Back to Search
Start Over
A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity
- Source :
- JACC: Cardiovascular Imaging. 14:1707-1720
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Objectives The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity. Background In 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. Methods Using 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. Results In 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. Conclusions Machine 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.
- Subjects :
- macromolecular substances
030204 cardiovascular system & hematology
Machine learning
computer.software_genre
Severity of Illness Index
Asymptomatic
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
Aortic valve replacement
Predictive Value of Tests
Humans
Medicine
Radiology, Nuclear Medicine and imaging
Decompensation
Adverse effect
Heart Valve Prosthesis Implantation
medicine.diagnostic_test
business.industry
Magnetic resonance imaging
Aortic Valve Stenosis
medicine.disease
Confidence interval
3. Good health
Stenosis
Phenotype
Aortic Valve
Cohort
Artificial intelligence
medicine.symptom
Cardiology and Cardiovascular Medicine
business
computer
Subjects
Details
- ISSN :
- 1936878X
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- JACC: Cardiovascular Imaging
- Accession number :
- edsair.doi.dedup.....c04f2b9028f1de8b148b215605359f16