Clinical significance of machine learning algorithm in predicting PPM during TAVR in small annuli.

Compared with left heart catheterization (LHC), the pressure gradient of an aortic valve (PGAV) measured by echocardiography during transcatheter aortic valve replacement (TAVR) in small annuli is overestimated. The purpose of this study was to improve the accuracy of PGAV measurements by echocardiography in small annuli and to evaluate the influence of PGAV on prognosis. The internal derivation cohort included 273 consecutive patients with aortic stenosis and a small annulus (computed tomographic scan showing an annulus circumference < 72 mm or area < 400 mm2) who underwent TAVR. Patients completed transthoracic echocardiography (TTE) and LHC measurements during TAVR, and an extreme gradient boosting (XGBoost) algorithm was trained. The primary outcome was a composite end point of all-cause mortality and readmission for heart failure. The mean PGAV level measured by TTE was overestimated compared to the LHC measurement {52.5 [interquartile range: 47.5-57.0] mmHg vs. 42.5 (interquartile range: 38.0-46.0) mmHg, P < 0.001}. After adjustments to the XGBoost, the mean PGAV measured by TTE could be significantly improved [Pearson correlation coefficient = 0.94, P < 0.001]. Importantly, patients with a predicted mean PGAV ≥ 68.6 mmHg showed a significantly increased incidence of composite end points at 2 years after the procedures (40.7% vs. 16.0%, P < 0.001). The XGBoost model could effectively improve the accuracy of the mean PGAV measured by TTE during TAVR, and the predicted mean increase in the PGAV level may lead to a worse prognosis.Clinical Trial Registration: ClinicalTrials.gov Protocol Registration System (NCT05044338).