Assessment of Myocardial Injury Size Metrics Using Carotid Pressure Waveform: Proof-of-Concept in Coronary Occlusion/Reperfusion Rat Model

IF 4.2 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Jiajun Li, Rashid Alavi, Wangde Dai, Ray V. Matthews, Robert A. Kloner, Niema M. Pahlevan
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Abstract

Myocardial infarction (MI) is a leading cause of death worldwide and the most common precursor to heart failure, even after initial treatment. Precise evaluation of myocardial injury is crucial for assessing interventions and improving outcomes. Extensive evidence from both preclinical models and clinical studies demonstrates that the extent and severity of myocardial injury (i.e., myocardial infarct size, ischemic risk zone, and no-reflow area) are critical determinants of long-term outcomes post-MI. This study aims to assess whether carotid pressure waveforms, analyzed using an intrinsic frequency (IF)–machine learning (ML) approach, can accurately quantify myocardial injury sizes: myocardial infarct size, ischemic risk zone, and no-reflow area. Acute MI was induced in N = 88 Sprague-Dawley rats using a standard coronary occlusion/reperfusion model. MI-injury sizes were obtained via histopathology. IF metrics were extracted from carotid pressure waveforms post-MI. ML classifiers were developed using 66 rats and externally tested on 22 additional rats. Our best developed model for infarct size achieved an accuracy of 0.95 (specificity = 0.95, sensitivity = 0.96). For the ischemic risk zone, the best model showed an accuracy of 0.85 (specificity = 0.90, sensitivity = 0.80), and for the no-reflow area, we reached an accuracy of 0.88 (specificity = 0.89, sensitivity = 0.86). To conclude, a hybrid physics-based ML approach applied to carotid pressure waveforms successfully classified MI-injury severity. As carotid pressure waveforms can be measured non-invasively and remotely (e.g., via smartphones), this proof-of-concept preclinical study suggests a translational potential for post-MI management, enabling timely interventions, improved patient monitoring, and mitigating adverse outcomes.

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用颈动脉压力波形评估心肌损伤大小:冠状动脉闭塞/再灌注大鼠模型的概念验证
心肌梗死(MI)是世界范围内死亡的主要原因,也是心力衰竭最常见的前兆,即使在最初治疗后也是如此。准确评估心肌损伤对评估干预措施和改善结果至关重要。临床前模型和临床研究的大量证据表明,心肌损伤的程度和严重程度(即心肌梗死面积、缺血危险区和无回流区)是心肌梗死后长期预后的关键决定因素。本研究旨在评估使用内在频率(IF) -机器学习(ML)方法分析的颈动脉压力波形是否可以准确量化心肌损伤大小:心肌梗死大小、缺血危险区和无血流区域。采用标准冠状动脉闭塞/再灌注模型,对N = 88只Sprague-Dawley大鼠进行急性心肌梗死诱导。通过组织病理学获得轻度损伤大小。从心肌梗死后的颈动脉压力波形中提取IF指标。使用66只大鼠开发了ML分类器,并对另外22只大鼠进行了外部测试。我们开发的最佳梗死面积模型的准确度为0.95(特异性= 0.95,敏感性= 0.96)。对于缺血危险区域,最佳模型的准确率为0.85(特异性= 0.90,敏感性= 0.80),对于无血流再流区域,我们的准确率为0.88(特异性= 0.89,敏感性= 0.86)。综上所述,基于混合物理的ML方法应用于颈动脉压力波形成功地分类了mi损伤的严重程度。由于颈动脉压力波形可以非侵入性和远程测量(例如,通过智能手机),这项概念验证的临床前研究表明,在心肌梗死后管理方面具有转化潜力,能够及时干预,改善患者监测,减轻不良后果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
The FASEB Journal
The FASEB Journal 生物-生化与分子生物学
CiteScore
9.20
自引率
2.10%
发文量
6243
审稿时长
3 months
期刊介绍: The FASEB Journal publishes international, transdisciplinary research covering all fields of biology at every level of organization: atomic, molecular, cell, tissue, organ, organismic and population. While the journal strives to include research that cuts across the biological sciences, it also considers submissions that lie within one field, but may have implications for other fields as well. The journal seeks to publish basic and translational research, but also welcomes reports of pre-clinical and early clinical research. In addition to research, review, and hypothesis submissions, The FASEB Journal also seeks perspectives, commentaries, book reviews, and similar content related to the life sciences in its Up Front section.
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