Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response Articles uri icon

authors

  • SANCHEZ DE LA NAVA, ANA MARIA
  • GOMEZ CID, LIDIA
  • DOMINGUEZ SOBRINO, ALONSO
  • FERNÁNDEZ AVILÉS, FRANCISCO
  • BERENFELD, OMER
  • ATIENZA, FELIPE

publication date

  • October 2022

issue

  • 1025430

volume

  • 13

International Standard Serial Number (ISSN)

  • 1664-042X

abstract

  • Background: Cardiac fibrosis has been identified as a major factor in
    conduction alterations leading to atrial arrhythmias and modification of drug
    treatment response.
    Objective: To perform an in silico proof-of-concept study of Artificial
    Intelligence (AI) ability to identify susceptibility for conduction blocks in
    simulations on a population of models with diffused fibrotic atrial tissue and
    anti-arrhythmic drugs.
    Methods: Activity in 2D cardiac tissue planes were simulated on a population of
    variable electrophysiological and anatomical profiles using the Koivumaki
    model for the atrial cardiomyocytes and the Maleckar model for the diffused
    fibroblasts (0%, 5% and 10% fibrosis area). Tissue sheets were of 2 cm side and
    the effect of amiodarone, dofetilide and sotalol was simulated to assess the
    conduction of the electrical impulse across the planes. Four different AI
    algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector
    Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were
    evaluated in predicting conduction of a stimulated electrical impulse.
    Results: Overall, fibrosis implementation lowered conduction velocity (CV) for
    the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s;
    10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action
    potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%:
    106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis:
    89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p <
    0.001). When the antiarrhythmic drugs were present, a total block was
    observed in most of the profiles. In those profiles in which electrical
    conduction was preserved, a decrease in CV was observed when simulations
    were performed in the 0% fibrosis tissue patch (Amiodarone ¿CV: ¿3.59 ±
    1.52 cm/s; Dofetilide ¿CV: ¿13.43 ± 4.07 cm/s; Sotalol ¿CV: ¿0.023 ± 0.24 cm/
    s). This effect was preserved for amiodarone in the 5% fibrosis patch
    (Amiodarone ¿CV: ¿4.96 ± 2.15 cm/s; Dofetilide ¿CV: 0.14 ± 1.87 cm/s;Sotalol ¿CV: 0.30 ± 4.69 cm/s). 10% fibrosis simulations showed that part of the
    profiles increased CV while others showed a decrease in this variable
    (Amiodarone ¿CV: 0.62 ± 9.56 cm/s; Dofetilide ¿CV: 0.05 ± 1.16 cm/s;
    Sotalol ¿CV: 0.22 ± 1.39 cm/s). Finally, when the AI algorithms were tested
    for predicting conduction on input of variables from the population of
    modelled, Cubic SVM showed the best performance with AUC = 0.95.
    Conclusion: In silico proof-of-concept study demonstrates that fibrosis can
    alter the expected behavior of antiarrhythmic drugs in a minority of atrial
    population models and AI can assist in revealing the profiles that will
    respond differently.

subjects

  • Biology and Biomedicine

keywords

  • atrial fibrillation; cardiac fibrosis; machine learning; population of models; support vector machines