经导管主动脉瓣置换术后永久性起搏器植入的预测：机器学习的作用。
Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement: The role of machine learning.
作者信息
Agasthi Pradyumna, Ashraf Hasan, Pujari Sai Harika, Girardo Marlene, Tseng Andrew, Mookadam Farouk, Venepally Nithin, Buras Matthew R, Abraham Bishoy, Khetarpal Banveet K, Allam Mohamed, Md Siva K Mulpuru, Eleid Mackram F, Greason Kevin L, Beohar Nirat, Sweeney John, Fortuin David, Holmes David R Jr, Arsanjani Reza
机构信息
Department of Cardiology, Mayo Clinic, Phoenix, AZ 85054, United States.
Department of Internal Medicine, The Brooklyn Hospital Center, Brooklyn, NY 11201, United States.
出版信息
World J Cardiol. 2023 Mar 26;15(3):95-105. doi: 10.4330/wjc.v15.i3.95.
BACKGROUND
Atrioventricular block requiring permanent pacemaker (PPM) implantation is an important complication of transcatheter aortic valve replacement (TAVR). Application of machine learning could potentially be used to predict pre-procedural risk for PPM.
AIM
To apply machine learning to be used to predict pre-procedural risk for PPM.
METHODS
A retrospective study of 1200 patients who underwent TAVR (January 2014-December 2017) was performed. 964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis. After the exclusion of variables with near-zero variance or ≥ 50% missing data, 167 variables were included in the random forest gradient boosting algorithm (GBM) optimized using 5-fold cross-validations repeated 10 times. The receiver operator curve (ROC) for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.
RESULTS
Of 964 patients included in the 30-d analysis without prior PPM, 19.6% required PPM post-TAVR. The mean age of patients was 80.9 ± 8.7 years. 42.1 % were female. Of 657 patients included in the 1-year analysis, the mean age of the patients was 80.7 ± 8.2. Of those, 42.6% of patients were female and 26.7% required PPM at 1-year post-TAVR. The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model (0.66 and 0.72) was superior to that of the PPM risk score (0.55 and 0.54) with a value < 0.001.
CONCLUSION
The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.
背景
需要植入永久起搏器（PPM）的房室传导阻滞是经导管主动脉瓣置换术（TAVR）的一种重要并发症。机器学习的应用可能有助于预测术前发生PPM的风险。
目的
应用机器学习预测术前发生PPM的风险。
方法
对1200例行TAVR的患者（2014年1月至2017年12月）进行回顾性研究。964例既往未植入PPM的患者纳入30天分析，657例30天内无需PPM的患者纳入1年分析。在排除方差接近零或缺失数据≥50%的变量后，将167个变量纳入采用5折交叉验证重复10次优化的随机森林梯度提升算法（GBM）。计算GBM模型和PPM风险评分模型的受试者工作特征曲线（ROC），以预测30天和1年时发生PPM的风险。
结果
在纳入30天分析的964例既往未植入PPM的患者中，19.6%在TAVR术后需要植入PPM。患者的平均年龄为80.9±8.7岁。42.1%为女性。在纳入1年分析的657例患者中，患者的平均年龄为80.7±8.2岁。其中，42.6%为女性，26.7%在TAVR术后1年需要植入PPM。GBM模型预测30天和1年PPM风险的ROC曲线下面积（分别为0.66和0.72）优于PPM风险评分（分别为0.55和0.54），P值<0.001。
结论
GBM模型在识别TAVR术后发生PPM高风险患者方面具有良好的区分度和校准能力。
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