This study aimed to determine whether artificial intelligence (AI)-based automated
assessment of left atrioventricular coupling index (LACI) can provide incremental
value above other traditional risk factors for predicting mortality among patients
with severe aortic stenosis (AS) undergoing coronary CT angiography (CCTA) before
transcatheter aortic valve replacement (TAVR).This retrospective study evaluated patients
with severe AS who underwent CCTA examination before TAVR between September 2014 and
December 2020. An AI-prototype software fully automatically calculated left atrial
and left ventricular end-diastolic volumes and LACI was defined by the ratio between
them. Uni- and multivariate Cox proportional hazard methods were used to identify
the predictors of mortality in models adjusting for relevant significant parameters
and Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) score.A total
of 656 patients (77 years [IQR, 71-84 years]; 387 [59.0 %] male) were included in
the final cohort. The all-cause mortality rate was 21.6 % over a median follow-up
time of 24 (10-40) months. When adjusting for clinical confounders, LACI ≥43.7 %
independently predicted mortality (adjusted HR, 1.52, [95 % CI: 1.03, 2.22]; p =
0.032). After adjusting for the STS-PROM score in a separate model, LACI ≥43.7 %
remained an independent prognostic parameter (adjusted HR, 1.47, [95 % CI: 1.03-2.08];
p = 0.031). In a sub-analysis of patients with preserved left ventricular ejection
fraction, LACI remained a significant predictor (adjusted HR, 1.72 [95 % CI: 1.02,
2.89]; p = 0.042).AI-based fully automated assessment of LACI can be used independently
to predict mortality in patients undergoing TAVR, including those with preserved LVEF.
