Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score
Name
e001990.full.pdf
Description
Published version
Size
2.55 MB
Format
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Checksum (MD5)
56823e1706d5d8b09ae4229e70837c25
Author(s)
Namasivayam, Mayooran
Myers, Paul D
Guttag, John V
Capoulade, Romain
Pibarot, Philippe
Picard, Michael H
Hung, Judy
Stultz, Collin M
Date Issued
May 2022
Journal
Open Heart
Publisher
BMJ
Citation
Namasivayam, Mayooran, Myers, Paul D, Guttag, John V, Capoulade, Romain, Pibarot, Philippe et al. 2022. "Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score." Open Heart, 9 (1).
Version
Final published version
Abstract
ObjectiveTo use echocardiographic and clinical features to develop an explainable clinical risk prediction model in patients with aortic stenosis (AS), including those with low-gradient AS (LGAS), using machine learning (ML).MethodsIn 1130 patients with moderate or severe AS, we used bootstrap lasso regression (BLR), an ML method, to identify echocardiographic and clinical features important for predicting the combined outcome of all-cause mortality or aortic valve replacement (AVR) within 5 years after the initial echocardiogram. A separate hold out set, from a different centre (n=540), was used to test the generality of the model. We also evaluated model performance with respect to each outcome separately and in different subgroups, including patients with LGAS.ResultsOut of 69 available variables, 26 features were identified as predictive by BLR and expert knowledge was used to further reduce this set to 9 easily available and input features without loss of efficacy. A ridge logistic regression model constructed using these features had an area under the receiver operating characteristic curve (AUC) of 0.74 for the combined outcome of mortality/AVR. The model reliably identified patients at high risk of death in years 2–5 (HRs ≥2.0, upper vs other quartiles, for years 2–5, p<0.05, p=not significant in year 1) and was also predictive in the cohort with LGAS (n=383, HRs≥3.3, p<0.05). The model performed similarly well in the independent hold out set (AUC 0.78, HR ≥2.5 in years 1–5, p<0.05).ConclusionIn two separate longitudinal databases, ML identified prognostic features and produced an algorithm that predicts outcome for up to 5 years of follow-up in patients with AS, including patients with LGAS. Our algorithm, the Aortic Stenosis Risk (ASteRisk) score, is available online for public use.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1136/openhrt-2022-001990
