Jiajun Li, Rashid Alavi, Wangde Dai, Ray V. Matthews, Robert A. Kloner, Niema M. Pahlevan
{"title":"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","authors":"Jiajun Li, Rashid Alavi, Wangde Dai, Ray V. Matthews, Robert A. Kloner, Niema M. Pahlevan","doi":"10.1096/fj.202502111R","DOIUrl":null,"url":null,"abstract":"<p>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 <i>N</i> = 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.</p>","PeriodicalId":50455,"journal":{"name":"The FASEB Journal","volume":"39 17","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://faseb.onlinelibrary.wiley.com/doi/epdf/10.1096/fj.202502111R","citationCount":"0","resultStr":"{\"title\":\"Assessment of Myocardial Injury Size Metrics Using Carotid Pressure Waveform: Proof-of-Concept in Coronary Occlusion/Reperfusion Rat Model\",\"authors\":\"Jiajun Li, Rashid Alavi, Wangde Dai, Ray V. Matthews, Robert A. Kloner, Niema M. Pahlevan\",\"doi\":\"10.1096/fj.202502111R\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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 <i>N</i> = 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.</p>\",\"PeriodicalId\":50455,\"journal\":{\"name\":\"The FASEB Journal\",\"volume\":\"39 17\",\"pages\":\"\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-09-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://faseb.onlinelibrary.wiley.com/doi/epdf/10.1096/fj.202502111R\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The FASEB Journal\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.202502111R\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The FASEB Journal","FirstCategoryId":"99","ListUrlMain":"https://faseb.onlinelibrary.wiley.com/doi/10.1096/fj.202502111R","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
Assessment of Myocardial Injury Size Metrics Using Carotid Pressure Waveform: Proof-of-Concept in Coronary Occlusion/Reperfusion Rat Model
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.
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.