Can ML flag high-risk AS+HFpEF after TAVR?
In 326 TAVR patients, an 8-feature SVM reached validation AUC 0.756 for 12-month MACCE, explained with SHAP.
Source
Multimodal Visualization and Explainable Machine Learning-Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study
Study at a glance
- Design
- Cohort — Multicenter retrospective TAVR cohort with train/validation ML risk models
- N
- N=326 · 195 derivation + 131 external validation patients
- Population
- Older patients with severe aortic stenosis and HFpEF undergoing TAVR
- Outcome
- 12-month major adverse cardiovascular and cerebrovascular events predicted by explainable ML
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Can ML flag high-risk AS+HFpEF after TAVR?
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326 patients; train n=195; validation n=131.
This multicenter cohort study included 326 patients diagnosed with severe AS and HFpEF who underwent TAVR between January 2017 and December 2023. Patients were allocated to training (n=195) and independent validation (n=131) sets based on hospital affiliation.
SVM validation AUC was 0.756 (95% CI 0.631–0.881).
achieving an area under the curve of 0.756 (95% CI 0.631-0.881) in the independent validation set
What they did
Selected features via LASSO+Boruta, trained five ML models on 195 patients, validated on 131 from other hospitals, and interpreted with SHAP.
What they found
SVM performed best (AUC 0.756); features centered on age, NT-proBNP, and metabolic lipid-glucose indices; good calibration/net benefit claimed vs alternatives.
The limits
What it doesn't show
Prospective deployment benefit, or psychology-primary mechanisms (cardiac cohort framed for aging risk markers).
Key terms
- TAVR
- Transcatheter aortic valve replacement.
- HFpEF
- Heart failure with preserved ejection fraction.
- MACCE
- Major adverse cardiovascular and cerebrovascular events.
- SVM
- Support vector machine classifier/regressor.
- SHAP
- Shapley-value explanations of model predictions.
- TyG index
- Triglyceride-glucose index of insulin resistance.
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Validation AUC for SVM?
Common questions
N?
326 (195+131).
Best AUC?
SVM 0.756 validation.
Features?
8 clinical/metabolic markers.
Explainability?
SHAP values.
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