Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：在中国和世界范围内，经导管主动脉瓣置换术（TAVR）的数量迅速增加，导致人们越来越关注4D-CT随访期间检测到的低衰减小叶增厚（HALT）。据报道，HALT可能会影响人工瓣膜的耐久性。因此，早期识别这些患者并及时部署抗凝治疗尤为重要。
方法：我们回顾性招募了在阜外医院接受TAVR手术的234例连续患者。我们从4D-CT中收集了TAVR手术后经导管心脏瓣膜（THV）的临床信息并提取了形态学特征参数。进行LASSO分析以选择重要特征。构建了三个模型，封装临床因素（模型1），形态特征参数(模型2)，和所有在一起（模型3），识别HALT患者。绘制受试者工作特征（ROC）曲线和决策曲线分析（DCA）以评估模型的判别能力。开发了HALT的列线图，并通过自举重新采样进行了验证。
结果：在我们的研究患者中，与模型1（AUC=0.674，p=0.032）和模型2（AUC=0.675，p=0.021）相比，模型3（AUC=0.738）显示出更高的识别效果。内部引导验证还显示模型3具有与初始逐步模型相似的统计功效(AUC=0.72395CI:0.661-0.786)。总的来说,模型3在TAVR患者中HALT的鉴定中被评为最佳。
结论：将患者临床因素与基于CT的形态学参数相结合的综合预测模型在预测TAVR患者HALT的发生方面具有较好的疗效。
方法：我们回顾性招募了在阜外医院接受TAVR手术的234例连续患者。我们从4D-CT中收集了TAVR手术后经导管心脏瓣膜（THV）的临床信息并提取了形态学特征参数。进行LASSO分析以选择重要特征。构建了三个模型，封装临床因素（模型1），形态特征参数(模型2)，和所有在一起（模型3），识别HALT患者。绘制受试者工作特征（ROC）曲线和决策曲线分析（DCA）以评估模型的判别能力。开发了HALT的列线图，并通过自举重新采样进行了验证。
结果：在我们的研究患者中，与模型1（AUC=0.674，p=0.032）和模型2（AUC=0.675，p=0.021）相比，模型3（AUC=0.738）显示出更高的识别效果。内部引导验证还显示模型3具有与初始逐步模型相似的统计功效(AUC=0.72395CI:0.661-0.786)。总的来说,模型3在TAVR患者中HALT的鉴定中被评为最佳。
结论：将患者临床因素与基于CT的形态学参数相结合的综合预测模型在预测TAVR患者HALT的发生方面具有较好的疗效。
