Early cerebrovascular events (CVEs) following transcatheter aortic valve replacement (TAVR) are severe complications, but effective methods for predicting and preventing these events have not been well established. A systematic review and meta-analysis were performed to identify significant predictors of early CVEs post-TAVR.

MEDLINE/Embase databases were searched for articles published between December 2015 and April 2023. Original studies evaluating predictors of CVEs within 30 days post-TAVR after adjusting for confounders were included. Two investigators independently extracted data following the PRISMA statement. Meta-analyses of multivariable data were performed using DerSimonian and Laird random-effects models, with results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Robustness was assessed via Harbord’s test, nonparametric trim-and-fill analysis, leave-one-out sensitivity analysis, the QUIPS quality assessment tools, meta-regression, and subgroup analyses.

Among the 74 included studies, multivariate meta-analyses identified 11 predictors of early CVEs, including 9 patient-level predictors-a CHA₂DS₂-VASc ≥ 5, no prior heart failure, diabetes, isolated aortic stenosis, carotid artery stenosis, peripheral artery disease, advanced age, New York Heart Association class ≥ III, and significant left ventricular outflow tract calcification-and 2 procedure-level predictors: the absence of cerebral embolization protection and post-dilation. Additionally, 10 patient-level factors and 5 procedure-level factors were not associated with early CVEs, although significant heterogeneity was observed in most analyses.

This study identified multiple patient-level and procedure-level factors associated or not associated with early CVEs after TAVR. These findings support the development of a comprehensive risk prediction model that can accommodate diverse patient populations and evolving procedural techniques, thereby enhancing clinical risk management strategies.

Epicardial adipose tissue (EAT) radiomics derived from cardiac computed tomography (CT) images may provide insights into EAT characteristics, which can further predict regression of left ventricular mass index (LVMI) after transcatheter aortic valve replacement (TAVR). This study aimed to develop and validate a radiomics nomogram based on pre-procedural EAT CT to predict inadequate LVMI regression following TAVR.

Inadequate LVMI regression was defined as ΔLVMI% < 15% at one-year post TAVR. Radiomics features from pre-procedural CT images were selected mainly by least absolute shrinkage and selection operator algorithm. The patients were randomly divided into the training and validation cohorts to establish and evaluate three feature classifier models based on the selected features, using which the Radiomics scores (Radscores) were then calculated. A radiomics nomogram was constructed using independent risk factors and further assessed using area under the curve, calibration curve, and decision curve analysis.

A total of 144 consecutive TAVR patients (42 patients with inadequate and 102 patients with adequate LVMI regression) were randomly assigned to the training and validation cohorts (116 patients and 28 patients, respectively). A total of 1130 radiomics features from each patient yielded 6 features for the Radscore construction after selection, with logistic regression and support vector machine models favored. Subsequently, a nomogram based solely on the Radscore was constructed, with an area under the curve of 0.743 in the validation cohort, along with favorable decision curve analysis and calibration curves.

The developed radiomics nomogram, serving as a non-invasive tool, achieved satisfactory preoperative prediction of inadequate LVMI regression in TAVR patients, thereby facilitating clinical management.