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Objective and significance <p>To systematically evaluate and model the risk factors for new - onset conduction block after transcatheter aortic valve replacement, aiming to establish a scientific risk - prediction nomogram model. This model is intended to guide clinical screening of high - risk patients, develop more targeted prevention and treatment strategies, and ultimately improve the overall prognosis and quality of life of patients by effectively reducing the incidence of postoperative conduction block.</p> Methods <p>Data of 163 patients who underwent transcatheter aortic valve replacement in our hospital from January 2022 to November 2024 were collected, including patients’ anatomical characteristics, surgical operation parameters, and other self - factors. First, the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis method was used to optimize variable selection. By introducing the predictive factors selected in the LASSO regression analysis, univariate and multivariate logistic regression analyses were then carried out to establish a prediction model. According to the selected variables, a visual nomogram was established. Subsequently, the model was verified by the receiver operating characteristic curve, calibration curve, and decision curve analysis.</p> Results <p>Through LASSO regression analysis, three predictive factors, namely age, basal valve diameter(mm), and annulus size, were screened out from the 29 variables studied. The model constructed using these three predictive factors had good predictive ability, with an area under the ROC curve of 0.988 in the training set and 0.989 in the validation set. The DCA curve showed that the nomogram could be applied clinically when the risk threshold was between 0.1 and 0.7.</p> Conclusion <p>Incorporating age, basal valve diameter(mm), and annulus size into the risk - prediction model significantly improves the accuracy of predicting the risk of new - onset conduction block in patients after transcatheter aortic valve replacement. This strategy provides a more reliable basis for preoperative risk stratification and helps optimize clinical decision - making and patient management.</p>

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Construction and validation of risk prediction models for new- onset conduction block complications after transcatheter aortic valve replacement

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月22日

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  • Lei Chen,
  • Shuyi Zeng,
  • Ming Li,
  • Wang Liao,
  • Jiangsheng Liang,
  • Shikun Yang,
  • Xiangwen Liang

摘要

Objective and significance

To systematically evaluate and model the risk factors for new - onset conduction block after transcatheter aortic valve replacement, aiming to establish a scientific risk - prediction nomogram model. This model is intended to guide clinical screening of high - risk patients, develop more targeted prevention and treatment strategies, and ultimately improve the overall prognosis and quality of life of patients by effectively reducing the incidence of postoperative conduction block.

Methods

Data of 163 patients who underwent transcatheter aortic valve replacement in our hospital from January 2022 to November 2024 were collected, including patients’ anatomical characteristics, surgical operation parameters, and other self - factors. First, the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis method was used to optimize variable selection. By introducing the predictive factors selected in the LASSO regression analysis, univariate and multivariate logistic regression analyses were then carried out to establish a prediction model. According to the selected variables, a visual nomogram was established. Subsequently, the model was verified by the receiver operating characteristic curve, calibration curve, and decision curve analysis.

Results

Through LASSO regression analysis, three predictive factors, namely age, basal valve diameter(mm), and annulus size, were screened out from the 29 variables studied. The model constructed using these three predictive factors had good predictive ability, with an area under the ROC curve of 0.988 in the training set and 0.989 in the validation set. The DCA curve showed that the nomogram could be applied clinically when the risk threshold was between 0.1 and 0.7.

Conclusion

Incorporating age, basal valve diameter(mm), and annulus size into the risk - prediction model significantly improves the accuracy of predicting the risk of new - onset conduction block in patients after transcatheter aortic valve replacement. This strategy provides a more reliable basis for preoperative risk stratification and helps optimize clinical decision - making and patient management.