Prediction of hypoattenuating leaflet thickening in patients undergoing transcatheter aortic valves replacement based on clinical factors and 4D-computed tomography morphological characteristics: A retrospective cross-sectional study

• Published in: International journal of cardiology Vol. 410; p. 132219
• Main Authors: Shi, Wence, Feng, Dejing, Hu, Xiangming, Wang, Can, Niu, Guannan, Zhao, Zhenyan, Zhang, Hongliang, Wang, Moyang, Wu, Yongjian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024
• Subjects: Computed tomography; Hypoattenuating leaflet thickening; Nomogram; Transcatheter aortic valve replacement; PCT; TTE; LVEF; TAVR; Cr; AUC; HDL; Computed tomography; LDL; Hypoattenuating leaflet thickening; WBC; PLT; Hb; BMI; HALT; NSH; THV; MR; Nomogram; ROC; LVEDD; Transcatheter aortic valve replacement; NSV; Alb; TR
• ISSN: 0167-5273; 1874-1754; 1874-1754
• DOI: 10.1016/j.ijcard.2024.132219

The rapid increase in the number of transcatheter aortic valve replacement (TAVR) procedures in China and worldwide has led to growing attention to hypoattenuating leaflet thickening (HALT) detected during follow-up by 4D-CT. It's reported that HALT may impact the durability of prosthetic valve. Early identification of these patients and timely deployment of anticoagulant therapy are therefore particularly important. We retrospectively recruited 234 consecutive patients who underwent TAVR procedure in Fuwai Hospital. We collected clinical information and extracted morphological characteristics parameters of the transcatheter heart valve (THV) post TAVR procedure from 4D-CT. LASSO analysis was conducted to select important features. Three models were constructed, encapsulating clinical factors (Model 1), morphological characteristics parameters (Model 2), and all together (Model 3), to identify patients with HALT. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were plotted to evaluate the discriminatory ability of models. A nomogram for HALT was developed and verified by bootstrap resampling. In our study patients, Model 3 (AUC = 0.738) showed higher recognition effectiveness compared to Model 1 (AUC = 0.674, p = 0.032) and Model 2 (AUC = 0.675, p = 0.021). Internal bootstrap validation also showed that Model 3 had a statistical power similar to that of the initial stepwise model (AUC = 0.723 95%CI: 0.661–0.786). Overall, Model 3 was rated best for the identification of HALT in TAVR patients. A comprehensive predictive model combining patient clinical factors with CT-based morphology parameters has superior efficacy in predicting the occurrence of HALT in TAVR patients. •Six features (four clinical factors [lipoprotein(a), hs-CRP, hypertension, hyperlipidemia], two morphological characteristics parameters [neosinus height, neosinus volume]) were selected as important features for HALT•A comprehensive model combining clinical factors and CT-based morphology parameters has the highest efficacy in predicting the occurrence of HALT in patients undergone TAVR.•Our findings may help clinicians monitor and rapidly identify patients with high risk for HALT, deploy anticoagulants to them early and reduce future bioprosthetic valve thrombosis.