A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity |
| |
Institution: | 1. West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA;2. M&H Research, LLC, San Antonio, Texas, USA;3. British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom;4. National Heart Centre, Singapore;5. University of Ottawa Heart Institute, Ottawa, Canada;6. Department of Medicine, Institut Universitaire de Cardiologie et de Pneumologie de Québec/Québec Heart and Lung Institute, Laval University, Québec, Canada |
| |
Abstract: | ObjectivesThe authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity.BackgroundIn AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty.MethodsUsing echocardiography (ECHO) measurements (ECHO cohort, n = 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine-learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT cohort, n = 752) and cardiovascular magnetic resonance (CMR) imaging (CMR cohort, n = 160). The classifier’s prognostic value was further validated using clinical outcomes (aortic valve replacement AVR] and death) observed in the ECHO and CMR cohorts.ResultsIn 1,964 patients from the 3 multi-institutional cohorts, 1,346 (68%) subjects had either nonsevere or discordant AS severity. Machine learning identified 1,117 (57%) patients as having high-severity and 847 (43%) as having low-severity AS. High-severity patients in CT and CMR cohorts had higher valve calcium scores and left ventricular mass and fibrosis, respectively than the low-severity group. In the ECHO cohort, progression to AVR and progression to death in patients who did not receive AVR was faster in the high-severity group. Compared with the conventional classification of disease severity, machine-learning–based severity classification improved discrimination (integrated discrimination improvement: 0.07; 95% confidence interval: 0.02 to 0.12) and reclassification (net reclassification improvement: 0.17; 95% confidence interval: 0.11 to 0.23) for the outcome of AVR at 5 years. For both ECHO and CMR cohorts, we observed prognostic value of the machine-learning classifications for subgroups with asymptomatic, nonsevere or discordant AS.ConclusionsMachine learning can integrate ECHO measurements to augment the classification of disease severity in most patients with AS, with major potential to optimize the timing of AVR. |
| |
Keywords: | aortic stenosis machine learning topological data analysis AS"} {"#name":"keyword" "$":{"id":"kwrd0030"} "$$":[{"#name":"text" "_":"aortic stenosis AVR"} {"#name":"keyword" "$":{"id":"kwrd0040"} "$$":[{"#name":"text" "_":"aortic valve replacement CMR"} {"#name":"keyword" "$":{"id":"kwrd0060"} "$$":[{"#name":"text" "_":"cardiovascular magnetic resonance CT"} {"#name":"keyword" "$":{"id":"kwrd0070"} "$$":[{"#name":"text" "_":"computed tomography ECHO"} {"#name":"keyword" "$":{"id":"kwrd0080"} "$$":[{"#name":"text" "_":"echocardiography HS"} {"#name":"keyword" "$":{"id":"kwrd0090"} "$$":[{"#name":"text" "_":"high severity IDI"} {"#name":"keyword" "$":{"id":"kwrd0100"} "$$":[{"#name":"text" "_":"the integrated discrimination improvement LGE"} {"#name":"keyword" "$":{"id":"kwrd0120"} "$$":[{"#name":"text" "_":"late gadolinium enhancement LS"} {"#name":"keyword" "$":{"id":"kwrd0130"} "$$":[{"#name":"text" "_":"low severity LV"} {"#name":"keyword" "$":{"id":"kwrd0140"} "$$":[{"#name":"text" "_":"left ventricular NRI"} {"#name":"keyword" "$":{"id":"kwrd0150"} "$$":[{"#name":"text" "_":"net reclassification index TDA"} {"#name":"keyword" "$":{"id":"kwrd0160"} "$$":[{"#name":"text" "_":"topological data analysis |
本文献已被 ScienceDirect 等数据库收录! |
|