An Extreme Gradient Boosting machine learning model accurately predicted subclinical leaflet thrombosis at 6 months after transcatheter aortic valve implantation (AUC 0.91; 95% CI 0.87-0.94).
Cohort (n=128)
Can an Extreme Gradient Boosting machine learning model accurately predict subclinical leaflet thrombosis in patients receiving a self-expanding transcatheter heart valve?
Machine learning using Accumulated Local Effects curves can accurately predict and interpret the dynamic risk of subclinical leaflet thrombosis after TAVI based on valve geometry and peri-procedural haematological changes.
Effect estimate: AUC 0.91 (95% CI 0.87-0.94)
Abstract Objectives Subclinical leaflet thrombosis is an early form of bioprosthetic valve dysfunction after transcatheter aortic valve implantation. Predicting subclinical leaflet thrombosis remains challenging. We aimed to apply machine learning not only to identify predictors but also to interpret their dynamic, non-linear effects on subclinical leaflet thrombosis risk using Accumulated Local Effects curves, a robust method that accounts for variable collinearity. Methods A prospective cohort of 128 consecutive patients receiving a self-expanding transcatheter heart valve underwent multimodality imaging and haematological profiling. The primary outcome was subclinical leaflet thrombosis on 6-month computed tomography. An Extreme Gradient Boosting classifier was trained on 126 variables. Model performance was evaluated via nested cross-validation. Interpretability used SHapley Additive exPlanations and Accumulated Local Effects plots. Results Subclinical thrombosis was detected in 22 patients (17.1%). Extreme Gradient Boosting model demonstrated excellent discrimination (AUC: 0.91, 95% CI: 0.87–0.94) and calibration (Brier score: 0.09). SHapley Additive exPlanations analysis identified the top predictors: bicuspid valve anatomy (mean: 0.45), baseline haemoglobin (0.28), peri-procedural Δhaematocrit (0.19), prosthesis eccentricity (0.14), and post-procedural platelet nadir (0.09). Accumulated Local Effects curves revealed a U-shaped association for baseline haemoglobin (lowest risk around 13 g/dL) a monotonic rise in SLT risk with increasing prosthesis eccentricity and greater peri-procedural declines in haematocrit, and a progressively higher risk with post-procedural thrombocytopenia. Conclusions This study introduces Accumulated Local Effectsplots to cardiovascular medicine, translating predictions into interpretable risk curves that show how leaflet thrombosis risk evolves across key predictors, linking valve geometry and peri-procedural haematological changes to guide patient surveillance after transcatheter aortic valve replacement.
Moscarelli et al. (Tue,) conducted a cohort in Subclinical leaflet thrombosis after transcatheter aortic valve implantation (n=128). Extreme Gradient Boosting classifier was evaluated on Subclinical leaflet thrombosis on 6-month computed tomography (AUC 0.91, 95% CI 0.87-0.94). An Extreme Gradient Boosting machine learning model accurately predicted subclinical leaflet thrombosis at 6 months after transcatheter aortic valve implantation (AUC 0.91; 95% CI 0.87-0.94).