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Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement:The role of machine learning

查看全文 作　　者：Pradyumna [1]Agasthi;Hasan [1]Ashraf;Sai Harika [2]Pujari;Marlene [3]Girardo;Andrew [4]Tseng;Farouk [1]Mookadam;Nithin [1]Venepally;Matthew R [1,5]Buras;Bishoy [1]Abraham;Banveet K [1]Khetarpal;Mohamed [1]Allam;Siva K Mulpuru [4]MD;Mackram F [4]Eleid;Kevin L [1,6]Greason;Nirat [7]Beohar;John [1]Sweeney;David [1]Fortuin;David R Jr [4]Holmes;Reza [1]Arsanjani 高影响力作者 机构地区：[1]Department of Cardiology,Mayo Clinic,Phoenix,AZ 85054,United States;[2]Department of Internal Medicine,The Brooklyn Hospital Center,Brooklyn,NY 11201,United States;[3]Department of Biostatistics,Mayo Clinic,Phoenix,AZ 85054,United States;[4]Department of Cardiovascular Diseases,Mayo Clinic,Rochester,MN 55905,United States;[5]Department of Statistics,Mayo Clinic,Phoenix,AZ 85054,United States;[6]Department of Cardiovascular Surgery,Mayo Clinic,Rochester,MN 55905,United States;[7]Mount Sinai Medical Center,Columbia University,Miami Beach,FL 33138,United States高影响力机构 出　　处：《World Journal of Cardiology》索引2023年第15卷第3期,共11页高影响力期刊 基　　金：funded by Mayo Clinic Arizona Cardiovascular Clinical Research Center (MCA CV CRC) 摘　　要：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 preprocedural 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 P value<0.001.CONCLUSION The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR. 关 键 词：Transcatheter aortic valve replacement Permanent pacemaker implantation Machine learning
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