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Abstract：
BACKGROUND: This study aims to develop a post-procedural risk prediction model for permanent pacemaker implantation (PPMI) in patients treated with transcatheter aortic valve replacement (TAVR).
METHODS: 336 patients undergoing TAVR at a single institution were included for model derivation. For primary analysis, multivariate logistic regression model was used to evaluate predictors and a risk score system was devised based on the prediction model. For secondary analysis, a Cox proportion hazard model was performed to assess characteristics associated with the time from TAVR to PPMI. The model was validated internally via bootstrap and externally using an independent cohort.
RESULTS: 48 (14.3%) patients in the derivation set had PPMI after TAVR. Prior right bundle branch block (RBBB, OR: 10.46; p < 0.001), pre-procedural aortic valve area (AVA, OR: 1.41; p = 0.004) and post- to pre-procedural AVA ratio (OR: 1.72; p = 0.043) were identified as independent predictors for PPMI. AUC was 0.7 and 0.71 in the derivation and external validation set. Prior RBBB (HR: 5.07; p < 0.001), pre-procedural AVA (HR: 1.33; p = 0.001), post-procedural AVA to prosthetic nominal area ratio (HR: 0.02; p = 0.039) and post- to pre-procedural troponin-T difference (HR: 1.72; p = 0.017) are independently associated with time to PPMI.
CONCLUSIONS: The post-procedural prediction model achieved high discriminative power and accuracy for PPMI. The risk score system was constructed and validated, providing an accessible tool in clinical setting regarding the Chinese population.
METHODS: 336 patients undergoing TAVR at a single institution were included for model derivation. For primary analysis, multivariate logistic regression model was used to evaluate predictors and a risk score system was devised based on the prediction model. For secondary analysis, a Cox proportion hazard model was performed to assess characteristics associated with the time from TAVR to PPMI. The model was validated internally via bootstrap and externally using an independent cohort.
RESULTS: 48 (14.3%) patients in the derivation set had PPMI after TAVR. Prior right bundle branch block (RBBB, OR: 10.46; p < 0.001), pre-procedural aortic valve area (AVA, OR: 1.41; p = 0.004) and post- to pre-procedural AVA ratio (OR: 1.72; p = 0.043) were identified as independent predictors for PPMI. AUC was 0.7 and 0.71 in the derivation and external validation set. Prior RBBB (HR: 5.07; p < 0.001), pre-procedural AVA (HR: 1.33; p = 0.001), post-procedural AVA to prosthetic nominal area ratio (HR: 0.02; p = 0.039) and post- to pre-procedural troponin-T difference (HR: 1.72; p = 0.017) are independently associated with time to PPMI.
CONCLUSIONS: The post-procedural prediction model achieved high discriminative power and accuracy for PPMI. The risk score system was constructed and validated, providing an accessible tool in clinical setting regarding the Chinese population.
摘要：
背景：本研究旨在开发一种用于经导管主动脉瓣置换术（TAVR）治疗的患者的永久性起搏器植入术（PPMI）的术后风险预测模型。
方法：纳入336名在单一机构接受TAVR的患者进行模型推导。对于主要分析，采用多因素logistic回归模型对预测因子进行评价，并根据预测模型设计风险评分系统。对于二次分析，采用Cox比例风险模型评估与TAVR至PPMI时间相关的特征.该模型通过引导进行内部验证，并使用独立队列进行外部验证。
结果：推导组中48例（14.3%）患者在TAVR后发生PPMI。先前右束支传导阻滞（RBBB，OR：10.46；p<0.001），术前主动脉瓣面积(AVA,OR：1.41；p=0.004）和术后至手术前AVA比率（OR：1.72；p=0.043）被确定为PPMI的独立预测因子。在推导和外部验证集中，AUC分别为0.7和0.71。先前RBBB（HR：5.07；p<0.001），术前AVA（HR：1.33；p=0.001），术后AVA与假体标称面积比(HR:0.02;p=0.039)和术后与术前肌钙蛋白T差异(HR:1.72;p=0.017)与PPMI时间独立相关。
结论：术后预测模型对PPMI具有较高的判别能力和准确性。构建并验证了风险评分系统,为中国人口提供临床环境中可访问的工具。
方法：纳入336名在单一机构接受TAVR的患者进行模型推导。对于主要分析，采用多因素logistic回归模型对预测因子进行评价，并根据预测模型设计风险评分系统。对于二次分析，采用Cox比例风险模型评估与TAVR至PPMI时间相关的特征.该模型通过引导进行内部验证，并使用独立队列进行外部验证。
结果：推导组中48例（14.3%）患者在TAVR后发生PPMI。先前右束支传导阻滞（RBBB，OR：10.46；p<0.001），术前主动脉瓣面积(AVA,OR：1.41；p=0.004）和术后至手术前AVA比率（OR：1.72；p=0.043）被确定为PPMI的独立预测因子。在推导和外部验证集中，AUC分别为0.7和0.71。先前RBBB（HR：5.07；p<0.001），术前AVA（HR：1.33；p=0.001），术后AVA与假体标称面积比(HR:0.02;p=0.039)和术后与术前肌钙蛋白T差异(HR:1.72;p=0.017)与PPMI时间独立相关。
结论：术后预测模型对PPMI具有较高的判别能力和准确性。构建并验证了风险评分系统,为中国人口提供临床环境中可访问的工具。
