T. Pezel , T. Hovasse , S. Toupin , P. Garot , F. Sanguineti , S. Champagne , T. Chitiboi , P. Sharma , T. Unterseeh , J. Garot
{"title":"基于人工智能的全自动全周应变对应激性CMR患者预后的影响","authors":"T. Pezel , T. Hovasse , S. Toupin , P. Garot , F. Sanguineti , S. Champagne , T. Chitiboi , P. Sharma , T. Unterseeh , J. Garot","doi":"10.1016/j.acvdsp.2023.04.045","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><p>To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator<span> stress cardiovascular magnetic resonance (CMR) can provide incremental prognostic value.</span></p></div><div><h3>Method</h3><p><span>Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late </span>gadolinium<span><span> enhancement (LGE). Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as cardiovascular mortality or nonfatal myocardial infarction. </span>Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators.</span></p></div><div><h3>Results</h3><p>In 2670 patients [65<!--> <!-->±<!--> <!-->12<!--> <!-->years, 68% men, 1:1 matched patients (1335 with normal and 1335 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8–5.5) years] after adjustment for risk factors in the propensity-matched population (adjusted hazard ratio [HR]: 1.12 [95% CI: 1.06–1.18]) and patients with normal CMR (HR: 1.43 [95% CI: 1.30–1.57], both <em>P</em> <!--><<!--> <span>0.001), but not in patients with abnormal CMR (</span><em>P</em> <!-->=<!--> <!-->0.33). In patients with normal CMR, an increased stress-GCS<!--> <!-->><!--> <!-->−10% showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.27; NRI<!--> <!-->=<!--> <!-->0.538; IDI<!--> <!-->=<!--> <!-->0.108, all <em>P</em> <!--><<!--> <!-->0.001; LR-test <em>P</em> <!--><<!--> <!-->0.001).</p></div><div><h3>Conclusion</h3><p>Stress-GCS is independently associated with MACE in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors and stress CMR findings in the group of patients with normal CMR. Prognostic value of AI-based Stress-GCS (<span>Fig. 1</span>).</p></div>","PeriodicalId":8140,"journal":{"name":"Archives of Cardiovascular Diseases Supplements","volume":"15 3","pages":"Pages 264-265"},"PeriodicalIF":18.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR\",\"authors\":\"T. Pezel , T. Hovasse , S. Toupin , P. Garot , F. Sanguineti , S. Champagne , T. Chitiboi , P. Sharma , T. Unterseeh , J. Garot\",\"doi\":\"10.1016/j.acvdsp.2023.04.045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Introduction</h3><p>To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator<span> stress cardiovascular magnetic resonance (CMR) can provide incremental prognostic value.</span></p></div><div><h3>Method</h3><p><span>Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late </span>gadolinium<span><span> enhancement (LGE). Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as cardiovascular mortality or nonfatal myocardial infarction. </span>Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators.</span></p></div><div><h3>Results</h3><p>In 2670 patients [65<!--> <!-->±<!--> <!-->12<!--> <!-->years, 68% men, 1:1 matched patients (1335 with normal and 1335 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8–5.5) years] after adjustment for risk factors in the propensity-matched population (adjusted hazard ratio [HR]: 1.12 [95% CI: 1.06–1.18]) and patients with normal CMR (HR: 1.43 [95% CI: 1.30–1.57], both <em>P</em> <!--><<!--> <span>0.001), but not in patients with abnormal CMR (</span><em>P</em> <!-->=<!--> <!-->0.33). In patients with normal CMR, an increased stress-GCS<!--> <!-->><!--> <!-->−10% showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.27; NRI<!--> <!-->=<!--> <!-->0.538; IDI<!--> <!-->=<!--> <!-->0.108, all <em>P</em> <!--><<!--> <!-->0.001; LR-test <em>P</em> <!--><<!--> <!-->0.001).</p></div><div><h3>Conclusion</h3><p>Stress-GCS is independently associated with MACE in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors and stress CMR findings in the group of patients with normal CMR. Prognostic value of AI-based Stress-GCS (<span>Fig. 1</span>).</p></div>\",\"PeriodicalId\":8140,\"journal\":{\"name\":\"Archives of Cardiovascular Diseases Supplements\",\"volume\":\"15 3\",\"pages\":\"Pages 264-265\"},\"PeriodicalIF\":18.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archives of Cardiovascular Diseases Supplements\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1878648023001842\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Cardiovascular Diseases Supplements","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1878648023001842","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR
Introduction
To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular magnetic resonance (CMR) can provide incremental prognostic value.
Method
Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement (LGE). Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as cardiovascular mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators.
Results
In 2670 patients [65 ± 12 years, 68% men, 1:1 matched patients (1335 with normal and 1335 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8–5.5) years] after adjustment for risk factors in the propensity-matched population (adjusted hazard ratio [HR]: 1.12 [95% CI: 1.06–1.18]) and patients with normal CMR (HR: 1.43 [95% CI: 1.30–1.57], both P < 0.001), but not in patients with abnormal CMR (P = 0.33). In patients with normal CMR, an increased stress-GCS > −10% showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.27; NRI = 0.538; IDI = 0.108, all P < 0.001; LR-test P < 0.001).
Conclusion
Stress-GCS is independently associated with MACE in patients undergoing stress CMR, with an incremental prognostic value over traditional risk factors and stress CMR findings in the group of patients with normal CMR. Prognostic value of AI-based Stress-GCS (Fig. 1).
期刊介绍：
Archives of Cardiovascular Diseases Supplements is the official journal of the French Society of Cardiology. The journal publishes original peer-reviewed clinical and research articles, epidemiological studies, new methodological clinical approaches, review articles, editorials, and Images in cardiovascular medicine. The topics covered include coronary artery and valve diseases, interventional and pediatric cardiology, cardiovascular surgery, cardiomyopathy and heart failure, arrhythmias and stimulation, cardiovascular imaging, vascular medicine and hypertension, epidemiology and risk factors, and large multicenter studies. Additionally, Archives of Cardiovascular Diseases also publishes abstracts of papers presented at the annual sessions of the Journées Européennes de la Société Française de Cardiologie and the guidelines edited by the French Society of Cardiology.
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