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Personalized, intuitive & visual QT-prolongation monitoring using patient-specific QTc threshold with pseudo-coloring and explainable AI.
Alahmadi, Alaa; Davies, Alan; Vigo, Markel; Jay, Caroline.
Afiliação
  • Alahmadi A; College of Computer Science and Engineering at Yanbu, Taibah University, Medina, KSA, Saudi Arabia; Department of Computer Science, The University of Manchester, Manchester, UK. Electronic address: aamahmdi@taibahu.edu.sa.
  • Davies A; Division of Informatics, Imaging and Data Sciences, School of Health Sciences, The University of Manchester, Manchester, UK.
  • Vigo M; Department of Computer Science, The University of Manchester, Manchester, UK.
  • Jay C; Department of Computer Science, The University of Manchester, Manchester, UK.
J Electrocardiol ; 81: 218-223, 2023.
Article em En | MEDLINE | ID: mdl-37837739
ABSTRACT

BACKGROUND:

Drug-induced QT-prolongation increases the risk of TdP arrhythmia attacks and sudden cardiac death. However, measuring the QT-interval and determining a precise cut-off QT/QTc value that could put a patient at risk of TdP is challenging and influenced by many factors including female sex, drug-free baseline, age, genetic predisposition, and bradycardia.

OBJECTIVES:

This paper presents a novel approach for intuitively and visually monitoring QT-prolongation showing a potential risk of TdP, which can be adjusted according to patient-specific risk factors, using a pseudo-coloring technique and explainable artificial intelligence (AI).

METHODS:

We extended the development and evaluation of an explainable AI-based technique- visualized using pseudo-color on the ECG signal, thus intuitively 'explaining' how its decision was made -to detect QT-prolongation showing a potential risk of TdP according to a cut-off personalized QTc value (using Bazett's ∆QTc > 60 ms relative to drug-free baseline and Bazett's QTc > 500 ms as examples), and validated its performance using a large number of ECGs (n = 5050), acquired from a clinical trial assessing the effects of four known QT-prolonging drugs versus placebo on healthy subjects. We compared this new personalized approach to our previous study that used a more general approach using the QT-nomogram. RESULTS AND

CONCLUSIONS:

The explainable AI-based algorithm can accurately detect QT-prolongation when adjusted to a personalized patient-specific cut-off QTc value showing a potential risk of TdP. Using ∆QTc > 60 ms relative to drug-free baseline and QTc > 500 ms as examples, the algorithm yielded a sensitivity of 0.95 and 0.79, and a specificity of 0.95 and 0.98, respectively. We found that adjusting pseudo-coloring according to Bazett's ∆QTc > 60 ms relative to a drug-free baseline personalized to each patient provides better sensitivity than using Bazett's QTc > 500 ms, which could underestimate a potentially clinically significant QT-prolongation with bradycardia.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndrome do QT Longo / Torsades de Pointes Limite: Female / Humans / Male Idioma: En Revista: J Electrocardiol Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Síndrome do QT Longo / Torsades de Pointes Limite: Female / Humans / Male Idioma: En Revista: J Electrocardiol Ano de publicação: 2023 Tipo de documento: Article