Robert J H Miller, Nipun Manral, Andrew Lin, Aakash Shanbhag, Caroline Park, Jacek Kwiecinski, Aditya Killekar, Priscilla McElhinney, Hidenari Matsumoto, Aryabod Razipour, Kajetan Grodecki, Alan C Kwan, Donghee Han, Keiichiro Kuronuma, Guadalupe Flores Tomasino, Jolien Geers, Markus Goeller, Mohamed Marwan, Heidi Gransar, Balaji K Tamarappoo, Sebastien Cadet, Victor Y Cheng, Stephan Achenbach, Stephen J Nicholls, Dennis T Wong, Lu Chen, J Jane Cao, Daniel S Berman, Marc R Dweck, David E Newby, Michelle C Williams, Piotr J Slomka, Damini Dey
{"title":"通过人工智能冠状动脉斑块分析得出特定患者的心肌梗死风险阈值。","authors":"Robert J H Miller, Nipun Manral, Andrew Lin, Aakash Shanbhag, Caroline Park, Jacek Kwiecinski, Aditya Killekar, Priscilla McElhinney, Hidenari Matsumoto, Aryabod Razipour, Kajetan Grodecki, Alan C Kwan, Donghee Han, Keiichiro Kuronuma, Guadalupe Flores Tomasino, Jolien Geers, Markus Goeller, Mohamed Marwan, Heidi Gransar, Balaji K Tamarappoo, Sebastien Cadet, Victor Y Cheng, Stephan Achenbach, Stephen J Nicholls, Dennis T Wong, Lu Chen, J Jane Cao, Daniel S Berman, Marc R Dweck, David E Newby, Michelle C Williams, Piotr J Slomka, Damini Dey","doi":"10.1161/CIRCIMAGING.124.016958","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.</p><p><strong>Methods: </strong>In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.</p><p><strong>Results: </strong>Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; <i>P</i>=0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume.</p><p><strong>Conclusions: </strong>Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.</p>","PeriodicalId":10202,"journal":{"name":"Circulation: Cardiovascular Imaging","volume":"17 10","pages":"e016958"},"PeriodicalIF":6.5000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.\",\"authors\":\"Robert J H Miller, Nipun Manral, Andrew Lin, Aakash Shanbhag, Caroline Park, Jacek Kwiecinski, Aditya Killekar, Priscilla McElhinney, Hidenari Matsumoto, Aryabod Razipour, Kajetan Grodecki, Alan C Kwan, Donghee Han, Keiichiro Kuronuma, Guadalupe Flores Tomasino, Jolien Geers, Markus Goeller, Mohamed Marwan, Heidi Gransar, Balaji K Tamarappoo, Sebastien Cadet, Victor Y Cheng, Stephan Achenbach, Stephen J Nicholls, Dennis T Wong, Lu Chen, J Jane Cao, Daniel S Berman, Marc R Dweck, David E Newby, Michelle C Williams, Piotr J Slomka, Damini Dey\",\"doi\":\"10.1161/CIRCIMAGING.124.016958\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.</p><p><strong>Methods: </strong>In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.</p><p><strong>Results: </strong>Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; <i>P</i>=0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume.</p><p><strong>Conclusions: </strong>Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.</p>\",\"PeriodicalId\":10202,\"journal\":{\"name\":\"Circulation: Cardiovascular Imaging\",\"volume\":\"17 10\",\"pages\":\"e016958\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Circulation: Cardiovascular Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1161/CIRCIMAGING.124.016958\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Circulation: Cardiovascular Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1161/CIRCIMAGING.124.016958","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/30 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Patient-Specific Myocardial Infarction Risk Thresholds From AI-Enabled Coronary Plaque Analysis.
Background: Plaque quantification from coronary computed tomography angiography has emerged as a valuable predictor of cardiovascular risk. Deep learning can provide automated quantification of coronary plaque from computed tomography angiography. We determined per-patient age- and sex-specific distributions of deep learning-based plaque measurements and further evaluated their risk prediction for myocardial infarction in external samples.
Methods: In this international, multicenter study of 2803 patients, a previously validated deep learning system was used to quantify coronary plaque from computed tomography angiography. Age- and sex-specific distributions of coronary plaque volume were determined from 956 patients undergoing computed tomography angiography for stable coronary artery disease from 5 cohorts. Multicenter external samples were used to evaluate associations between coronary plaque percentiles and myocardial infarction.
Results: Quantitative deep learning plaque volumes increased with age and were higher in male patients. In the combined external sample (n=1847), patients in the ≥75th percentile of total plaque volume (unadjusted hazard ratio, 2.65 [95% CI, 1.47-4.78]; P=0.001) were at increased risk of myocardial infarction compared with patients below the 50th percentile. Similar relationships were seen for most plaque volumes and persisted in multivariable analyses adjusting for clinical characteristics, coronary artery calcium, stenosis, and plaque volume, with adjusted hazard ratios ranging from 2.38 to 2.50 for patients in the ≥75th percentile of total plaque volume.
Conclusions: Per-patient age- and sex-specific distributions for deep learning-based coronary plaque volumes are strongly predictive of myocardial infarction, with the highest risk seen in patients with coronary plaque volumes in the ≥75th percentile.
期刊介绍:
Circulation: Cardiovascular Imaging, an American Heart Association journal, publishes high-quality, patient-centric articles focusing on observational studies, clinical trials, and advances in applied (translational) research. The journal features innovative, multimodality approaches to the diagnosis and risk stratification of cardiovascular disease. Modalities covered include echocardiography, cardiac computed tomography, cardiac magnetic resonance imaging and spectroscopy, magnetic resonance angiography, cardiac positron emission tomography, noninvasive assessment of vascular and endothelial function, radionuclide imaging, molecular imaging, and others.
Article types considered by Circulation: Cardiovascular Imaging include Original Research, Research Letters, Advances in Cardiovascular Imaging, Clinical Implications of Molecular Imaging Research, How to Use Imaging, Translating Novel Imaging Technologies into Clinical Applications, and Cardiovascular Images.