Predicting outcomes in patients with aortic stenosis using machine learning: the Aortic Stenosis Risk (ASteRisk) score

• Main Authors: Namasivayam, Mayooran (Author), Myers, Paul D (Author), Guttag, John V (Author), Capoulade, Romain (Author), Pibarot, Philippe (Author), Picard, Michael H (Author), Hung, Judy (Author), Stultz, Collin M (Author)
• Format: Article
• Language: English
• Published: BMJ, 2022-06-29T16:23:55Z.
• Subjects: Article
• Online Access: Get fulltext

 <jats:sec><jats:title>Objective</jats:title><jats:p>To 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).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>In 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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Out 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).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>In 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.</jats:p></jats:sec>