Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.
Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.
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DOI：
10.3389/fphys.2022.1025430
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发表时间：
2022
影响因子：
4
通讯作者：
中科院分区：
文献类型：
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背景资料：心脏纤维化已被确定为导致房性心律失常和药物治疗反应改变的传导改变的主要因素。 目的：对人工智能（AI）在具有弥漫性纤维化心房组织和抗心律失常药物的模型群体的模拟中识别传导阻滞易感性的能力进行计算机模拟概念验证研究。 研究方法：使用心房心肌细胞的Koivumaki模型和扩散成纤维细胞的Maleckar模型（0%、5%和10%纤维化面积），在可变电生理学和解剖学特征的群体上模拟2D心脏组织平面中的活动。组织片为2 cm侧，模拟胺碘酮、多非利特和索他洛尔的作用，以评估电脉冲跨平面的传导。四种不同的AI算法（二次支持向量机，QSVM，三次支持向量机，CSVM，决策树，DT和K-最近邻，KNN）进行了评估，在预测传导的刺激电脉冲。 结果如下：总体而言，纤维化实施降低了传导曲线的传导速度（CV）（0%纤维化：67.52 ± 7.3 cm/s; 5%：58.81 ± 14.04 cm/s; 10%：57.56 ± 14.78 cm/s; p < 0.001）联合90%动作电位时程缩短（0%纤维化：187.77 ± 37.62 ms; 5%：93.29 ± 82.69 ms; 10%：106.37 ± 85.15 ms; p < 0.001）和峰值膜电位（0%纤维化：89.16 ± 16.01 mV; 5%：70.06 ± 17.08 mV; 10%：82.21 ± 19.90 mV; p < 0.001）。当存在抗抑郁药时，在大多数曲线中观察到完全阻断。在保留电传导的曲线中，当在0%纤维化组织贴片中进行模拟时，观察到CV降低（胺碘酮ΔCV：−3.59 ± 1.52 cm/s;多非利特ΔCV：−13.43 ± 4.07 cm/s;索他洛尔ΔCV：−0.023 ± 0.24 cm/s）。胺碘酮在5%纤维化贴剂中保留了这种作用（胺碘酮ΔCV：−4.96 ± 2.15 cm/s;多非利特ΔCV：0.14 ± 1.87 cm/s;索他洛尔ΔCV：0.30 ± 4.69 cm/s）。10%纤维化模拟显示部分曲线CV增加，而其他曲线显示该变量降低（胺碘酮ΔCV：0.62 ± 9.56 cm/s;多非利特ΔCV：0.05 ± 1.16 cm/s;索他洛尔ΔCV：0.22 ± 1.39 cm/s）。最后，当测试AI算法用于预测来自建模群体的变量输入的传导时，Cubic SVM显示出最佳性能，AUC = 0.95。 结论：计算机模拟概念验证研究表明，纤维化可以改变少数心房群体模型中抗心律失常药物的预期行为，AI可以帮助揭示不同反应的特征。
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 ΔCV: −3.59 ± 1.52 cm/s; Dofetilide ΔCV: −13.43 ± 4.07 cm/s; Sotalol ΔCV: −0.023 ± 0.24 cm/s). This effect was preserved for amiodarone in the 5% fibrosis patch (Amiodarone ΔCV: −4.96 ± 2.15 cm/s; Dofetilide ΔCV: 0.14 ± 1.87 cm/s; Sotalol ΔCV: 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 ΔCV: 0.62 ± 9.56 cm/s; Dofetilide ΔCV: 0.05 ± 1.16 cm/s; Sotalol ΔCV: 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.
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影响因子：
10.8
作者：
Lin, Xianming;Zemlin, Christian;Veenstra, Richard D.
通讯作者：
Veenstra, Richard D.
影响因子：
3.4
作者：
Maleckar, Mary M.;Greenstein, Joseph L.;Trayanova, Natalia A.
通讯作者：
Trayanova, Natalia A.
影响因子：
37.8
作者：
Lee, Ken W.;Everett, Thomas H.;Olgin, Jeffrey E.
通讯作者：
Olgin, Jeffrey E.
影响因子：
8.4
作者：
Krul, Sebastien P. J.;Berger, Wouter R.;de Groot, Joris R.
通讯作者：
de Groot, Joris R.
影响因子：
3.5
作者：
Peirlinck M;Costabal FS;Yao J;Guccione JM;Tripathy S;Wang Y;Ozturk D;Segars P;Morrison TM;Levine S;Kuhl E
通讯作者：
Kuhl E