukset (Pure)

    Abstrakti

    Background: : 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.

    AlkuperäiskieliEnglanti
    Artikkeli105056
    Sivumäärä13
    JulkaisuBiomedical Signal Processing and Control
    Vuosikerta86
    NumeroPart A
    DOI - pysyväislinkit
    TilaJulkaistu - syysk. 2023
    OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

    Rahoitus

    This work was supported by projects PID2019-104881RB-I00, PID2019-105674RB-I00, and TED2021-130459B-I00 funded by Spanish Ministry of Science and Innovation (MICINN), Spain and FEDER , by Gobierno de Aragón (Reference Group Biomedical Signal Interpretation and Computational Simulation (BSICoS) T39-23R and project LMP94_21) cofunded by FEDER 2014–2020 “Building Europe from Aragón” and by European Research Council (project ERC-StG 638284). The computation was performed at the High Performance computing platform of the NANBIOSIS ICTS. C. Pérez would like to thank the support from Gobierno de Aragón with a personal PhD grant, IIU/796/2019 .

    Julkaisufoorumi-taso

    • Jufo-taso 1

    !!ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Health Informatics

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