Evangelos K Oikonomou, Gregory Holste, Neal Yuan, Andreas Coppi, Robert L McNamara, Norrisa Haynes, Amit N Vora, Eric J Velazquez, Fan Li, Venu Menon, Samir R Kapadia, Thomas M Gill, Girish N Nadkarni, Harlan M Krumholz, Zhangyang Wang, David Ouyang, Rohan Khera
{"title":"使用二维超声心动图深度学习的主动脉狭窄发展和进展的数字生物标志物。","authors":"Evangelos K Oikonomou, Gregory Holste, Neal Yuan, Andreas Coppi, Robert L McNamara, Norrisa Haynes, Amit N Vora, Eric J Velazquez, Fan Li, Venu Menon, Samir R Kapadia, Thomas M Gill, Girish N Nadkarni, Harlan M Krumholz, Zhangyang Wang, David Ouyang, Rohan Khera","doi":"10.1101/2023.09.28.23296234","DOIUrl":null,"url":null,"abstract":"<p><strong>Importance: </strong>Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up.</p><p><strong>Objective: </strong>A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression.</p><p><strong>Design setting and participants: </strong>We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024.</p><p><strong>Exposure: </strong>DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos.</p><p><strong>Main outcomes and measures: </strong>Annualized change in peak aortic valve velocity (AV-V<sub>max</sub>) and late (>6 months) aortic valve replacement (AVR).</p><p><strong>Results: </strong>A total of 12,599 participants were included in the echocardiographic study (YNHHS: <i>n</i>=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: <i>n</i>=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-V<sub>max</sub> (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-V<sub>max</sub>. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction.</p><p><strong>Conclusions and relevance: </strong>In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.</p>","PeriodicalId":18659,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/46/a1/nihpp-2023.09.28.23296234v1.PMC10557799.pdf","citationCount":"0","resultStr":"{\"title\":\"A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.\",\"authors\":\"Evangelos K Oikonomou, Gregory Holste, Neal Yuan, Andreas Coppi, Robert L McNamara, Norrisa Haynes, Amit N Vora, Eric J Velazquez, Fan Li, Venu Menon, Samir R Kapadia, Thomas M Gill, Girish N Nadkarni, Harlan M Krumholz, Zhangyang Wang, David Ouyang, Rohan Khera\",\"doi\":\"10.1101/2023.09.28.23296234\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Importance: </strong>Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up.</p><p><strong>Objective: </strong>A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression.</p><p><strong>Design setting and participants: </strong>We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024.</p><p><strong>Exposure: </strong>DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos.</p><p><strong>Main outcomes and measures: </strong>Annualized change in peak aortic valve velocity (AV-V<sub>max</sub>) and late (>6 months) aortic valve replacement (AVR).</p><p><strong>Results: </strong>A total of 12,599 participants were included in the echocardiographic study (YNHHS: <i>n</i>=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: <i>n</i>=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-V<sub>max</sub> (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-V<sub>max</sub>. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction.</p><p><strong>Conclusions and relevance: </strong>In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.</p>\",\"PeriodicalId\":18659,\"journal\":{\"name\":\"medRxiv : the preprint server for health sciences\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/46/a1/nihpp-2023.09.28.23296234v1.PMC10557799.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv : the preprint server for health sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2023.09.28.23296234\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2023.09.28.23296234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
背景：及时识别主动脉狭窄（AS）和值得干预的疾病分期需要经常进行超声心动图检查。然而，没有对所需监测频率进行个性化设置的策略。目的：探讨AI增强二维超声心动图在AS发展和进展风险分层中的作用。方法：这是一项多中心研究，共有12609名无严重AS的患者在新英格兰（n=8798,71[IQR60-80]岁，n=4250[48.3%]女性）和加利福尼亚州雪松西奈（n=3811,67[IQR54-78]岁，1688[44.3%]女性）接受了经胸超声心动图检查。我们分别使用多变量广义线性和Cox回归模型，研究了AI衍生的数字AS严重程度指数（DASSi；范围0-1）与i）主动脉瓣峰值流速（AV V max；m/sec/年）的纵向变化，以及ii）全因死亡率或主动脉瓣置换术（AVR）发生率的关系，并根据年龄、性别、种族/民族进行了调整，以及基线超声心动图测量。结果：中位随访时间为4.1[IQR 2.3-5.4]（新英格兰）和3.8[IQR 3.1-4.4]年（Cedars-Sinai）。在每个队列中，基线DASSi越高，AV V max的进展速度越快（每增加0.1：+0.033 m/s/年[95%CI:0.28-0.038，p p p结论：为二维超声心动图建立的人工智能模型可以对AS进展的风险进行分层，对社区的纵向监测有意义。摘要：在这项针对12609名无、轻度或中度主动脉狭窄（AS）患者的多中心队列研究中，我们探索了一种依赖于无多普勒的单视图二维视频的深度学习增强方法是否可以对AS的发展和进展风险进行分层。基于数字AS严重程度指数（DASSi）的视频表型确定了具有不同超声心动图和临床轨迹的患者亚组，这些患者亚组独立于基线AS分期和特征。结果在两个地理位置不同的队列和关键临床亚组中是一致的，支持使用深度学习增强的二维超声心动图作为对社区as传统评估的补充。
A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.
Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up.
Objective: A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression.
Design setting and participants: We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024.
Exposure: DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos.
Main outcomes and measures: Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR).
Results: A total of 12,599 participants were included in the echocardiographic study (YNHHS: n=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-Vmax. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction.
Conclusions and relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.
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